pwc-archive/papers-with-abstracts huggingface dataset:
Detection for Flat-Fading Channels based on Expectation Propagation
Authors: Elisa Conti, Amina Piemontese, Giulio Colavolpe, Armando Vannucci
Date: 2023-05-30 00:00:00
Tasks: Scheduling

This paper aims at tackling the problem of signal detection in flat-fading channels. In this context, receivers based on the expectation propagation framework appear to be very promising although presenting some critical issues. We develop a new algorithm based on this framework where, unlike previous works, convergence is achieved after a single forward-backward pass, without additional inner detector iterations. The proposed message scheduling, together with novel adjustments of the approximating distributions' parameters, allows to obtain significant performance advantages with respect to the state-of-the-art solution. Simulation results show the applicability of this algorithm when sparser pilot configurations have to be adopted and a considerable gain compared to the current available strategies.
New Sequence Alignment Algorithm using AI Rules and Dynamic Seeds
Authors: Suchindra, Preetam Nagaraj
Date: 2023-05-25 00:00:00

DNA sequence alignment is important today as it is usually the first step in finding gene mutation, evolutionary similarities, protein structure, drug development and cancer treatment. Covid-19 is one recent example. There are many sequencing algorithms developed over the past decades but the sequence alignment using expert systems is quite new. To find DNA sequence alignment, dynamic programming was used initially. Later faster algorithms used small DNA sequence length of fixed size to find regions of similarity, and then build the final alignment using these regions. Such systems were not sensitive but were fast. To improve the sensitivity, we propose a new algorithm which is based on finding maximal matches between two sequences, find seeds between them, employ rules to find more seeds of varying length, and then employ a new stitching algorithm, and weighted seeds to solve the problem
Adaptive coding efficiency in recurrent cortical circuits via gain control
Authors: Lyndon R. Duong, Colin Bredenberg, David J. Heeger, Eero P. Simoncelli
Date: 2023-05-31 00:00:00

Sensory systems across all modalities and species exhibit adaptation to continuously changing input statistics. Individual neurons have been shown to modulate their response gains so as to maximize information transmission in different stimulus contexts. Experimental measurements have revealed additional, nuanced sensory adaptation effects including changes in response maxima and minima, tuning curve repulsion from the adapter stimulus, and stimulus-driven response decorrelation. Existing explanations of these phenomena rely on changes in inter-neuronal synaptic efficacy, which, while more flexible, are unlikely to operate as rapidly or reversibly as single neuron gain modulations. Using published V1 population adaptation data, we show that propagation of single neuron gain changes in a recurrent network is sufficient to capture the entire set of observed adaptation effects. We propose a novel adaptive efficient coding objective with which single neuron gains are modulated, maximizing the fidelity of the stimulus representation while minimizing overall activity in the network. From this objective, we analytically derive a set of gains that optimize the trade-off between preserving information about the stimulus and conserving metabolic resources. Our model generalizes well-established concepts of single neuron adaptive gain control to recurrent populations, and parsimoniously explains experimental adaptation data.
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
Authors: Polina Turishcheva, Paul G. Fahey, Laura Hansel, Rachel Froebe, Kayla Ponder, Michaela Vystrčilová, Konstantin F. Willeke, Mohammad Bashiri, Eric Wang, Zhiwei Ding, Andreas S. Tolias, Fabian H. Sinz, Alexander S. Ecker
Date: 2023-05-31 00:00:00

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022, we introduced benchmarks for vision models with static input. However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input. It includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input. We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
Reliability of energy landscape analysis of resting-state functional MRI data
Authors: Pitambar Khanra, Johan Nakuci, Sarah Muldoon, Takamitsu Watanabe, Naoki Masuda
Date: 2023-05-31 00:00:00
Tasks: Time Series

Energy landscape analysis is a data-driven method to analyze multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e., within-participant reliability) than across different sets of sessions from different participants (i.e., between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.
Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome
Authors: Bryant Avila, Pedro Augusto, Manuel Zimmer, Matteo Serafino, Hernán A. Makse
Date: 2023-05-30 00:00:00

Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
Sequence Evolution, Structure and Dynamics of Transmembrane Proteins: Rhodopsin
Authors: J. C. Phillips
Date: 2023-05-28 00:00:00

Rhodopsin is a G-protein coupled receptor found in retinal rod cells, where it mediates monocrhromatic vision in dim light. It is one of the most studied proteins with thousands of reviewed entries in Uniprot. It has seven transmembrane segments, here examined for their hydrophobic character, and how that has evolved from chickens to humans. Elastic features associated with Proline are also discussed. Finally, differences between rhodopsin and cone opsins are also discussed.
A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics
Authors: Dongyan Zhang, Wuyue Yang, Wanqi Wen, Liangrong Peng, Changjingn Zhuge, Liu Hong
Date: 2023-05-31 00:00:00
Tasks: regression

We make a retrospective review on various control measures taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 7, 2020, and evaluate their impacts on the epidemic dynamics quantitatively. The SEIR-QD model, as a representative for general compartment models, is used to fit the epidemic data, enabling the extraction of crucial model parameters and dynamical features. The mediation effect of the SEIR-QD model is revealed by using the mediation analysis with structure equation modeling for multiple mediators operating in parallel. The inherent impacts of these control policies on the transmission dynamics of COVID-19 epidemics are clarified, and compared with results derived from both multiple linear regression and neural-network-based nonlinear regression. Through this data-driven analysis, the mediation effect of compartment models is confirmed, which provides a better understanding on the intrinsic correlations among the strength of control measures and the dynamical features of COVID-19 epidemics.
A Comparison of Mutation and Amplification-Driven Resistance Mechanisms and Their Impacts on Tumor Recurrence
Authors: Aaron Li, Danika Kibby, Jasmine Foo
Date: 2023-05-30 00:00:00

Tumor recurrence, driven by the evolution of drug resistance is a major barrier to therapeutic success in cancer. Resistance is often caused by genetic alterations such as point mutation, which refers to the modification of a single genomic base pair, or gene amplification, which refers to the duplication of a region of DNA that contains a gene. Here we investigate the dependence of tumor recurrence dynamics on these mechanisms of resistance, using stochastic multi-type branching process models. We derive tumor extinction probabilities and deterministic estimates for the tumor recurrence time, defined as the time when an initially drug sensitive tumor surpasses its original size after developing resistance. For models of amplification-driven and mutation-driven resistance, we prove law of large numbers results regarding the convergence of the stochastic recurrence times to their mean. Additionally, we prove sufficient and necessary conditions for a tumor to escape extinction under the gene amplification model, discuss behavior under biologically relevant parameters, and compare the recurrence time and tumor composition in the mutation and amplification models both analytically and using simulations. In comparing these mechanisms, we find that the ratio between recurrence times driven by amplification vs. mutation depends linearly on the number of amplification events required to acquire the same degree of resistance as a mutation event, and we find that the relative frequency of amplification and mutation events plays a key role in determining the mechanism under which recurrence is more rapid. In the amplification-driven resistance model, we also observe that increasing drug concentration leads to a stronger initial reduction in tumor burden, but that the eventual recurrent tumor population is less heterogeneous, more aggressive, and harbors higher levels of drug-resistance.
Systematic Review of Methods and Prognostic Value of Mitotic Activity. Part 2: Canine Tumors
Authors: Christof A. Bertram, Taryn A. Donovan, Alexander Bartel
Date: 2023-05-31 00:00:00
Tasks: Articles

One of the most relevant prognostication tests for tumors is cellular proliferation, which is most commonly measured by the mitotic activity in routine tumor sections. The goal of this systematic review is to scholarly analyze the methods and prognostic relevance of histologically measuring mitotic activity in canine tumors. A total of 137 articles that correlated the mitotic activity in canine tumors with patient outcome were identified through a systematic (PubMed and Scopus) and manual (Google Scholar) literature search and eligibility screening process. These studies determined the mitotic count (MC, number of mitotic figures per tumor area) in 126 instances, presumably the mitotic count (method not specified) in 6 instances and the mitotic index (MI, proportion of mitotic figures per tumor cells) in 5 instances. A particularly high risk of bias was identified in the available details of the MC methods and statistical analysis, which often did not quantify the prognostic discriminative ability of the MC and only reported p-values. A significant association of the MC with survival was found in 72/109 (66%) studies. However, survival was evaluated by at least three studies in only 7 tumor types/groups, of which a prognostic relevance is apparent for mast cell tumors of the skin, cutaneous melanoma and soft tissue sarcoma of the skin. None of the studies on the MI found a prognostic relevance. Further studies on the MC and MI with standardized methods are needed to prove the prognostic benefit of this test for further tumor types.
Parameter Estimation Methods of Required Rate of Return
Authors: Battulga Gankhuu
Date: 2023-05-31 00:00:00
Tasks: parameter estimation

In this study, we introduce new estimation methods for the required rate of returns on equity and liabilities of private and public companies using the stochastic dividend discount model (DDM). To estimate the required rate of return on equity, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. We also provide a method that evaluates the market values of liabilities. We apply the model to a set of firms from the S\&P 500 index using historical dividend and price data over a 32--year period. Overall, the suggested methods can be used to estimate the required rate of returns.
Modeling and evaluating conditional quantile dynamics in VaR forecasts
Authors: Fabrizio Cipollini, Giampiero M. Gallo, Alessandro Palandri
Date: 2023-05-31 00:00:00

We focus on the time-varying modeling of VaR at a given coverage $\tau$, assessing whether the quantiles of the distribution of the returns standardized by their conditional means and standard deviations exhibit predictable dynamics. Models are evaluated via simulation, determining the merits of the asymmetric Mean Absolute Deviation as a loss function to rank forecast performances. The empirical application on the Fama-French 25 value-weighted portfolios with a moving forecast window shows substantial improvements in forecasting conditional quantiles by keeping the predicted quantile unchanged unless the empirical frequency of violations falls outside a data-driven interval around $\tau$.
Large language models improve Alzheimer's disease diagnosis using multi-modality data
Authors: Yingjie Feng, Jun Wang, Xianfeng GU, Xiaoyin Xu, Min Zhang
Date: 2023-05-26 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

In diagnosing challenging conditions such as Alzheimer's disease (AD), imaging is an important reference. Non-imaging patient data such as patient information, genetic data, medication information, cognitive and memory tests also play a very important role in diagnosis. Effect. However, limited by the ability of artificial intelligence models to mine such information, most of the existing models only use multi-modal image data, and cannot make full use of non-image data. We use a currently very popular pre-trained large language model (LLM) to enhance the model's ability to utilize non-image data, and achieved SOTA results on the ADNI dataset.
Global Layers: Non-IID Tabular Federated Learning
Authors: Yazan Obeidi
Date: 2023-05-29 00:00:00
Tasks: Federated Learning

Data heterogeneity between clients remains a key challenge in Federated Learning (FL), particularly in the case of tabular data. This work presents Global Layers (GL), a novel partial model personalization method robust in the presence of joint distribution $P(X,Y)$ shift and mixed input/output spaces $X \times Y$ across clients. To the best of our knowledge, GL is the first method capable of supporting both client-exclusive features and classes. We introduce two new benchmark experiments for tabular FL naturally partitioned from existing real world datasets: i) UCI Covertype split into 4 clients by "wilderness area" feature, and ii) UCI Heart Disease, SAHeart, UCI Heart Failure, each as clients. Empirical results in these experiments in the full-participant setting show that GL achieves better outcomes than Federated Averaging (FedAvg) and local-only training, with some clients even performing better than their centralized baseline.
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction
Authors: Ta Jiun Ting, Xiaocan Li, Scott Sanner, Baher Abdulhai
Date: 2023-05-30 00:00:00
Tasks: Prediction, regression, Traffic Prediction

Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have become the prevailing models in the traffic prediction literature since they excel at extracting spatial correlations. In this work, we classify the components of successful GCNN prediction models and analyze the effects of matrix factorization, attention mechanism, and weight sharing on their performance. Furthermore, we compare these variations against random forests, a traditional regression method that predates GCNNs by over 15 years. We evaluated these methods using simulated data of two regions in Toronto as well as real-world sensor data from selected California highways. We found that incorporating matrix factorization, attention, and location-specific model weights either individually or collectively into GCNNs can result in a better overall performance. Moreover, although random forest regression is a less compact model, it matches or exceeds the performance of all variations of GCNNs in our experiments. This suggests that the current graph convolutional methods may not be the best approach to traffic prediction and there is still room for improvement. Finally, our findings also suggest that for future research on GCNN for traffic prediction to be credible, researchers must include performance comparison to random forests.
Memory as a Mass-based Graph: Towards a Conceptual Framework for the Simulation Model of Human Memory in AI
Authors: Mahdi Mollakazemiha, Hassan Fatzade
Date: 2023-05-19 00:00:00

There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a model of brain activity that contains a quite good congruence with observational facts such as mistakes and forgotten experiences. Given that human memory has a solid core that includes the components of our identity, our family and our hometown, the major and determinative events of our lives, and the countless repeated and accepted facts of our culture, the more we go to the peripheral spots the data becomes flimsier and more easily exposed to oblivion. It was essential to propose a model in which the topographical differences are quite distinguishable. In our proposed model, we have translated this topographical situation into quantities, which are attributed to the nodes. The result is an edge-weighted graph with mass-based values on the nodes which demonstrates the importance of each atomic proposition, as a truth, for an intelligent being. Furthermore, it dynamically develops and modifies, and in successive phases, it changes the mass of the nodes and weight of the edges depending on gathered inputs from the environment.
Enhancing Programming eTextbooks with ChatGPT Generated Counterfactual-Thinking-Inspired Questions
Authors: Arun Balajiee Lekshmi Narayanan, Rully Agus Hendrawan, Venktesh V
Date: 2023-06-01 00:00:00
Tasks: counterfactual

Digital textbooks have become an integral part of everyday learning tasks. In this work, we consider the use of digital textbooks for programming classes. Generally, students struggle with utilizing textbooks on programming to the maximum, with a possible reason being that the example programs provided as illustration of concepts in these textbooks don't offer sufficient interactivity for students, and thereby not sufficiently motivating to explore or understand these programming examples better. In our work, we explore the idea of enhancing the navigability of intelligent textbooks with the use of ``counterfactual'' questions, to make students think critically about these programs and enhance possible program comprehension. Inspired from previous works on nudging students on counter factual thinking, we present the possibility to enhance digital textbooks with questions generated using GPT.
Training-free Neural Architecture Search for RNNs and Transformers
Authors: Aaron Serianni, Jugal Kalita
Date: 2023-06-01 00:00:00
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, Neural Architecture Search

Neural architecture search (NAS) has allowed for the automatic creation of new and effective neural network architectures, offering an alternative to the laborious process of manually designing complex architectures. However, traditional NAS algorithms are slow and require immense amounts of computing power. Recent research has investigated training-free NAS metrics for image classification architectures, drastically speeding up search algorithms. In this paper, we investigate training-free NAS metrics for recurrent neural network (RNN) and BERT-based transformer architectures, targeted towards language modeling tasks. First, we develop a new training-free metric, named hidden covariance, that predicts the trained performance of an RNN architecture and significantly outperforms existing training-free metrics. We experimentally evaluate the effectiveness of the hidden covariance metric on the NAS-Bench-NLP benchmark. Second, we find that the current search space paradigm for transformer architectures is not optimized for training-free neural architecture search. Instead, a simple qualitative analysis can effectively shrink the search space to the best performing architectures. This conclusion is based on our investigation of existing training-free metrics and new metrics developed from recent transformer pruning literature, evaluated on our own benchmark of trained BERT architectures. Ultimately, our analysis shows that the architecture search space and the training-free metric must be developed together in order to achieve effective results.