pwc-archive/papers-with-abstracts huggingface dataset:
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
Authors: Thorir Mar Ingolfsson, Upasana Chakraborty, Xiaying Wang, Sandor Beniczky, Pauline Ducouret, Simone Benatti, Philippe Ryvlin, Andrea Cossettini, Luca Benini
Date: 2023-08-28 00:00:00
Tasks: EEG, Seizure Detection, Sensitivity, Specificity

Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessity for effective patient treatment. To enable long-term care in daily-life conditions, comfortable and smart wearable devices with long battery life are required, which in turn set the demand for resource-constrained and energy-efficient computing solutions. In this context, the development of machine learning algorithms for seizure detection faces the challenge of heavily imbalanced datasets. This paper introduces EpiDeNet, a new lightweight seizure detection network, and Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), a new loss function that incorporates sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The proposed EpiDeNet-SSWCE approach demonstrates the successful detection of 91.16% and 92.00% seizure events on two different datasets (CHB-MIT and PEDESITE, respectively), with only four EEG channels. A three-window majority voting-based smoothing scheme combined with the SSWCE loss achieves 3x reduction of false positives to 1.18 FP/h. EpiDeNet is well suited for implementation on low-power embedded platforms, and we evaluate its performance on two ARM Cortex-based platforms (M4F/M7) and two parallel ultra-low power (PULP) systems (GAP8, GAP9). The most efficient implementation (GAP9) achieves an energy efficiency of 40 GMAC/s/W, with an energy consumption per inference of only 0.051 mJ at high performance (726.46 MMAC/s), outperforming the best ARM Cortex-based solutions by approximately 160x in energy efficiency. The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.
Assessing cognitive function among older adults using machine learning and wearable device data: a feasibility study
Authors: Collin Sakal, Tingyou Li, Juan Li, Xinyue Li
Date: 2023-08-28 00:00:00
Tasks: Nutrition

Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring to detect changes in cognitive function. Data gathered using wearable devices that can continuously monitor factors known to be associated with cognition could be used to train machine learning models and develop wearable-based cognitive monitoring systems. Using data from over 2,400 older adults in the National Health and Nutrition Examination Survey (NHANES) we developed prediction models to differentiate older adults with normal cognition from those with poor cognition based on outcomes from three cognitive tests measuring different domains of cognitive function. During repeated cross-validation, CatBoost, XGBoost, and Random Forest models performed best when predicting cognition based on processing speed, working memory, and attention (median AUCs >0.82) compared to immediate and delayed recall (median AUCs >0.72) and categorical verbal fluency (median AUC >0.68). Activity and sleep parameters were also more strongly associated with processing speed, working memory, and attention compared to other cognitive subdomains. Our work provides proof of concept that wearable-based cognitive monitoring systems may be a viable alternative to traditional methods for monitoring processing speeds, working memory, and attention. We further identified novel metrics that could be targets in future causal studies seeking to better understand how sleep and activity parameters influence cognitive function among older adults.
Wideband High Gain Metasurface-Based 4T4R MIMO antenna with Highly Isolated Ports for Sub-6 GHz 5G Applications
Authors: Mahdi Salehi, Homayoon Oraizi
Date: 2023-08-26 00:00:00

This study presents the design of four $178\times178$ $(mm)^{2}$ wideband, high gain, highly efficient metasurface-based 4T4R MIMO (Multiple-Input Multiple-Output) antennas with highly isolated ports, covering the middle and a portion of the upper bands of the sub 6 GHz 5G frequency spectrum for 5G-based systems, such as IoT (Internet of Things) applications, vehicular communications (e.g., rooftop antennas of cars or trains), smart industries (e.g., farms and factories). The radiating elements of these antennas use the aperture-coupled feeding technique with a dumbbell-shaped slot, a truncated square patch with two U-shaped slots, and a metasurface layer. The proposed MIMO structures place four identical radiating elements like a $2\times2$ matrix with $90^\circ$ successive rotations to produce orthogonal electromagnetic waves, improving the isolation between ports. Six-millimeter spaces are added between these elements, and two vertical and horizontal strip slots are carved on the ground as the decoupling structure to decrease the mutual coupling. Simulation results show that Antenna\_{1}, Antenna\_{2}, and Antenna\_{3} achieve gain values of 6.2 to 9.4 dBi, 8.2 to 11.6 dBi, 6.2 to 9.5 dBi, below -35, -25, and -33 isolation and almost 10 dB diversity gain from 2.8 to 4.7 GHz, 2.8 to 4.5 GHz, and 2.7 to 4.9 GHz, respectively. As a prototype, Antenna\_{4} is manufactured, and measurements are performed. It achieves 6.28 to 10.45 dBi gain values, below -23 dB isolation, and 0.001 envelope correlation coefficient over 2.7 to 4.3 GHz. The results confirm that the proposed MIMO antennas are compatible with the 5G essential requisites.
Semi-orthogonal subspaces for value mediate a tradeoff between binding and generalization
Authors: W. Jeffrey Johnston, Justin M. Fine, Seng Bum Michael Yoo, R. Becket Ebitz, Benjamin Y. Hayden
Date: 2023-09-14 00:00:00

When choosing between options, we must associate their values with the action needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. To test this hypothesis, we examined neuronal responses in five reward-sensitive regions in macaques performing a risky choice task with sequential offers. Surprisingly, in all areas, the neural population encoded the values of offers presented on the left and right in distinct subspaces. We show that the encoding we observe is sufficient to bind the values of the offers to their respective positions in space while preserving abstract value information, which may be important for rapid learning and generalization to novel contexts. Moreover, after both offers have been presented, all areas encode the value of the first and second offers in orthogonal subspaces. In this case as well, the orthogonalization provides binding. Our binding-by-subspace hypothesis makes two novel predictions borne out by the data. First, behavioral errors should correlate with putative spatial (but not temporal) misbinding in the neural representation. Second, the specific representational geometry that we observe across animals also indicates that behavioral errors should increase when offers have low or high values, compared to when they have medium values, even when controlling for value difference. Together, these results support the idea that the brain makes use of semi-orthogonal subspaces to bind features together.
A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli
Authors: Giovanni M. Di Liberto, Aaron Nidiffer, Michael J. Crosse, Nathaniel J. Zuk, Stephanie Haro, Giorgia Cantisani, Martin M. Winchester, Aoife Igoe, Ross McCrann, Satwik Chandra, Edmund C. Lalor, Giacomo Baruzzo
Date: 2023-09-14 00:00:00
Tasks: EEG

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data or CND), providing precise naming conventions and data types, as well as a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface with existing EEG/MEG analysis toolboxes, such as Eelbrain, NAPLib, MNE, and mTRF-Toolbox. We present guidelines by taking both the user view (rapidly re-analyse existing data) and the experimenter view (store, analyse, and share), making the process straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis.
Diminished circadian and ultradian rhythms of human brain activity in pathological tissue in vivo
Authors: Christopher Thornton, Mariella Panagiotopoulou, Fahmida A Chowdhury, Beate Diehl, John S Duncan, Sarah J Gascoigne, Guillermo Besne, Andrew W McEvoy, Anna Miserocchi, Billy C Smith, Jane de Tisi, Peter N Taylor, Yujiang Wang
Date: 2023-09-13 00:00:00
Tasks: EEG, Rhythm

Chronobiological rhythms, such as the circadian rhythm, have long been linked to neurological disorders, but it is currently unknown how pathological processes affect the expression of biological rhythms in the brain. Here, we use the unique opportunity of long-term, continuous intracranially recorded EEG from 38 patients (totalling 6338 hours) to delineate circadian (daily) and ultradian (minute to hourly) rhythms in different brain regions. We show that functional circadian and ultradian rhythms are diminished in pathological tissue, independent of regional variations. We further demonstrate that these diminished rhythms are persistent in time, regardless of load or occurrence of pathological events. These findings provide evidence that brain pathology is functionally associated with persistently diminished chronobiological rhythms in vivo in humans, independent of regional variations or pathological events. Future work interacting with, and restoring, these modulatory chronobiological rhythms may allow for novel therapies.
Clinical dichotomania: A major cause of over-diagnosis and over-treatment?
Authors: Huw Llewelyn
Date: 2023-09-13 00:00:00
Tasks: Decision Making, Diagnostic

Introduction: There have been many warnings that inappropriate dichotomisation of results into positive or negative, high, or normal etc., during medical research could be very damaging. The aim of this paper is to argue that this is the main cause of over-diagnosis and over-treatment. Methods: Illustrative data were taken from a randomised control trial (RCT) that compared the frequency of nephropathy within 2 years in those on treatment with an angiotensin receptor blocker and a control and on patients in whom the numerical value of the albumin excretion rate (AER) was available on all patients before they are randomised. Results: When the RCT results were divided into AER ranges, a negligible proportion developed nephropathy within 2 years and benefited from treatment in the range 20 to 40mcg/min in which 36% of currently treated patients fall (and are thus over-diagnosed and overtreated). Above an AER of 40mcg/min, there was a gradual increase in proportions with nephropathy in each range, with fewer developing nephropathy in each range on irbesartan 150mg daily than on control and fewer still developing nephropathy on 300mg daily. Interpretation: When logistic regression functions were fitted to the data and calibrated, curves were created that allowed outcome probabilities and absolute risk reductions to be estimated for use in shared decision making (illustrated by application to an example patient). This could avoid much overdiagnosis and overtreatment. Conclusion: Careful attention to disease severity by interpreting each numerical diagnostic result provides better application of the principles of diagnosis and treatment decisions that can prevent over-diagnosis and over-treatment.
African swine fever in wild boar: investigating model assumptions and structure
Authors: Callum Shaw, Angus McLure, Kathryn Glass
Date: 2023-09-14 00:00:00

African swine fever (ASF) is a highly virulent viral disease that affects both domestic pigs and wild boar. Current ASF transmission in Europe is in part driven by wild boar populations, which act as a disease reservoir. Wild boar are abundant throughout Europe and are highly social animals with complex social organisation. Despite the known importance of wild boar in ASF spread and persistence, there remain knowledge gaps surrounding wild boar transmission. To investigate the influence of density-contact functions and wild boar social structure on disease dynamics, we developed a wild boar modelling framework. The framework included an ordinary differential equation model, a homogeneous stochastic model, and various network-based stochastic models that explicitly included wild boar social grouping. We found that power law functions (transmission $\propto$ density$^{0.5}$) and frequency-based density-contact functions were best able to reproduce recent Baltic outbreaks; however, power law function models predicted considerable carcass transmission, while frequency-based models had negligible carcass transmission. Furthermore, increased model heterogeneity caused a decrease in the relative importance of carcass-based transmission. The different dominant transmission pathways predicted by each model type affected the efficacy of potential interventions, which highlights the importance of evaluating model type and structure when modelling systems with uncertainties.
Revive, Restore, Revitalize: An Eco-economic Methodology for Maasai Mara
Authors: Yipeng Xu, He Sun, Junfeng Zhu
Date: 2023-09-11 00:00:00

The Maasai Mara in Kenya, renowned for its biodiversity, is witnessing ecosystem degradation and species endangerment due to intensified human activities. Addressing this, we introduce a dynamic system harmonizing ecological and human priorities. Our agent-based model replicates the Maasai Mara savanna ecosystem, incorporating 71 animal species, 10 human classifications, and 2 natural resource types. The model employs the metabolic rate-mass relationship for animal energy dynamics, logistic curves for animal growth, individual interactions for food web simulation, and human intervention impacts. Algorithms like fitness proportional selection and particle swarm mimic organism preferences for resources. To guide preservation activities, we formulated 21 management strategies encompassing tourism, transportation, taxation, environmental conservation, research, diplomacy, and poaching, employing a game-theoretic framework. Using the TOPSIS method, we prioritized four key developmental indicators: environmental health, research advancement, economic growth, and security. The interplay of 16 factors determines these indicators, each influenced by our policies to varying degrees. By evaluating the policies' repercussions, we aim to mitigate adverse animal-human interactions and equitably address human concerns. We classified the policy impacts into three categories: Environmental Preservation, Economic Prosperity, and Holistic Development. By applying these policy groupings to our ecosystem model, we tracked the effects on the intricate animal-human-resource dynamics. Utilizing the entropy weight method, we assessed the efficacy of these policy clusters over a decade, identifying the optimal blend emphasizing both environmental conservation and economic progression.
Transmission matrix parameter estimation of COVID-19 evolution with age compartments using ensemble-based data assimilation
Authors: Santiago Rosa, Manuel Pulido, Juan Ruiz, Tadeo Cocucci
Date: 2023-09-06 00:00:00
Tasks: parameter estimation

The COVID-19 pandemic and its multiple outbreaks have challenged governments around the world. Much of the epidemiological modeling was based on pre-pandemic contact information of the population, which changed drastically due to governmental health measures, so called non-pharmaceutical interventions made to reduce transmission of the virus, like social distancing and complete lockdown. In this work, we evaluate an ensemble-based data assimilation framework applied to a meta-population model to infer the transmission of the disease between different population agegroups. We perform a set of idealized twin-experiments to investigate the performance of different possible parameterizations of the transmission matrix. These experiments show that it is not possible to unambiguously estimate all the independent parameters of the transmission matrix. However, under certain parameterizations, the transmission matrix in an age-compartmental model can be estimated. These estimated parameters lead to an increase of forecast accuracy in agegroups compartments assimilating age-dependent accumulated cases and deaths observed in Argentina compared to a single-compartment model, and reliable estimations of the effective reproduction number. The age-dependent data assimilation and forecasting of virus transmission may be important for an accurate prediction and diagnosis of health care demand.
Long-Term Mean-Variance Optimization Under Mean-Reverting Equity Returns
Authors: Michael Preisel
Date: 2023-09-14 00:00:00

This paper studies the mean-variance optimal portfolio choice of an investor pre-committed to a deterministic investment policy in continuous time in a market with mean-reversion in the risk-free rate and the equity risk-premium. In the tradition of Markowitz, optimal policies are restricted to a subclass of factor exposures in which losses cannot exceed initial capital and it is shown that the optimal policy is characterized by an Euler-Lagrange equation derived by the method of Calculus of Variations. It is a main result, that the Euler-Lagrange equation can be recast into a matrix differential equation by an integral transformation of the factor exposure and that the solution to the characteristic equation can be parametrized by the eigenvalues of the associated lambda-matrix, hence, the optimization problem is equivalent to a spectral problem. Finally, explicit solutions to the optimal policy are provided by application of suitable boundary conditions and it is demonstrated that - if in fact the equity risk-premium is slowly mean-reverting - then investors committing to long investment horizons realize better risk-return trade-offs than investors with shorter investment horizons.
Profit and loss attribution: An empirical study
Authors: Solveig Flaig, Gero Junike
Date: 2023-09-14 00:00:00

The profit and loss (p&l) attrition for each business year into different risk or risk factors (e.g., interest rates, credit spreads, foreign exchange rate etc.) is a regulatory requirement, e.g., under Solvency 2. Three different decomposition principles are prevalent: one-at-a-time (OAT), sequential updating (SU) and average sequential updating (ASU) decompositions. In this research, using financial market data from 2003 to 2022, we demonstrate that the OAT decomposition can generate significant unexplained p&l and that the SU decompositions depends significantly on the order or labeling of the risk factors. On the basis of an investment in a foreign stock, we further explain that the SU decomposition is not able to identify all relevant risk factors. This potentially effects the hedging strategy of the portfolio manager. In conclusion, we suggest to use the ASU decomposition in practice.
MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection
Authors: Onkar Krishna; Hiroki Ohashi; Saptarshi Sinha
Date: 2023-11-20 00:00:00
Tasks: Domain Adaptation, object-detection, Object Detection

Cross-domain object detection is challenging, and it involves aligning labeled source and unlabeled target domains. Previous approaches have used adversarial training to align features at both image-level and instance-level. At the instance level, finding a
suitable source sample that aligns with a target sample is crucial. A source sample is considered suitable if it differs from the target sample only in domain, without differences in unimportant characteristics such as orientation and color, which can hinder the
model’s focus on aligning the domain difference. However, existing instance-level feature alignment methods struggle to find suitable source instances because their search scope is limited to mini-batches. Mini-batches are often so small in size that they do not always contain suitable source instances. The insufficient diversity of mini-batches becomes problematic particularly when the target instances have high intra-class variance. To address this issue, we propose a memory-based instance-level domain adaptation framework. Our method aligns a target instance with the most similar source instance of the same category retrieved from a memory storage. Specifically, we introduce a memory module that dynamically stores the pooled features of all labeled source instances, categorized by their labels. Additionally, we introduce a simple yet effective memory retrieval module that retrieves a set of matching memory slots for target instances. Our experiments on various domain shift scenarios demonstrate that our approach outperforms existing non-memory-based methods significantly.
De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments
Authors: Kevin Eloff, Konstantinos Kalogeropoulos, Oliver Morell, Amandla Mabona, Jakob Berg Jespersen, Wesley Williams, Sam van Beljouw, Marcin Skwark, Andreas Hougaard Laustsen, Stan J. J. Brouns, Anne Ljungars, Erwin Marten Schoof, Jeroen Van Goey, Ulrich auf dem Keller, Karim Beguir, Nicolas Lopez Carranza, Timothy Patrick Jenkins
Date: 2023-08-31 00:00:00
Proceeding: bioRxiv 2023 8
Tasks: de novo peptide sequencing

Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum. Traditional methods that rely on known peptide sequence databases are limited and may not be applicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to the spectra without prior information, is valuable for various biological applications; yet, due to a lack of accuracy, it remains challenging to apply this approach in many situations. Here, we introduce InstaNovo, a transformer neural network with the ability to translate fragment ion peaks into the sequence of amino acids that make up the studied peptide(s). The model was trained on 28 million labelled spectra matched to 742k human peptides from the ProteomeTools project. We demonstrate that InstaNovo outperforms current state-of-the-art methods on benchmark datasets and showcase its utility in several applications. Building upon human intuition, we also introduce InstaNovo+, a multinomial diffusion model that further improves performance by iterative refinement of predicted sequences. Using these models, we could de novo sequence antibodybased therapeutics with unprecedented coverage, discover novel peptides, and detect unreported organisms in different datasets, thereby expanding the scope and detection rate of proteomics searches. Finally, we could experimentally validate tryptic and non-tryptic peptides with targeted proteomics, demonstrating the fidelity of our predictions. Our models unlock a plethora of opportunities across different scientific domains, such as direct protein sequencing, immunopeptidomics, and exploration of the dark proteome.