pwc-archive/papers-with-abstracts huggingface dataset:
Topology of protein metastructure and $β$-sheet topology
Authors: Jørgen Ellegaard Andersen, Hiroyuki Fuji, Yuki Koyanagi
Date: 2021-11-29 00:00:00

We introduce a new, simplified model of proteins, which we call protein metastructure. The metastructure of a protein carries information about its secondary structure and $\beta$-strand conformations. Furthermore, protein metastructure allows us to associate an object called a fatgraph to a protein, and a fatgraph in turn gives rise to a topological surface. It becomes thus possible to study the topological invariants associated to a protein. We discuss the correspondence between protein metastructures and fatgraphs, and how one can compute topological invariants, such as genus and the number of boundary components, from fatgraphs. We then describe an algorithm for generating likely candidate metastructures using the information obtained from topology of protein fatgraphs. This algorithm is further developed to predict $\beta$-sheet topology of proteins, with a possibility to combine it with an existing algorithm. We demonstrate the algorithm on the data from PDB, and improve the performance of and existing algorithm by combining with it.
Using Topology to Estimate Structural Similarities of Proteins
Authors: Jørgen Ellegaard Andersen, Jens Ledet Jensen, Yuki Koyanagi, Jakob Toudahl Nielsen, Rasmus Villemoes
Date: 2021-11-29 00:00:00

An effective model for protein structures is important for the study of protein geometry, which, to a large extent, determine the functions of proteins. There are a number of approaches for modelling; one might focus on the conformation of the backbone or H-bonds, and the model may be based on the geometry or the topology of the structure in focus. We focus on the topology of H-bonds in proteins, and explore the link between the topology and the geometry of protein structures. More specifically, we take inspiration from CASP Evaluation of Model Accuracy and investigate the extent to which structural similarities, via GDT_TS, can be estimated from the topology of H-bonds. We report on two experiments; one where we attempt to mimic the computation of GDT_TS based solely on the topology of H-bonds, and the other where we perform linear regression where the independent variables are various scores computed from the topology of H-bonds. We achieved an average $\Delta\text{GDT}$ of 6.45 with 54.5% of predictions inside 2 $\Delta\mathrm{GDT}$ for the first method, and an average $\Delta\mathrm{GDT}$ of 4.41 with 72.7% of predictions inside 2 $\Delta\mathrm{GDT}$ for the second method.
Marangoni effect and cell spreading
Authors: Ivana Pajic-Lijakovic, Milan Milivojevic
Date: 2021-11-29 00:00:00

Cells are very sensitive to the shear stress (SS). However, undesirable SS is generated during physiological process such as collective cell migration (CCM) and influences the biological processes such as morphogenesis, wound healing and cancer invasion. Despite extensive research devoted to study the stress generation caused by CCM, we still do not fully understand the main cause of SS generation. An attempt is made here to offer some answers to these questions by considering the rearrangement of cell monolayers. The SS generation represents a consequence of natural and forced convection. While forced convection is dependent on cell speed, the natural convection is induced by the gradient of tissue surface tension. The phenomenon is known as the Marangoni effect. The gradient of tissue surface tension induces directed cell spreading from the regions of lower tissue surface tension to the regions of higher tissue surface tension. This directed cell migration is described by the Marangoni flux. The phenomenon has been recognized during the rearrangement of (1) epithelial cell monolayers and (2) mixed cell monolayers made by epithelial and mesenchymal cells. The consequence of the Marangoni effect is an intensive spreading of cancer cells through an epithelium. In this work, a review of existing literature about SS generation caused by CCM is given along with the assortment of published experimental findings, in order to invite experimentalists to test given theoretical considerations in multicellular systems.
Prediction of H-Bond Rotations from Protein H-Bond Topology
Authors: Jørgen Ellegaard Andersen, Yuki Koyanagi, Jakob Toudahl Nielsen, Rasmus Villemoes
Date: 2021-11-29 00:00:00

H-bonds are known to play an important role in the folding of proteins into three-dimensional structures, which in turn determine their diverse functions. The conformations around H-bonds are important, in that they can be non-local along the backbone and are therefore not captured by the methods such as Ramachandran plots. We study the relationship between the geometry of H-bonds in proteins, expressed as a spatial rotation between the two bonded peptide units, and their topology, expressed as a subgraph of the protein fatgraph. We describe two experiments to predict H-bond rotations from their corresponding subgraphs. The first method is based on sequence alignment between sequences of the signed lengths of H-bonds measured along the backbone. The second method is based on finding an exact match between the descriptions of subgraphs around H-bonds. We find that 88.14% of the predictions lie inside the ball, centred around the true rotation, occupying just 1% of the volume of the rotation space SO(3).
NanoFrame: A web-based DNA wireframe design tool for 3D structures
Authors: Samson Petrosyan, Grigory Tikhomirov
Date: 2021-11-27 00:00:00
Tasks: Management

The rapid development of the DNA nanotechnology field has been facilitated by advances in CAD software. However, as more complex concepts arose, the lag between the needs and software capabilities appeared. Further derailed by manual installation and software incompatibility across different platforms and often tedious library management issues, the software has become hard-to-use for many. Here we present NanoFrame, a web-based DNA wireframe design tool for making 3D nanostructures from a single scaffold. Within this software, we devised algorithms for DNA routing, staple breaking, and wireframe cage opening which, while modeled for cuboid structure, can be generalized to a variety of platonic and Archimedean shapes. In addition, NanoFrame provides a platform for editing auto-generated staple sequences and saving work online.
Fast Sampling from Time-Integrated Bridges using Deep Learning
Authors: Leonardo Perotti, Lech A. Grzelak
Date: 2021-11-27 00:00:00
Tasks: Deep Learning

We propose a methodology to sample from time-integrated stochastic bridges, namely random variables defined as $\int_{t_1}^{t_2} f(Y(t))dt$ conditioned on $Y(t_1)\!=\!a$ and $Y(t_2)\!=\!b$, with $a,b\in R$. The Stochastic Collocation Monte Carlo sampler and the Seven-League scheme are applied for this purpose. Notably, the distribution of the time-integrated bridge is approximated utilizing a polynomial chaos expansion built on a suitable set of stochastic collocation points. Furthermore, artificial neural networks are employed to learn the collocation points. The result is a robust, data-driven procedure for the Monte Carlo sampling from conditional time-integrated processes, which guarantees high accuracy and generates thousands of samples in milliseconds. Applications, with a focus on finance, are presented here as well.
Superhedging duality for multi-action options under model uncertainty with information delay
Authors: Anna Aksamit, Ivan Guo, Shidan Liu, Zhou Zhou
Date: 2021-11-29 00:00:00

We consider the superhedging price of an exotic option under nondominated model uncertainty in discrete time in which the option buyer chooses some action from an (uncountable) action space at each time step. By introducing an enlarged space we reformulate the superhedging problem for such an exotic option as a problem for a European option, which enables us to prove the pricing-hedging duality. Next, we present a duality result that, when the option buyers action is observed by the seller up to $l$ periods later, the superhedging price equals the model-based price where the option buyer has the power to look into the future for $l$ periods.
Optimal Load Scheduling Using Genetic Algorithm to Improve the Load Profile
Authors: Farhat Iqbal, Shafiq ur Rehman, Khawar Iqbal
Date: 2021-10-14 00:00:00
Tasks: Scheduling

Stability and protection of the electrical power systems are always of primary concern. Stability can be affected mostly by increase in the load demand. Power grids are overloaded in peak hours so more power generation units are required to cope the demand. Increase in power generation is not an optimal solution. With the enlargement in Smart grid (SG), it becomes easier to correlate the consumer demand and available power. The most significant featutre of smart grid is demand response (DR) which is used to match the demand of available electrical energy and shift the peak load into off peak hours to improve the economics of energy and stability of grid stations. Presently we used Genetic algorithm (GA) to schedule the load via real time pricing signal (RTP). Load is categorized depending on their energy requirement, operational constraint and duty cycle. We conclude that GA provides optimal solution for scheduling of house hold appliances by curtailing overall utilized energy cost and peak to average ratio hence improving the load profile.
Solving classification problems using Traceless Genetic Programming
Authors: Mihai Oltean
Date: 2021-10-07 00:00:00
Tasks: Classification

Traceless Genetic Programming (TGP) is a new Genetic Programming (GP) that may be used for solving difficult real-world problems. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. In this paper, TGP is used for solving real-world classification problems taken from PROBEN1. Numerical experiments show that TGP performs similar and sometimes even better than other GP techniques for the considered test problems.
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications
Authors: Adrien Bennetot, Ivan Donadello, Ayoub El Qadi, Mauro Dragoni, Thomas Frossard, Benedikt Wagner, Anna Saranti, Silvia Tulli, Maria Trocan, Raja Chatila, Andreas Holzinger, Artur d'Avila Garcez, Natalia Díaz-Rodríguez
Date: 2021-11-13 00:00:00
Tasks: BIG-bench Machine Learning, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Fairness

Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed explanations of their behaviour. As opaque machine learning models are increasingly being employed to make important predictions in critical environments, the danger is to create and use decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing machine learning models with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is meant to be the go-to handbook for any audience with a computer science background aiming at getting intuitive insights on machine learning models, accompanied with straight, fast, and intuitive explanations out of the box. This article aims to fill the lack of compelling XAI guide by applying XAI techniques in their particular day-to-day models, datasets and use-cases. Figure 1 acts as a flowchart/map for the reader and should help him to find the ideal method to use according to his type of data. In each chapter, the reader will find a description of the proposed method as well as an example of use on a Biomedical application and a Python notebook. It can be easily modified in order to be applied to specific applications.
Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture
Authors: Seongsu Bae, Daeyoung Kim, Jiho Kim, Edward Choi
Date: 2021-11-14 00:00:00
Tasks: Decoder, Natural Questions, Question Answering

An intelligent machine that can answer human questions based on electronic health records (EHR-QA) has a great practical value, such as supporting clinical decisions, managing hospital administration, and medical chatbots. Previous table-based QA studies focusing on translating natural questions into table queries (NLQ2SQL), however, suffer from the unique nature of EHR data due to complex and specialized medical terminology, hence increased decoding difficulty. In this paper, we design UniQA, a unified encoder-decoder architecture for EHR-QA where natural language questions are converted to queries such as SQL or SPARQL. We also propose input masking (IM), a simple and effective method to cope with complex medical terms and various typos and better learn the SQL/SPARQL syntax. Combining the unified architecture with an effective auxiliary training objective, UniQA demonstrated a significant performance improvement against the previous state-of-the-art model for MIMICSQL* (14.2% gain), the most complex NLQ2SQL dataset in the EHR domain, and its typo-ridden versions (approximately 28.8% gain). In addition, we confirmed consistent results for the graph-based EHR-QA dataset, MIMICSPARQL*.
Biosignals learning and synthesis using deep neural networks
Authors: David Belo, João Rodrigues, João R. Vaz, Pedro Pezarat-Correia, Hugo Gamboa
Date: 2017-09-25 00:00:00
Proceeding: BioMedical Engineering OnLine 2017 9

Background
Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field.

Method
The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself.

Results and conclusions
The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models’ prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.
Manifold Optimization Methods for Hybrid beamforming in mmWave Dual-Function Radar-Communication System
Authors: Bowen Wang, Ziyang Cheng, Zishu He
Date: 2021-11-30 00:00:00
Tasks: Riemannian optimization

As a cost-effective alternative, hybrid analog and digital beamforming architecture is a promising scheme for millimeter wave (mmWave) system. This paper considers two hybrid beamforming architectures, i.e. the partially-connected and fully-connected structures, for mmWave dual-function radar communication (DFRC) system, where the transmitter communicates with the downlink users and detects radar targets simultaneously. The optimization problems are formulated by minimizing a weighted summation of radar and communication performance, subject to constant modulus and power constraints. To tackle the non-convexities caused by the two resultant problems, effective Riemannian optimization algorithms are proposed. Specifically, for the fully-connected structure, a manifold algorithm based on the alternating direction method of multipliers (ADMM) is developed. While for the partially-connected structure, a low-complexity Riemannian product manifold trust region (RPM-TR) algorithm is proposed to approach the near-optional solution. Numerical simulations are provided to demonstrate the effectiveness of the proposed methods.
Scalable Machine Learning Architecture for Neonatal Seizure Detection on Ultra-Edge Devices
Authors: Vishal Nagarajan, Ashwini Muralidharan, Deekshitha Sriraman, Pravin Kumar S
Date: 2021-11-29 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), Seizure Detection

Neonatal seizures are a commonly encountered neurological condition. They are the first clinical signs of a serious neurological disorder. Thus, rapid recognition and treatment are necessary to prevent serious fatalities. The use of electroencephalography (EEG) in the field of neurology allows precise diagnosis of several medical conditions. However, interpreting EEG signals needs the attention of highly specialized staff since the infant brain is developmentally immature during the neonatal period. Detecting seizures on time could potentially prevent the negative effects on the neurocognitive development of the infants. In recent years, neonatal seizure detection using machine learning algorithms have been gaining traction. Since there is a need for the classification of bio-signals to be computationally inexpensive in the case of seizure detection, this research presents a machine learning (ML) based architecture that operates with comparable predictive performance as previous models but with minimum level configuration. The proposed classifier was trained and tested on a public dataset of NICU seizures recorded at the Helsinki University Hospital. Our architecture achieved a best sensitivity of 87%, which is 6% more than that of the standard ML model chosen in this study. The model size of the ML classifier is optimized to just 4.84 KB with minimum prediction time of 182.61 milliseconds, thus enabling it to be deployed on wearable ultra-edge devices for quick and accurate response and obviating the need for cloud-based and other such exhaustive computational methods.
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning
Authors: Hanbyel Cho, Yooshin Cho, Jaemyung Yu, Junmo Kim
Date: 2021-11-30 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Human Pose Estimation, Camera Calibration, Domain Adaptation, Meta-Learning, Pose Estimation, Video Recognition

Existing 3D human pose estimation algorithms trained on distortion-free datasets suffer performance drop when applied to new scenarios with a specific camera distortion. In this paper, we propose a simple yet effective model for 3D human pose estimation in video that can quickly adapt to any distortion environment by utilizing MAML, a representative optimization-based meta-learning algorithm. We consider a sequence of 2D keypoints in a particular distortion as a single task of MAML. However, due to the absence of a large-scale dataset in a distorted environment, we propose an efficient method to generate synthetic distorted data from undistorted 2D keypoints. For the evaluation, we assume two practical testing situations depending on whether a motion capture sensor is available or not. In particular, we propose Inference Stage Optimization using bone-length symmetry and consistency. Extensive evaluation shows that our proposed method successfully adapts to various degrees of distortion in the testing phase and outperforms the existing state-of-the-art approaches. The proposed method is useful in practice because it does not require camera calibration and additional computations in a testing set-up.
Interval-valued q-Rung Orthopair Fuzzy Choquet Integral Operators and Its Application in Group Decision Making
Authors: Benting Wan, Juelin Huang, Xi Chen
Date: 2021-11-30 00:00:00
Tasks: Decision Making, Management

It is more flexible for decision makers to evaluate by interval-valued q-rung orthopair fuzzy set (IVq-ROFS),which offers fuzzy decision-making more applicational space. Meanwhile, Choquet integralses non-additive set function (fuzzy measure) to describe the interaction between attributes directly.In particular, there are a large number of practical issues that have relevance between attributes.Therefore,this paper proposes the correlation operator and group decision-making method based on the interval-valued q-rung orthopair fuzzy set Choquet integral.First,interval-valued q-rung orthopair fuzzy Choquet integral average operator (IVq-ROFCA) and interval-valued q-rung orthopair fuzzy Choquet integral geometric operator (IVq-ROFCG) are inves-tigated,and their basic properties are proved.Furthermore, several operators based on IVq-ROFCA and IVq-ROFCG are developed. Then, a group decision-making method based on IVq-ROFCA is developed,which can solve the decision making problems with interaction between attributes.Finally,through the implementation of the warning management system for hypertension,it is shown that the operator and group decision-making method proposed in this paper can handle complex decision-making cases in reality, and the decision result is consistent with the doctor's diagnosis result.Moreover,the comparison with the results of other operators shows that the proposed operators and group decision-making method are correct and effective,and the decision result will not be affected by the change of q value.
Easy Semantification of Bioassays
Authors: Marco Anteghini, Jennifer D'Souza, Vitor A. P. Martins dos Santos, Sören Auer
Date: 2021-11-30 00:00:00
Tasks: Clustering, Data Integration, Knowledge Graphs, Retrieval

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.
The Effect of Iterativity on Adversarial Opinion Forming
Authors: Konstantinos Panagiotou, Simon Reisser
Date: 2021-11-30 00:00:00

Consider the following model to study adversarial effects on opinion forming. A set of initially selected experts form their binary opinion while being influenced by an adversary, who may convince some of them of the falsehood. All other participants in the network then take the opinion of the majority of their neighbouring experts. Can the adversary influence the experts in such a way that the majority of the network believes the falsehood? Alon et al. [1] conjectured that in this context an iterative dissemination process will always be beneficial to the adversary. This work provides a counterexample to that conjecture. [1] N. Alon, M. Feldman, O. Lev, and M. Tennenholtz. How Robust Is the Wisdom of the Crowds? In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), pages 2055-2061, 2015.