Clique Cover of Graphs with Bounded Degeneracy

Structural parameters of graph (such as degeneracy and arboricity) had rarely been considered . Taking degeneracy of graph into account, we present a greedyframework and two fixed-parameter tractable algorithms . We introduce a set theoretic concept and demonstrate its use in the computations of different objectives .…

On a Tverberg graph

For a graph whose vertices are closed, consider the closed (open) balls with diameters induced by its edges . The graphis called a (an open) Tverberg graph) if these closed balls intersect . For any finite set of points in the plane, there exists a Hamiltonian cycle that is a Tverberberg graph .…

SERF Towards better training of deep neural networks using log Softplus ERror activation Function

Serf is self-regularized and nonmonotonic in nature and belongs to the Swish family of functions . Serf outperforms ReLU and other activation functions including both Swish and Mish, with a markedly bigger margin ondeeper architectures . Ablation studies further demonstrate that Serf basedarchitectitectures perform better than those of Swish or Mish in varying scenarios,validating the effectiveness and compatibility of Serf with varying depth,complexity, optimizers, learning rates, batch sizes, initializers and dropoutrates .…

Public sentiment analysis and topic modeling regarding COVID 19 vaccines on the Reddit social media platform A call to action for strengthening vaccine confidence

The COVID-19 pandemic fueled one of the most rapid vaccine developments in history . However, misinformation spread through online social media often leadsto negative vaccine sentiment and hesitancy . To investigate vaccine-related discussion in social media, we conducted a sentiment analysisand Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the vaccine from Dec 1, 2020, to May 15, 2021 .…

Proceedings Combined 28th International Workshop on Expressiveness in Concurrency and 18th Workshop on Structural Operational Semantics

This volume contains the proceedings of EXPRESS/SOS 2021: the Combined 28thInternational Workshop on Expressiveness in Concurrency and the 18th Workshop on Structural Operational Semantics . The EXPRESS series aims at bringing together researchers interested in the formal semantics of systems and programming concepts, and inthe expressiveness of computational models .…

Description logics as polyadic modal logics

We study extensions of standard description logics to the framework ofpolyadic modal logic . We promote a natural approach to such logics via generalrelation algebras that can be used to define operations on relations of allarities . We investigate thepolyadic version of ALC extended with relational permutation operators andtuple counting .…

Proceedings Combined 28th International Workshop on Expressiveness in Concurrency and 18th Workshop on Structural Operational Semantics

This volume contains the proceedings of EXPRESS/SOS 2021: the Combined 28thInternational Workshop on Expressiveness in Concurrency and the 18th Workshop on Structural Operational Semantics . The EXPRESS series aims at bringing together researchers interested in the formal semantics of systems and programming concepts, and inthe expressiveness of computational models .…

Signed Bipartite Graph Neural Networks

Signed networks are social networks having both positive and negative links . Signed bipartitenetworks can be commonly found in many fields including business, politics, andacademics . Previous work mainly focuses on theunipartite signed networks where the nodes have the same type .…

Evolutionary Ensemble Learning for Multivariate Time Series Prediction

Multivariate time series (MTS) prediction plays a key role in many field fields such as finance, energy and transport . We propose a novelevolutionary ensemble learning framework to optimize the entire pipeline in aholistic manner . In this framework, a specific pipeline is encoded as acandidate solution and a multi-objective evolutionary algorithm is applied under different population sizes to produce multiple Pareto optimal sets .…

On a Tverberg graph

For a graph whose vertices are closed, consider the closed (open) balls with diameters induced by its edges . The graphis called a (an open) Tverberg graph) if these closed balls intersect . For any finite set of points in the plane, there exists a Hamiltonian cycle that is a Tverberberg graph .…

Decomposition Multi Objective Evolutionary Optimization From State of the Art to Future Opportunities

Decomposition has been the mainstream approach in the classic mathematicalprogramming for multi-objective optimization and multi-criteriondecision-making . We present a comprehensive survey of the development of MOEA/D fromits origin to the current state-of-the-art approaches . We also overviews some furtherdevelopments for constraint handling, computationally expensive objective functions, preference incorporation, and real-world applications .…

Developments in Mathematical Algorithms and Computational Tools for Proton CT and Particle Therapy Treatment Planning

Proton computed tomography (pCT) andintensity-modulated particle therapy (IMPT) treatment planning . Proton therapynecessitates a high level of delivery accuracy to exploit the selectivetargeting imparted by the Bragg peak . The pCT technique allows reconstruction of the volumetric distribution of the relative stopping power (RSP) of the patient tissue for use in treatment planning and pre-treatment range verification .…

Developments in Mathematical Algorithms and Computational Tools for Proton CT and Particle Therapy Treatment Planning

Proton computed tomography (pCT) andintensity-modulated particle therapy (IMPT) treatment planning . Proton therapynecessitates a high level of delivery accuracy to exploit the selectivetargeting imparted by the Bragg peak . The pCT technique allows reconstruction of the volumetric distribution of the relative stopping power (RSP) of the patient tissue for use in treatment planning and pre-treatment range verification .…

Temporal Induced Self Play for Stochastic Bayesian Games

Temporal-Induced Self-Play (TISP) is a novel reinforcement learning-basedframework to find strategies with decent performances from any decision point onward . TISP uses belief-space representation, backward induction, policylearning, and non-parametric approximation . The results show that TisP-PG is more scalable than existing mathematicalprogramming-based methods and significantly outperforms other learning–basedmethods .…

Palmira A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts

Handwritten documents are often characterized by dense and uneven layout . This phenomenon is especially pronounced for the low-resource Indicpalm-leaf manuscript domain . We propose a novel deep network Palmira for robust, deformation-aware segmentation of regions in handwritten manuscripts . Our experiments demonstrate that Palmira provides robust layouts, outperformsstrong baseline approaches and ablative variants .…

A computational study on imputation methods for missing environmental data

The process of collecting data, however, may experienceirregularities, resulting in databases with missing data . Missing entries mightalter analysis efficiency and, consequently, the associated decision-makingprocess . This paper focuses on databases collecting information related to thenatural environment . We believe that the present study demonstrates the pertinence of using MF as imputation method whendealing with missing environmental data, we believe that .…

BoundaryNet An Attentive Deep Network with Fast Marching Distance Maps for Semi automatic Layout Annotation

BoundaryNet is a novel resizing-free approach for high-precisionsemi-automatic layout annotation . The variable-sized user selected region of interest is first processed by an attention-guided skip network . The networkoptimization is guided via Fast Marching distance maps to obtain a good quality initial boundary estimate and an associated feature representation.…

Eliciting Social Knowledge for Creditworthiness Assessment

Access to capital is a major constraint for economic growth in the developing world . Those attempting to lend in this space face high defaults due to inability to distinguish creditworthy borrowers from the rest . In thispaper, we propose two novel scoring mechanisms that incentivize communitymembers to truthfully report their signal on the creditworthiness of others intheir community .…

Developments in Mathematical Algorithms and Computational Tools for Proton CT and Particle Therapy Treatment Planning

Proton computed tomography (pCT) andintensity-modulated particle therapy (IMPT) treatment planning . Proton therapynecessitates a high level of delivery accuracy to exploit the selectivetargeting imparted by the Bragg peak . The pCT technique allows reconstruction of the volumetric distribution of the relative stopping power (RSP) of the patient tissue for use in treatment planning and pre-treatment range verification .…

Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics

Machine learning approaches have recently been leveraged as a substitute or an aid for physical/mathematicical modeling approaches to dynamical systems . We propose a reservoir computing model by using a recurrent network of heterogeneous leakyintegrator neurons . The proposed model has a higher potential than the existing standard model, and yields a performance comparable to the best one of the standard model even without an optimization of the leak rate parameter .…

On the Parallel I O Optimality of Linear Algebra Kernels Near Optimal Matrix Factorizations

Matrix factorizations are among the most important building blocks of computing . State-of-the-art libraries, however, are notcommunication-optimal, underutilizing current parallel architectures . Wepresent novel algorithms for Cholesky and LU factorizations that utilize anasymptotically communication-optimistic 2.5D decomposition . Our code is ScaLAPACK-compatible and available as an open-source library.…

SiReN Sign Aware Recommendation Using Graph Neural Networks

In recent years, many recommender systems using network embedding (NE) suchas graph neural networks (GNNs) have been extensively studied in the sense ofimproving recommendation accuracy . SiReN has three key components: constructing a signed bipartite graph for moreprecisely representing users’ preferences, splitting into twoedge-disjoint graphs with positive and negative edges each, generating twoembeddings for the partitioned graphs .…

Does Preprocessing help in Fast Sequence Comparisons

We study edit distance computation with preprocessing: the preprocessingalgorithm acts on each string separately, and then the query algorithm takes as input the two preprocessed strings . This model is inspired by scenarios wherewe would like to compute edit distance between many pairs in the same pool ofstrings .…