## Quadratic and Higher Order Unconstrained Binary Optimization of Railway Dispatching Problem for Quantum Computing

This paper outlines QUBO and HOBO representations for dispatching problems of rail traffic management . The consequences of disruptions in railway traffic are the primary cause of passengers’ dissatisfaction . The main result is the hybrid algorithm to deal with disturbancesin rail traffic on single-, double- and multi-track lines; the demonstrativemodel illustrates the issue briefly .…

## Reshaping Convex Polyhedra

Given a convex polyhedral surface P, we define a tailoring as excising from Pa simple polygonal domain that contains one vertex v . We show that P can be reshaped to anypolyhedral convex surface Q a subset of conv(P) by a sequence of tailorings .…

## DGO A new DIRECT type MATLAB toolbox for derivative free global optimization

DGO collects various deterministic derivative-freeDIRECT-type algorithms for box-constrained problems . DGO is equipped with agraphical user interface (GUI), which ensures the user-friendly use of allfunctionalities available in DGO . Available features are demonstrated indetailed computational studies using a created comprehensive library of globaloptimization problems .…

## DGO A new DIRECT type MATLAB toolbox for derivative free global optimization

DGO collects various deterministic derivative-freeDIRECT-type algorithms for box-constrained problems . DGO is equipped with agraphical user interface (GUI), which ensures the user-friendly use of allfunctionalities available in DGO . Available features are demonstrated indetailed computational studies using a created comprehensive library of globaloptimization problems .…

## Geometric averages of partitioned datasets

We introduce a method for jointly registering ensembles of partitioneddatasets in a way which is both geometrically coherent and partition-aware . We establish basic theory in this general setting, including Alexandrov curvature bounds and a verifiable characterization of local means in symmetric products of metric spaces .…

## A new metaheuristic approach for the art gallery problem

The goal of the art gallery problem is to find the minimum number of guards within a simplepolygon to observe and protect the entire of it . It has many applications inRobotics, Telecommunication and so on and there are some approaches to handle the problem which is theoretically NP-hard .…

## Minimum Constraint Removal Problem for Line Segments is NP hard

In the minimum constraint removal ($MCR$), there is no feasible path to move from the starting point to the goal and, the minimum constraints should be removed in order to find a collision-free path . Problem is $NP-hard$ when constraints have arbitrary shapes or even they are inshape of convex polygons .…

## IRS Aided WPCNs A New Optimization Framework for Dynamic IRS Beamforming

In this paper, we propose a new dynamic IRS beamforming framework to boost the sum throughput of an intelligent reflecting surface (IRS) aided wirelesspowered communication network (WPCN) Specifically, the IRS phase-shift vectorsacross time and resource allocation are jointly optimized to enhance theefficiencies of both downlink wireless power transfer (DL WPT) and uplinkwireless information transmission (UL WIT) between a hybrid access point (HAP) and multiple wirelessly powered devices .…

## Self Adversarial Training incorporating Forgery Attention for Image Forgery Localization

Image editing techniques enable people to modify content of an imagewithout leaving visual traces and thus may cause serious security risks . Hencethe detection and localization of these forgeries become quite necessary and challenging . In this paper, we propose a self-adversarial training strategy and a reliable coarse-to-fine network that utilizes a selfattention mechanism to localize forged regions in forgery images .…

## A convolutional neural network for teeth margin detection on 3 dimensional dental meshes

We proposed a convolutional neural network for vertex classification on 3-dimensional dental meshes . An expandinglayer was constructed to collect statistic values of neighbor features . The accuracy, recall and precision werevalidated on 145 dental meshes to rate the best network structures .…

## On Search Complexity of Discrete Logarithm

In this work, we study the discrete logarithm problem in the context of TFNP- the complexity class of search problems with a syntactically guaranteedexistence of a solution for all instances . Our main results establish thatsuitable variants of the discrete .…

## On the Hardness of Compressing Weights

We investigate computational problems involving large weights through the lens of kernelization . Our main focus is the weighted Cliqueproblem, where we are given an edge-weighted graph and the goal is to detect aclique of total weight equal to a prescribed value .…

## A Leap among Entanglement and Neural Networks A Quantum Survey

In recent years, Quantum Computing witnessed massive improvements in resources and algorithms development . The ability toharness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community’s interest since the late ’80s . In such a context, we pose our contribution to quantum computing .…

## OptiMic A tool to generate optimized polycrystalline microstructures for materials simulations

Polycrystal microstructures, with their distinct physical, chemical,structural and topological entities, play an important role in determining theeffective properties of materials . The software allows for both monodispersive grains as well as irregulargrains obtained currently via Voronoi tessellations. These initialmicrostructures can then be optimized to reflect desired statistical features.…

## Long Short Transformer Efficient Transformers for Language and Vision

Long-Short Transformer (Transformer-LS) is anefficient self-attention mechanism for modeling long sequences with linearcomplexity . It aggregates a novel long-rangeattention with dynamic projection to model distant correlations and ashort-term attention to capture fine-grained local correlations . The method outperforms the state-of-the-art models on multiple tasks in language and vision domains, including the Long Range Arena benchmark, autoregressive language modeling, andImageNet classification .…

## Space Efficient Two Dimensional Orthogonal Colored Range Counting

In the two-dimensional orthogonal colored range counting problem, we process a set of $P$ of $n$ colored points on the plane . We design three new solutions, and the bounds of each can be expressed in some form of time-space tradeoff .…

## Thread modular Analysis of Release Acquire Concurrency

We present a thread-modular abstract interpretation(TMAI) technique to verifyprograms under the release-acquire (RA) memory model for safety propertyviolations . We capture the executionorder of program statements as an abstract domain, and propose a sound upperapproximation over this domain to efficiently reason over RA concurrency .…

## Special Purpose Computers for Statistical Physics achievements and lessons

In the late 80s and 90s, theoretical physicists designed and developed several specialized computers for challenging computational problems in the physics of phase transitions . Computers performed calculations three orders of magnitude faster than similar calculations on the world’s best supercomputers .…

## Exact Analytical Parallel Vectors

This paper demonstrates that parallel vector curves are piecewise cubicrational curves in 3D piecewise linear vector fields . We discuss how singularities of the rationals lead to different types of intersections with tetrahedral cells . We define the term \emph{generalized and underdetermined eigensystem} inthe form of the form of $\mathbf{A}\mathbf {x}+\mathf {a}=\lambda(\mathf)$ inorder to derive the piecewise rational representation of 3D parallel vector curve .…

## CAP RAM A Charge Domain In Memory Computing 6T SRAM for Accurate and Precision Programmable CNN Inference

CAP-RAM is presented forenergy-efficient convolutional neural network (CNN) inference . It leverages anovel charge-domain multiply-and-accumulate (MAC) mechanism and circuitry to achieve superior linearity under process variations compared to conventionalIMC designs . A single 512×128 macro stores a complete pruned and quantized CNN model to achieve 98.8% inference accuracy on the MNIST data set and 89.0% on the CIFAR-10 data set, with a573.4-giga operations per second (GOPS) peak throughput .…

## Scheme theoretic Approach to Computational Complexity II The Separation of P and NP over mathbb C mathbb R and mathbb Z

The problem of determining the feasibility of quadratic systemsover $C$ and $R$ requires exponential time . This separates P and NP over these fields/rings in the BCSS model ofcomputation .…

## Accessible Color Cycles for Data Visualization

Data were collected with an online survey, and the results were used to train a machine-learning model . Optimal color cycles containing six, eight, and tencolors were generated using the data-driven aesthetic-preference model and accessibility constraints . Due to the balance of aesthetics and accessibility considerations, the resulting color cycles can serve as reasonable defaults indata-plotting codes for data visualization .…

## ATC an Advanced Tucker Compression library for multidimensional data

ATC is a C++ library for advanced Tucker-based compression of numerical data . It is based on the sequentially truncated higher-order singular value decomposition (ST-HOSVD) and bit plane truncation . Numerical results show that ATC maintains state-of-the-art Tucker compression rates, while providing average speed-ups of 2.6-3.6 and halving memory usage .…

## TransformerFusion Monocular RGB Scene Reconstruction using Transformers

TransformerFusion is a transformer-based 3D scene reconstruction approach . From an input monocular RGB video, the video frames are processed by a transformer network that fuses the observations into a volumetric featuregrid representing the scene . This feature grid is then decoded to a higher-resolutionscene reconstruction, using an MLP-based surface occupancy prediction frominterpolated coarse-to-fine 3D features .…

## State efficient QFA Algorithm for Quantum Computers

Moore-Crutchfield quantum finite automaton(MCQFA) is proven to be exponentially more succinct than classical finiteautomata models . In this paper, we present a modified MCQFA algorithm for the language $mathtt{MOD}_{\rm p}$, the operators of which are selected based on the basis gates on the availablereal quantum computers .…

## Demonstration of Faceted Search on Scholarly Knowledge Graphs

Traditional scholarly search systems listdocuments instead of providing direct answers to the search queries . As data in knowledge graphs are not acquainted semantically, they are not machine-readable . Instead, a search on scholarly knowledge graphs ends up in a full-text search, not a search in the content of scholarly literature .…

## ParDen Surrogate Assisted Hyper Parameter Optimisation for Portfolio Selection

Portfolio optimisation is a multi-objective optimisation problem (MOP) where an investor aims to maximise the conflicting criteria of maximising a portfolio’s expected return whilst minimising its risk and other costs . Selecting a portfolio is a computationally expensive problem because of the cost associated with performing multiple evaluations on test data .…

## Nested Sequents for Intuitionistic Modal Logics via Structural Refinement

We employ a recently developed methodology — called “structural refinement” — to extract nested sequent systems for a sizable class of intuitionisticmodal logics . We show that our nested systems are sound, cut-free complete, and admithp-admissibility of typical structural rules .…

## Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries

Traditional scholarly digital libraries list documents in search results, and therefore are unable to provide precise answers to search queries . We present a methodology for improving a faceted search system on structured content by leveraging afederation of scholarly knowledge graphs .…

## A Data Driven Method for Recognizing Automated Negotiation Strategies

Understanding an opponent agent helps in negotiating with it . Wepropose a novel data-driven approach for recognizing an opponent’s snegotiation strategy . The approach includes a data generation method for anagent to generate domain-independent sequences by negotiating with a variety of opponents across domains, a feature engineering method for representingnegotiation data as time series with time-step features and overall features, and a hybrid (recurrent neural network-based) deep learning method .…

## Recovering the Unbiased Scene Graphs from the Biased Ones

Scene graph generation (SGG) aims to producecomprehensive, graphical representations describing visual relationships among objects . However, the imbalance in the fraction of missing labels of differentclasses, or reporting bias, exacerbating the long tail is rarely considered and cannot be solved by the existing debiasing methods .…

## Analyzing a Knowledge Graph of Industry4 0 Standards

In this article, we tackle the problem of standard interoperability across different standardization frameworks, and devise a knowledge-driven approach . The STO ontology representsproperties of standards and standardization . The I40KG integrates more than 200 standards and fourstandardization frameworks . We analyze both thenumber of discovered relations between standards and accuracy of theserelations .…

## A Lottery Ticket Hypothesis Framework for Low Complexity Device Robust Neural Acoustic Scene Classification

We propose a novel neural model compression strategy combining dataaugmentation, knowledge transfer, pruning, and quantization for device-robustacoustic scene classification . Acoustic Lottery could compress an ASC model over$1/10^{4}$ and attain a superior performance (validation accuracy of 74.01% and log loss of 0.76) compared to its not compressed seed model .…

## An Analytical Survey on Recent Trends in High Dimensional Data Visualization

Data visualization is the process by which data of any size or dimensionality is processed to produce an understandable set of data in a lowerdimensionality . The goal of our paper is to survey the performance of currenthigh-dimensional data visualization techniques and quantify their strengths andweaknesses through relevant quantitative measures .…