Tries-Based Parallel Solutions for Generating Perfect Crosswords Grids

A general crossword grid generation is considered an NP-complete problem and theoretically it could be a good candidate to be used by cryptography algorithms. In this article, we propose a new algorithm for generating perfect crosswords grids (with no black boxes) that relies on using tries data structures, which are very important for reducing the time for finding the solutions, and offers good opportunity for parallelisation, too. The algorithm uses a special tries representation and it is very efficient, but through parallelisation the performance is improved to a level that allows the solution to be obtained extremely fast. The experiments were conducted using a dictionary of almost 700,000 words, and the solutions were obtained using the parallelised version with an execution time in the order of minutes. We demonstrate here that finding a perfect crossword grid could be solved faster than has been estimated before, if we use tries as supporting data structures together with parallelisation. Still, if the size of the dictionary is increased by a lot (e.g., considering a set of dictionaries for different languages—not only for one), or through a generalisation to a 3D space or multidimensional spaces, then the problem still could be investigated for a possible usage in cryptography.

Public Law Restrictions in the Context of 3D Land Administration—Review on Legal and Technical Approaches

Intense exploitation of land implies the development of multi-level, multi-purpose, overlapping and interlocking structures on 3D space, thus resulting in complex, stratified, 3D real property rights between individual owners, as well as restrictions. Legislation regulates the ownership status and use of land by imposing restrictions known as Public Law Restrictions (PLRs). PLRs extend to various fields and various legislative frameworks, such as the protection of archaeological sites, protection and maintenance of underground infrastructures and utilities, environmental protection, flying of unmanned air vehicles, etc. PLRs are usually investigated in the context of property rights and restrictions in the various Land Administration Systems worldwide, and do not often gain specific attention. However, it is noticed that the restrictions that arise from Public Law need to be investigated and classified, so that they can be better utilised in the property status of land ownership. This review paper investigates the legal statutes on PLRs within the context of 3D land administration and the stipulations used to provide unambiguous modelling of PLRs, as provided by the relative literature. Moreover, the PLRs applied in the 3D space, to clearly depict rights, restrictions and responsibilities on the relevant spatial unit (land, air, marine parcel, mine, utility network, etc.), are particularly examined. Therefore, this work is to critically review and assess the aforementioned approaches on PLRs’ registration, modelling and organisation, as provided by a literature survey, and provides an overall view of the requirements and challenges within the development of 3D Land Administration Systems also considering standardisation developments.

Stress Conditions Modulate the Chromatin Interactions Network in Arabidopsis

Stresses have been known to cause various responses like cellular physiology, gene regulation, and genome remodeling in the organism to cope and survive. Here, we assessed the impact of stress conditions on the chromatin-interactome network of Arabidopsis thaliana. We identified thousands of chromatin interactions in native as well as in salicylic acid treatment and high temperature conditions in a genome-wide fashion. Our analysis revealed the definite pattern of chromatin interactions and stress conditions could modulate the dynamics of chromatin interactions. We found the heterochromatic region of the genome actively involved in the chromatin interactions. We further observed that the establishment or loss of interactions in response to stress does not result in the global change in the expression profile of interacting genes; however, interacting regions (genes) containing motifs for known TFs showed either lower expression or no difference than non-interacting genes. The present study also revealed that interactions preferred among the same epigenetic state (ES) suggest interactions clustered the same ES together in the 3D space of the nucleus. Our analysis showed that stress conditions affect the dynamics of chromatin interactions among the chromatin loci and these interaction networks govern the folding principle of chromatin by bringing together similar epigenetic marks.

Towards the analysis of coral skeletal density-banding using deep learning

X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI.

Methodological Challenges in Eye-Tracking based Usability Testing of 3-Dimensional Software – Presented via Experiences of Usability Tests of Four 3D Applications

Eye-tracking based usability testing and User Experience (UX) research are widespread in the development processes of various types of software; however, there exist specific difficulties during usability tests of three-dimensional (3D) software. Analysing the screen records with gaze plots, heatmaps of fixations, and statistics of Areas of Interests (AOI), methodological problems occur when the participant wants to rotate, zoom, or move the 3D space. The data gained regarded the menu bar is mainly interpretable; however, the data regarded the 3D environment is hardly so, or not at all. Our research tested four software applications with the aforementioned problem in mind: ViveLab and Jack Digital Human Modelling (DHM) and ArchiCAD and CATIA Computer Aided Design (CAD) software. Our original goal was twofold. Firstly, with these usability tests, we aimed to identify issues in the software. Secondly, we tested the utility of a new methodology which was included in the tests. This paper summarizes the results on the methodology based on individual experiments with different software applications. One of the main ideas behind the methodology adopted is to tell the participants (during certain subtasks of the tests) not to move the 3D space while they perform the given tasks at a certain point in the usability test. During the experiments, we applied a Tobii eye-tracking device, and after the task completion, each participant was interviewed. Based on these experiences, the methodology appears to be both useful and applicable, and its visualisation techniques for one or more participants are interpretable.

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention Based Graph Neural Networks

Performing first principle calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use pre-defined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed method, which combines both atomic composition and atomic coordinates in 3D-space, improves the accuracy in voltage prediction by 17% when compared to composition based ML models. The first model directly learns the chemical reaction of electrodes and metal-ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (Eform) to compute their average voltage. Our models demonstrates improved accuracy in transferability from our subset of learned metal-ions to other metal-ions.

A three-dimensional broadband underwater acoustic concentrator

We propose a three-dimensional (3D) omnidirectional underwater acoustic concentrator based on the concept of acoustic prison, which can realize a substantial enhancement of underwater sound signals in broadband ranges. This device mainly employs the non-resonant multiple reflection characteristics of the semi-enclosed geometric space, so it has a wide working frequency bandwidth. Compared with the previous reported concentrators based on transform acoustics mechanism, the structure is more simple, and most importantly, it can realize omnidirectional signal enhancement in 3D space. Moreover, the working frequency band of this acoustic concentrator depends on the size of the concentrator, so it can be changed directly through a size scaling, which is convenient for engineering applications. In general, the designed underwater acoustic concentrator has the advantages of simple structure, scalability and large bandwidth of working frequency, and high signal gain. It has potential application values in underwater target detection and other aspects.

Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment

A holistic understanding of tissue and organ structure and function requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) enables simultaneous detection of up to 40 antigens and transcripts using metal-tagged antibodies but has so far been restricted to two-dimensional imaging. Here we report the development of 3D IMC for multiplexed 3D tissue analysis at single-cell resolution and demonstrate the utility of the technology by analysis of human breast cancer samples. The resulting 3D models reveal cellular and microenvironmental heterogeneity and cell-level tissue organization not detectable in two dimensions. 3D IMC will prove powerful in the study of phenomena occurring in 3D space such as tumor cell invasion and is expected to provide invaluable insights into cellular microenvironments and tissue architecture.