Arbitrary Size Research Articles

Precise Photochemical Post‐Processing of Molecular Crystals

Molecular crystals carry a great potential as new soft smart materials, with a plethora of recent examples overcoming the major obstacle of mechanical flexibility, and this research direction holds enormous potential to revolutionize optics, electronics, medicine, and space exploration. However, shaping organic crystals into desired shapes and sizes remains a major practical challenge due to the lack of control over the crystallization process, and the difficulties in mechanical post‐processing without introduction of defects that are usually imparted by their soft nature. Here we present an innovative approach that employs photochemical processing for precise and nondestructive cutting of a molecular crystal. Our proposed method uses light to post‐process crystals of the desired size and shape, similar to using light to cut other materials. This reaction induces strain, ensuring sharp cleavage without the need for melting or other processes. We further demonstrate the potential of this approach by producing crystals of arbitrary size, which can be used as controllable optical waveguides. Among other potential applications, this method can be used to prepare dynamic crystals, particularly those with aspect ratios crucial for mechanical deformation, such as flexible electronics, soft robotics, and sensing.

Precise Photochemical Post-Processing of Molecular Crystals.

Molecular crystals carry a great potential as new soft smart materials, with a plethora of recent examples overcoming the major obstacle of mechanical flexibility, and this research direction holds enormous potential to revolutionize optics, electronics, medicine, and space exploration. However, shaping organic crystals into desired shapes and sizes remains a major practical challenge due to the lack of control over the crystallization process, and the difficulties in mechanical post-processing without introduction of defects that are usually imparted by their soft nature. Here we present an innovative approach that employs photochemical processing for precise and nondestructive cutting of a molecular crystal. Our proposed method uses light to post-process crystals of the desired size and shape, similar to using light to cut other materials. This reaction induces strain, ensuring sharp cleavage without the need for melting or other processes. We further demonstrate the potential of this approach by producing crystals of arbitrary size, which can be used as controllable optical waveguides. Among other potential applications, this method can be used to prepare dynamic crystals, particularly those with aspect ratios crucial for mechanical deformation, such as flexible electronics, soft robotics, and sensing.

Analysis and Application of Particle Backtracking Algorithm in Wind–Sand Two-Phase Flow Using SPH Method

Due to the high sensitivity of grid-based micro-scale wind–sand flow models to deformation and distortion, this study employs the Smooth Particle Hydrodynamics (SPH) method for numerical simulations. The advantage of the SPH method is that it can dynamically analyze the entire trajectory of the particles, thus allowing the initial positional distribution of sand-buried particles to be traced. This study utilizes the advantages of the SPH method. It develops particle backtracking algorithms based on the SPH method using the C language. It analyses the initial location distribution, concentration, velocity, and particle size distribution of sand-buried particles to formulate targeted measures to cope with wind–sand disasters. Meanwhile, this paper improves a particle modeling algorithm to realize arbitrary mixing particle size and mixing ratio by programming in C language and combining it with pixel recognition technology. In addition, this paper will use the particle backtracking algorithm to analyze the classical embankment wind and sand flow field and then propose adequate measures for embankment wind and sand disaster management by investigating sand particle movement characteristics.

Ionisation Chemistry in the Inner Disc: A Combined Treatment of Ionic and Thermionic Emission and Arbitrary Grain Size Distributions

Ionisation Chemistry in the Inner Disc: A Combined Treatment of Ionic and Thermionic Emission and Arbitrary Grain Size Distributions

Structured multi-view k-means clustering

K-means is a very efficient clustering method and many multi-view k-means clustering methods have been proposed for multi-view clustering during the past decade. However, since k-means have trouble uncovering clusters of varying sizes and densities, these methods suffer from the same performance issues as k-means. Improving the clustering performance of multi-view k-means has become a challenging problem. In this paper, we propose a new multi-view k-means clustering method that is able to uncover clusters in arbitrary sizes and densities. The new method simultaneously performs three tasks, i.e., sparse connection probability matrices learning, prototypes aligning, and cluster structure learning. We evaluate the proposed new method by 5 benchmark datasets and compare it with 11 multi-view clustering methods. The experimental results on both synthetic and real-world experiments show the superiority of our proposed method.

Inverting Cryptographic Hash Functions via Cube-and-Conquer

MD4 and MD5 are fundamental cryptographic hash functions proposed in the early 1990s. MD4 consists of 48 steps and produces a 128-bit hash given a message of arbitrary finite size. MD5 is a more secure 64-step extension of MD4. Both MD4 and MD5 are vulnerable to practical collision attacks, yet it is still not realistic to invert them, i.e., to find a message given a hash. In 2007, the 39-step version of MD4 was inverted by reducing to SAT and applying a CDCL solver along with the so-called Dobbertin’s constraints. As for MD5, in 2012 its 28-step version was inverted via a CDCL solver for one specified hash without adding any extra constraints. In this study, Cube-and-Conquer (a combination of CDCL and lookahead) is applied to invert step-reduced versions of MD4 and MD5. For this purpose, two algorithms are proposed. The first one generates inverse problems for MD4 by gradually modifying the Dobbertin’s constraints. The second algorithm tries the cubing phase of Cube-and-Conquer with different cutoff thresholds to find the one with the minimum runtime estimate of the conquer phase. This algorithm operates in two modes: (i) estimating the hardness of a given propositional Boolean formula; (ii) incomplete SAT solving of a given satisfiable propositional Boolean formula. While the first algorithm is focused on inverting step-reduced MD4, the second one is not area-specific and is therefore applicable to a variety of classes of hard SAT instances. In this study, 40-, 41-, 42-, and 43-step MD4 are inverted for the first time via the first algorithm and the estimating mode of the second algorithm. Also, 28-step MD5 is inverted for four hashes via the incomplete SAT solving mode of the second algorithm. For three hashes out of them, it is done for the first time.

Data-driven fingerprint nanoelectromechanical mass spectrometry

Fingerprint analysis is a ubiquitous tool for pattern recognition with applications spanning from geolocation and DNA analysis to facial recognition and forensic identification. Central to its utility is the ability to provide accurate identification without an a priori mathematical model for the pattern. We report a data-driven fingerprint approach for nanoelectromechanical systems mass spectrometry that enables mass measurements of particles and molecules using complex, uncharacterized nanoelectromechanical devices of arbitrary specification. Nanoelectromechanical systems mass spectrometry is based on the frequency shifts of the nanoelectromechanical device vibrational modes that are induced by analyte adsorption. The sequence of frequency shifts constitutes a fingerprint of this adsorption, which is directly amenable to pattern matching. Two current requirements of nanoelectromechanical-based mass spectrometry are: (1) a priori knowledge or measurement of the device mode-shapes, and (2) a mode-shape-based model that connects the induced modal frequency shifts to mass adsorption. This may not be possible for advanced nanoelectromechanical devices with three-dimensional mode-shapes and nanometer-sized features. The advance reported here eliminates this impediment, thereby allowing device designs of arbitrary specification and size to be employed. This enables the use of advanced nanoelectromechanical devices with complex vibrational modes, which offer unprecedented prospects for attaining the ultimate detection limits of nanoelectromechanical mass spectrometry.

Point cloud upsampling network based on pyramid pooling and self-attention mechanism

For the problems of irregular point cloud data and non-unique input size, a point-cloud upsampling network (PPSA-PU) based on Pyramid Pooling and Self-Attention mechanism is proposed. Multi-scale features are first extracted from the input point cloud using dense map convolution with different expansion rates, and the pyramid pooling module and self-attention mechanism are added. By applying the same pooling operation to regions of different sizes and numbers, the network is made to accept inputs of arbitrary size. Self-attention is used to weight the features to establish global correlation and improve the feature extraction capability. The features are then extended by the Self-Attention NodeShuffle (SA-NS) module, which uses a graph convolution layer to fuse neighbour information and learn new point features from the latent space. Finally, the coordinate reconstruction module is used to generate a dense up-sampled point cloud. Using the existing dataset to train and test the network and compare it with the existing up-sampling network, the experimental results show that PPSA-PU is better in terms of evaluation metrics and performance, and has better generalisation and robustness.

A mechanics-based data-free Problem Independent Machine Learning (PIML) model for large-scale structural analysis and design optimization

Machine learning (ML) enhanced fast structural analysis and design recently attracted considerable attention. In most related works, however, the generalization ability of the ML model and the massive cost of dataset generation are the two most criticized aspects. This work combines the advantages of the universality of the substructure method and the superior predictive ability of the operator learning architecture. Specifically, using a novel mechanics-based loss function, lightweight neural network mapping from the material distribution inside a substructure and the corresponding continuous multiscale shape function is well-trained without preparing a dataset. In this manner, a problem machine learning model (PIML) that is generally applicable for efficient linear elastic analysis and design optimization of large-scale structures with arbitrary size and various boundary conditions is proposed. Several examples validate the effectiveness of the present work on efficiency improvement and different kinds of optimization problems. This PIML model-based design and optimization framework can be extended to large-scale multiphysics problems.

Generalizing Random Butterfly Transforms to Arbitrary Matrix Sizes

Parker and Lê introduced random butterfly transforms (RBTs) as a preprocessing technique to replace pivoting in dense LU factorization. Unfortunately, their FFT-like recursive structure restricts the dimensions of the matrix. Furthermore, on multi-node systems, efficient management of the communication overheads restricts the matrix’s distribution even more. To remove these limitations, we have generalized the RBT to arbitrary matrix sizes by truncating the dimensions of each layer in the transform. We expanded Parker’s theoretical analysis to generalized RBT, specifically that in exact arithmetic, Gaussian elimination with no pivoting will succeed with probability 1 after transforming a matrix with full-depth RBTs. Furthermore, we experimentally show that these generalized transforms improve performance over Parker’s formulation by up to 62 % while retaining the ability to replace pivoting. This generalized RBT is available in the SLATE numerical software library.

Device-Performance-Driven Heterogeneous Multiparty Learning for Arbitrary Images.

Multiparty learning (MPL) is an emerging framework for privacy-preserving collaborative learning. It enables individual devices to build a knowledge-shared model and remaining sensitive data locally. However, with the continuous increase of users, the heterogeneity gap between data and equipment becomes wider, which leads to the problem of model heterogeneous. In this article, we concentrate on two practical issues: data heterogeneous problem and model heterogeneous problem, and propose a novel personal MPL method named device-performance-driven heterogeneous MPL (HMPL). First, facing the data heterogeneous problem, we focus on the problem of various devices holding arbitrary data sizes. We introduce a heterogeneous feature-map integration method to adaptively unify the various feature maps. Meanwhile, to handle the model heterogeneous problem, as it is essential to customize models for adapting to the various computing performances, we propose a layer-wise model generation and aggregation strategy. The method can generate customized models based on the device's performance. In the aggregation process, the shared model parameters are updated through the rules that the network layers with the same semantics are aggregated with each other. Extensive experiments are conducted on four popular datasets, and the result demonstrates that our proposed framework outperforms the state of the art (SOTA).

HPNet: Text Detection Network with Hybrid Attention and Pixel Aggregation for Irregularly-Shaped Nearby Texts

Scene text detection is a challenging topic in computer vision, characterized by complex illumination, irregular shape, and arbitrary size. While recent advancements have been made in scene text detection, it remains difficult to simultaneously distinguish nearby text and accommodate irregularly shaped text. Therefore, this paper introduces HPNet, an enhanced text detector, based on the segmentation method that predicts two-scale results. To improve the shape robustness, the Hybrid Attentional Feature Fusion (HAFF) module is integrated into Feature Pyramid Networks (FPN) to dynamically perform feature fusion. Additionally, to distinguish nearby text, the model predicts the text region covering text instances and the text kernel covering the central region of the text. The improved Pixel Aggregation (PA) algorithm is then utilized to guide the expansion from the text kernel to the text region. Experiments on IC15, Total-Text, and CTW1500 validate the effectiveness of these improvements and the superiority of HPNet. Compared with the previous method PSENet for nearby texts, the proposed HPNet has improved inference speed by 63.6% and F-measure metric by 2.6%, 3.7%, and 2.5% on three datasets, respectively.

NIR-II light-encoded 4D-printed magnetic shape memory composite for real-time reprogrammable soft actuator

For the intelligent 4D-printed actuators, the excellent performance, including quickly reversible spatial-shape transformation and locking, digital and precise shape manipulation in real-time, and remote actuation in special spaces or harsh environments, is significantly desirable but still challenging. Here, using a UV-curable system containing the shape memory polymer (SMP) and NdFeB particles, namely the magSMP composite, we fabricate a real-time reprogrammable soft actuator via high-resolution Digital Light Processing (DLP)-based 4D printing. The printed structure is composed of an array of physical binary magSMP composite elements (m-bits), analogous to digital bits. Owing to the NdFeB's photothermal effect, each m-bit can be independently and reversibly switched between unlocking or locking states (allowing or prohibiting responsive shape-morphing) in response to the on/off state of NIR-II light. Through projecting NIR-II light patterns for encoding a set of binary instructions onto 4D-printed actuators, the real-time light-programmed deformations are induced precisely under an actuation magnetic field due to the NdFeB's huge coercivity. Thus, the synergistic magnetic and light field-manipulated multimodal deformations of actuators, including mimosa shape changing, grasping, and wire guiding, are achieved. This study shines lights on the fabrication of soft structures of arbitrary sizes and provides their future perspectives in soft robot design.

Negative results on density determination with one-dimensional cellular automata with block-sequential asynchronous updates

A large number of research efforts have been made in trying to solve global decision problems with cellular automata by means of their cells reaching a distributed consensus via their local action. Among them, the determination of the most frequent state in configurations with arbitrary size, i.e., the density classification task, has been the most widely approached benchmark problem. The literature abounds with cases demonstrating that, depending on how it is formulated, a solution can be shown to exist or not. Here we address the problem in terms of deterministic, block-sequential asynchronous updates, over cyclic configurations, by which the possibility of a solution remains open. Our main results are negative in terms of the possibility of solving the problem with such formulation, encompassing the cases of any cellular automaton with any sequential update, and any elementary cellular automaton with any block-sequential update; furthermore, we uncover some properties that any potential solution with block-sequential update should have in order for it to be a candidate to solving the problem. Incidentally, we also give a new, very simple proof of the impossibility of solving the problem with any synchronous rule.

Quantum-assisted rendezvous on graphs: explicit algorithms and quantum computer simulations

Abstract We study quantum advantage in one-step rendezvous games on simple graphs analytically, numerically, and using noisy intermediate-scale quantum (NISQ) processors. Our protocols realise the recently discovered (Mironowicz 2023 New J. Phys. 25 013023) optimal bounds for small cycle graphs and cubic graphs. In the case of cycle graphs, we generalise the protocols to arbitrary graph size. The NISQ processor experiments realise the expected quantum advantage with high accuracy for rendezvous on the complete graph K 3. In contrast, for the graph 2 K 4 , formed by two disconnected 4-vertex complete graphs, the performance of the NISQ hardware is sub-classical, consistent with the deeper circuit and known qubit decoherence and gate error rates.

Generation of Absolute Patterns for Magnetic Encoders of Arbitrary Size With De Bruijn Sequences

Generation of Absolute Patterns for Magnetic Encoders of Arbitrary Size With De Bruijn Sequences

Density change consistency clustering from density extreme

Density-based clustering algorithms can identify clusters with arbitrary shapes and sizes. However, the algorithms still have difficulty detecting clusters with heterogeneous density within or between clusters. To overcome the weakness, we propose an effective clustering algorithm, called ETCD. First, starting from the density extremes, ETCD identifies the points satisfying density change consistency to generate the initial clusters, in which the density differences between neighboring points are slight and the points density decrease from center to boundary. Next, some initial clusters are merged based on density difference between clusters and attachness of clusters borders. To comprehensively demonstrate the performance of ETCD, we benchmarked ETCD on over 40 datasets with 4 classical baselines and 7 State-Of-The-Arts. Experimental results indicate ETCD outperforms the eleven baselines in most cases. ETCD is robust to density change within and between clusters, as well as clusters with different sizes, arbitrary shapes and various compactness.

Study of the fuel irradiation capsule using NEPTUNE_CFD software

The Fuel Irradiation Capsule (FUICA) is an experimental device designed to irradiate fuel elements in a Material Testing Reactor (MTR) during long period. This component is being studied for implementation in the Jules Horowitz reactor (RJH) under construction in France (1).Its function is to irradiate and to cool fuel sample on a passive way by transferring the thermal energy generated to the device periphery, through an annular zone of confined and pressure-regulated water (2). Its external wall, kept cold by the reactor cooling circuit, allows for the heat extraction. Natural convection is the driving force for the heat transfer between the fuel element and the cold wall of the device. By eliminating any internal forced convection cooling system, the device becomes relatively simple and cost-effective.This paper describes the device modeling carried out using the NEPTUNE_CFD software (3). The present work, in its first part, focuses on the behavior of the device depending on the heat flux, and in its second part, the sensitivity to boundary conditions. The objective is to provide a way to design and optimize the fuel capsule thermal characteristics. For that, an arbitrary size of fuel capsule is chosen in order to analyze the physics involved. A sensitivity study is then presented in order to highlight the influence of the physical parameters on the fuel capsule performance.Three operating ranges for the heat transfer are exhibited in this study: moderate in the liquid phase, the heat transfer becomes optimal in the two-phase regime, and even improves with the level of heat flux before deteriorating abruptly for high flux called Critical Heat Flux (CHF). Two observations also arise to improve thermal transfer: in the liquid phase, it is necessary to reduce the water thickness to improve convection, and in the two-phase regime, the heat transfer increases proportionally to the heat flux. In the end, the irradiation capsule is capable of extracting very high heat flux.

Measurement-induced phase transitions by matrix product states scaling

We study the time evolution of long quantum spin chains subjected to continuous monitoring projected on matrix product states (MPS) with fixed bond dimension, by means of the Time-Dependent Variational Principle (TDVP) algorithm. The latter gives an effective unitary evolution which approximates the real quantum evolution up to the projection error. We show that such error displays, at large times, a phase transition in the monitoring strength, which can be well detected by scaling analysis with relatively low values of bond dimensions. Moreover, in the presence of U(1) global spin charge, we show the existence of a charge-sharpening transition well separated from the entanglement transition which we detect by studying the charge fluctuations of a local subpart of the system at large times. Our work shows that quantum monitored dynamics projected on MPS manifolds contains relevant information on measurement-induced phase transitions and provides a new method to identify measured-induced phase transitions in systems of arbitrary dimensions and sizes. Published by the American Physical Society 2024

Random Quantum Ising Model with Three-Spin Couplings.

We apply a real-space block renormalization group approach to study the critical properties of the random transverse-field Ising spin chain with multispin interactions. First, we recover the known properties of the traditional model with two-spin interactions by applying the renormalization approach for the arbitrary size of the block. For the model with three-spin couplings, we calculate the critical point and demonstrate that the phase transition is controlled by an infinite disorder fixed point. We have determined the typical correlation-length critical exponent, which seems to be different from that of the random transverse Ising chain with nearest-neighbor couplings. Thus, this model represents a new infinite disorder universality class.