Density Information Research Articles

Efficient and scalable wave function compression using corner hierarchical matrices.

The exponential scaling of complete active space and full configuration interaction (CI) calculations limits the ability of quantum chemists to simulate the electronic structures of strongly correlated systems. Herein, we present corner hierarchically approximated CI (CHACI), an approach to wave function compression based on corner hierarchical matrices (CH-matrices)-a new variant of hierarchical matrices based on block-wise low-rank decomposition. By application to dodecacene, a strongly correlated molecule, we demonstrate that CH matrix compression provides superior compression compared to truncated global singular value decomposition. The compression ratio is shown to improve with increasing active space size. By comparison of several alternative schemes, we demonstrate that superior compression is achieved by (a) using a blocking approach that emphasizes the upper-left corner of the CI vector, (b) sorting the CI vector prior to compression, and (c) optimizing the rank of each block to maximize information density.

Nanoscale phase transformation in SiC wafer by spatiotemporal tailored ultrafast laser pulses for multiple optical data encryption

Color center has shown great potential in optical data encryption due to its tunable optical performance. In this work, precise and controllable implantation of color centers within 4H-SiC wafers has been demonstrated by spatiotemporal tailored ultrafast laser irradiation. Distinct microstructures via micro-ablation, micro-cracking and internal phase transformation can be achieved by precisely regulating the spatial energy input in SiC wafer. The orientation of generated microstructures can be accurately controlled by tuning the laser polarization directions. Those color centers can be further customized by varying the defocusing distances and pulse durations. Wherein, nanoscale amorphous carbon layer with thickness ~ 100 nm was achieved by ultrafast laser induced internal phase transformation, which was ascribed to an enhanced near-field optical effect. Due to the thin-film interference, various colors can display from pink to light-green in those laser modified samples with different carbon nanolayer depths. With the highly flexible and precise modification in SiC crystal, multiple colors can therefore be implanted to encode optical information at small scale, allowing high density and complex information storage. This represents a significant advance in controlled phase transformation for nanoscale color centers fabrication, which is promising in multiple optical data encryption.

DP-ID: Interleaving and Denoising to Improve the Quality of DNA Storage Image.

In the field of storing images into DNA, the code tables and universal error correction codes have the potential to mitigate the effect of base errors to a certain extent. However, they prove to be ineffective in dealing with indels (insertion and deletion errors), resulting in a decline in information density and the quality of reconstructed image. This paper proposes a novel encoding and decoding method named DP-ID for storing images into DNA that improves information density and the quality of reconstructed image. Firstly, the image is compressed as bitstreams by the dynamic programming algorithm. Secondly, the bitstreams obtained are mapped to DNA, which are then interleaved. The reconstructed image is obtained by applying median filtering to remove salt-and-pepper noise. Simulation results show the reconstructed image by DP-ID at 5% error rate is better than that by other methods at 1% error rate. This robustness to high errors is compatible with the unsatisfied biological constraints caused by high information density. Wet experiments show that DP-ID can reconstruct high quality image at 5X sequencing depth. The high information density and low sequencing depth significantly reduce the cost of DNA storage, facilitating the large-scale storage of images into DNA.

A Spectral–Spatial Approach for the Classification of Tree Cover Density in Mediterranean Biomes Using Sentinel-2 Imagery

Tree canopy cover is an important forest inventory parameter and a critical component for the in-depth mapping of forest fuels. This research examines the potential of employing single-date Sentinel-2 multispectral imagery, combined with contextual spatial information, to classify areas based on their tree cover density using Random Forest classifiers. Three spatial information extraction methods are investigated for their capacity to acutely detect canopy cover: two based on Gray-Level Co-Occurrence Matrix (GLCM) features and one based on segment statistics. The research was carried out in three different biomes in Greece, in a total study area of 23,644 km2. Three tree cover classes were considered, namely, non-forest (cover < 15%), open forest (cover = 15%–70%), and closed forest (cover ≥ 70%), based on the requirements set for fuel mapping in Europe. Results indicate that the best approach identified delivers F1-scores ranging 70%–75% for all study areas, significantly improving results over the other alternatives. Overall, the synergistic use of spectral and spatial features derived from Sentinel-2 images highlights a promising approach for the generation of tree cover density information layers in Mediterranean regions, enabling the creation of additional information in support of the detailed mapping of forest fuels.

Dotless Arabic Text for Natural Language Processing

Abstract This article introduces a novel representation of Arabic text as an alternative approach for Arabic NLP, inspired by the dotless script of ancient Arabic. We explored this representation through extensive analysis on various text corpora, differing in size and domain, and tokenized using multiple tokenization techniques. Furthermore, we examined the information density of this representation and compared it with the standard dotted Arabic text using text entropy analysis. Utilizing parallel corpora, we also drew comparisons between Arabic and English text analysis to gain additional insights. Our investigation extended to various upstream and downstream NLP tasks, including language modeling, text classification, sequence labeling, and machine translation, examining the implications of both the representations. Specifically, we performed seven different downstream tasks using various tokenization schemes comparing the standard dotted text with dotless Arabic text representations. Performance using both the representations were comparable across different tokenizations. However, dotless representation achieves these results with significant reduction in vocabulary sizes, and in some scenarios showing reduction of up to 50%. Additionally, we present a system that restores dots to the dotless Arabic text. This system is useful for tasks that require Arabic texts as output.

Number training in simultaneous interpreting: A corpus-assisted longitudinal study

Numbers are regarded as a common problem trigger in simultaneous interpreting (SI). Their low predictability and high information density can generate additional cognitive loads for interpreters, especially for trainees. The data of this study were collected from the English into Mandarin Chinese SI performances by trainees over a semester-long period of training to explore if there was a correlation between training and the accuracy with which numbers are rendered in SI. Specifically, this study adopts a longitudinal approach to investigate the impact of a 13-week formal training course on trainees’ ability to render numbers, and to determine the accuracy of different number types in renditions using six accuracy indicators. Two research questions were answered: (a) The 13-week formal training course did not significantly improve trainees’ accuracy in rendering numbers; (b) among number types, part of name, ranges, and small and whole numbers led to the poorest performance, which were attributed to source-text factors and lack of visual reference points. These results suggest that SI training could combine numbers with additional challenges, such as accents or fast speech delivery, and focus more on ranges and small numbers. Future studies could further control the influential variables identified in this research to validate their impact on the rendition of numbers.

Multipartite entanglement in crossing the quantum critical point

Abstract We investigate the multipartite entanglement for a slow quantum quench crossing a critical point. We consider the quantum Ising model and the Lipkin-Meshkov-Glick model, which are local and full-connected quantum systems, respectively. The multipartite entanglement is quantified by quantum Fisher information with the generator defined as the operator of the ferromagnetic order parameter. The quench dynamics begins with a ground state in a paramagnetic phase, and then the transverse field is driven slowly to cross a quantum critical point, and ends with a zero transverse field. For the quantum Ising model, based on methods of matrix product states, we calculate the quantum Fisher information density of the final state. Numerical results of both linear and nonlinear quenches show that the quantum Fisher information density of the final state scales as a power law of the quench rate, which overall conforms to the prediction of the Kibble-Zurek mechanism with a small correction. We show that this correction results from the long-range behaviors. We also calculate the quantum Fisher information density in the Lipkin-Meshkov-Glick model. The results show that the scaling of quantum Fisher information in this full-connected system conforms to the Kibble-Zurek mechanism better, since the long-range physics cannot be defined in this nonlocal system. Our results reveal that the multipartite entanglement provides an alternative viewpoint to understand the dynamics of quantum phase transitions, specifically, the nontrivial long-range physics.

Beyond Photochromism: Alternative Stimuli to Trigger Diarylethenes Switching.

Diarylethenes (DAEs) are typical photochemically reversible type (P-type) photochromic materials with excellent thermal stability and high fatigue resistance and are widely exploited as photo-switches for various applications in molecular devices, data storage, photoresponsive materials, and bioimaging, etc. In recent years, there is an increasing number of reports using heat, acid, electrochemistry, etc. to drive the isomerization reaction of DAEs. The response to two or more different stimuli enables multi-functionality within a single DAE molecule, which would facilitate complex logic-gate operations, multimode data storage, and increased information density. Herein, the recent advances in DAE systems utilizing stimuli "beyond photo" to trigger the isomerization processes from three perspectives: acidochromism, thermochromism, and electrochromism are reviewed. Emphasis is placed on the molecule design strategies and the underlying mechanisms for cyclization and cycloreversion processes addressed by the alternative stimulus. Then the noticeable applications made in multi-stimuli responsive DAE systems are summarized. Additionally, the challenges and opportunities of DAE switches driven by stimuli "beyond photo" in the future are also discussed.

Rhythmic qualities of jazz improvisation predict performer identity and style in source-separated audio recordings.

Great musicians have a unique style and, with training, humans can learn to distinguish between these styles. What differences between performers enable us to make such judgements? We investigate this question by building a machine learning model that predicts performer identity from data extracted automatically from an audio recording. Such a model could be trained on all kinds of musical features, but here we focus specifically on rhythm, which (unlike harmony, melody and timbre) is relevant for any musical instrument. We demonstrate that a supervised learning model trained solely on rhythmic features extracted from 300 recordings of 10 jazz pianists correctly identified the performer in 59% of cases, six times better than chance. The most important features related to a performer's 'feel' (ensemble synchronization) and 'complexity' (information density). Further analysis revealed two clusters of performers, with those in the same cluster sharing similar rhythmic traits, and that the rhythmic style of each musician changed relatively little over the duration of their career. Our findings highlight the possibility that artificial intelligence can perform performer identification tasks normally reserved for experts. Links to each recording and the corresponding predictions are available on an interactive map to support future work in stylometry.

An improved laser photo-detachment diagnostic for negative ion density measurement

A photo-detachment Langmuir probe is a crucial tool because it gives a point measurement of negative ion density. The detection circuitry of a photo-detachment diagnostic with nanosecond laser pulses is critical for the accuracy of the results. Applying the electromagnetic theory to the design of the photo-detachment system has allowed it to stabilize its frequency response up to ~445 MHz, providing a significantly higher time resolution than in a common photo-detachment circuit setup. A systematic design rule is given in this paper to standardize the proper circuit. The new standard allows comparison between laboratories without concern for electronic parameter differences. The high-time resolution result shows three different peaks in the photo-detached electron current. This paper identified that the first peak is the most correlated to negative-ion density information, and the second and third peaks are related to background electrons interacting with build-up potential.

Composite Hedges Nanopores codec system for rapid and portable DNA data readout with high INDEL-Correction

Reading digital information from highly dense but lightweight DNA medium nowadays relies on time-consuming next-generation sequencing. Nanopore sequencing holds the promise to overcome the efficiency problem, but high indel error rates lead to the requirement of large amount of high quality data for accurate readout. Here we introduce Composite Hedges Nanopores, capable of handling indel rates up to 15.9% and substitution rates up to 7.8%. The overall information density can be doubled from 0.59 to 1.17 by utilizing a degenerated eight-letter alphabet. We demonstrate that sequencing times of 20 and 120 minutes are sufficient for processing representative text and image files, respectively. Moreover, to achieve complete data recovery, it is estimated that text and image data require 4× and 8× physical redundancy of composite strands, respectively. Our codec system excels on both molecular design and equalized dictionary usage, laying a solid foundation approaching to real-time DNA data retrieval and encoding.

Clustering by detecting skeletal structure and identifying density fluctuation

Clustering is one of the most important techniques for unsupervised learning, it tries to divide points into different clusters without any priori knowledge of data. Therefore, several criterions for clustering algorithm are as follows: 1. Handling clusters with arbitrary shape and various density; 2. Finding cluster centers automatically; 3. Low parameter sensitivity and computational complexity. In this context, a novel algorithm namely clustering by detecting skeletal structure and identifying density fluctuation (CSSDF) was presented. In CSSDF, an efficient strategy based on density and local information of neighborhood is firstly proposed to detect the skeletal structure, which can collect the local information and identify the rough distribution of data. With the identified distribution information, a method takes expanded neighborhood and density fluctuation into consideration is proposed to further collect global information of data, which can assign all skeleton points and find cluster centers. To sum up, CSSDF can not only discover the underlying structure of data regardless of its’ distribution, but also ensure the correct assignment of all skeleton points and thus lead to a satisfying clustering performance. In addition, the computational complexity of the proposed approach is O(nlogn), which makes it possible to deal with some large clustering problem.

The relative influence of language backgrounds, communicative text types, and disciplines in undergraduate student writing

Abstract Previous studies of undergraduate writing investigated linguistic variation across (i) assignment types, (ii) disciplines, and (iii) language backgrounds. The combined findings of these studies allowed us to formulate eight hypotheses as to how undergraduate writing is likely to vary across these three variables. Three of the hypotheses are as follows: (a) writing in humanities will have more features of ‘academic involvement’, while writing in sciences will have more features of ‘information density’; (b) assignments such as proposals and procedural recounts will have more features of ‘expression of possibility’; and (c) L1 students will use more features of ‘information density’ than L2 students. In the current study, we test these hypotheses, examining whether the language of undergraduate writing varies in accordance with the expectations from previous research. We use the dimensions identified in Goulart (2024) to examine these hypotheses in a corpus of undergraduate student writing. The results provide support for hypotheses related to disciplines and communicative purposes, but not for those related to language backgrounds.

Topological analysis of the three-dimensional radiodensity distribution of fish otoliths: Point sampling effects on dimensionality reduction

Otoliths are calcified structures found in the inner ears of teleost fish, pivotal in marine biology for studies on metabolism, age, growth, and the identification of fish stocks, potentially leading to sustainable management practices. An important feature of this structure is its density, as it corresponds to modifications in the crystalline form of calcium carbonate during the fish's lifetime, resulting in variations in its final shape. The internal and external 3D radiodensity of otoliths from different species was obtained utilizing micro-computed tomography, however, an appropriate methodology for describing and conducting comparative studies on these data appears to be absent in the current body of literature. Therefore, we study otolith density variations from 3D computed tomography images, employing the Ball Mapper technique of Topological Data Analysis. We focus on reducing the computational cost of this analysis by applying probabilistic sampling and assessing its effects on the density variations provided by the Ball Mapper graph. To determine the sample size, we used the topology to establish what we term "Topological Sample Validation", which provided the minimum resolution with the same density information as raw data. Sample representativeness was validated through non-parametric statistical tests on the density variable. Based on the network's structural characteristics, network properties allowed for evaluating similarity between graphs. Besides the small sample size, remarkable correlations were obtained between age and network variables. Additionally, the Ball Mapper technique proved effective as a preprocessing algorithm for tomographic images, enabling the segmentation of undesired features in the object of interest.

Deep learning-based automated identification on vortex-induced vibration of long suspenders for the suspension bridge

Large amplitude vortex-induced vibration (VIV) of long suspenders, caused by vortex-shedding and wake flow, will decrease the service life of high-strength wires and its anchorage systems, hence listed as an essential monitoring indicator of the structural health monitoring system in the suspension bridges. Traditionally, a static vibration amplitude limitation is usually predefined and used to identify VIV from continuously monitored acceleration data of bridge suspenders. Due to the complexity of the starting vibration of the suspenders, it may not be able to flexibly adapt to the suspenders VIV under different conditions. In addition, transient but significant vibration events may not be captured since it relies only on static vibration amplitude limits without considering the temporal information of vibration. To address the above-mentioned issues, a novel framework to autonomously identify the suspenders VIV is proposed using the multimodal fusion techniques with deep neural networks. The main contributions of the methodology are: i) by calculating the wind field and vibration characteristic, two vibration response parameters are identified as indexes for the identification of suspenders VIV based on the significant difference method of big data analysis; ii) Two different modal information of time history images and power spectral density (PSD) sequences are used as inputs to the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to extract the suspenders vibration response features from the time and frequency domain perspectives, respectively; and iii) The multimodal information is concentrated and enhanced by the multi-head attention (MHA) mechanism to achieve automated identification of the suspenders VIV. The proposed framework is validated with data collected from a full-scale long-span suspension bridge in comparison with the base model. The results show that the proposed framework can efficiently identify the full-cycle development process of the suspenders VIV. This study provides a convenient strategy for suspenders VIV identification, which can be used for bridge management and vibration mitigation device design.

Adjustable viewpoint allocation for enhancing the viewing angle and reducing the crosstalk in super multi-view display.

In this Letter, we propose an adjustable viewpoint allocation method with forward and backward ray tracing to enhance the viewing angle and reduce the crosstalk in super multi-view (SMV) display. The synthetic image (SI) is initially calculated by backward ray tracing according to the viewing distance and the viewpoint interval. Forward ray tracing is then performed on the result of the viewpoint allocation to correct the deviation between the actual and the ideal viewpoint positions. Optical experiments are performed to verify the effectiveness of the proposed method. The results show that the proposed method effectively suppresses the crosstalk between viewpoints and eliminates artifacts in adjacent viewing areas within the ideal viewing angle, thereby enhancing the actual viewing angle. Meanwhile, due to the ability of reallocating information on the SI, the proposed method can achieve varying information density from different views. It is expected that our proposed method could be widely used in light field display to realize high quality display in the future.

Combining geometrical and intensity information to recognize vehicles from super-high density UAV-LiDAR point clouds

The traditional airborne LiDAR point clouds vehicle recognition algorithms only utilize the contained geometric information and are not sufficient to recognize vehicles accurately, especially in complex urban scenes. And their applicability in super-high density UAV-LiDAR point clouds is unknown. Therefore, a 3-D algorithm combining geometric and intensity information from super-high density UAV-LiDAR point clouds is presented. The proposed algorithm firstly converts the original point clouds into a 3D multivalued image which fuses intensity, elevation and density information simultaneously. Thereafter, the potential vehicle voxels are extracted according to the intensity, elevation and density consistency of vehicles. Subsequently, individual vehicles are recognized using the potential vehicle voxel’s spatially connected set with vehicle size constraint. Finally, the quantified attribute information of each individual vehicle, containing the spatial location, type, and size, is determined. The results for recognized vehicles are evaluated using UAV-LiDAR data with different densities and demonstrate an average quality (Kappa coefficient) of 96.58% (96.04%) without being significantly affected by occlusion and the very close vehicle arrangement.

Heterogeneous Hierarchical Fusion Network for Multimodal Sentiment Analysis in Real-World Environments

Multimodal sentiment analysis models can determine users’ sentiments by utilizing rich information from various sources (e.g., textual, visual, and audio). However, there are two key challenges when deploying the model in real-world environments: (1) the limitations of relying on the performance of automatic speech recognition (ASR) models can lead to errors in recognizing sentiment words, which may mislead the sentiment analysis of the textual modality, and (2) variations in information density across modalities complicate the development of a high-quality fusion framework. To address these challenges, this paper proposes a novel Multimodal Sentiment Word Optimization Module and a heterogeneous hierarchical fusion (MSWOHHF) framework. Specifically, the proposed Multimodal Sentiment Word Optimization Module optimizes the sentiment words extracted from the textual modality by the ASR model, thereby reducing sentiment word recognition errors. In the multimodal fusion phase, a heterogeneous hierarchical fusion network architecture is introduced, which first utilizes a Transformer Aggregation Module to fuse the visual and audio modalities, enhancing the high-level semantic features of each modality. A Cross-Attention Fusion Module then integrates the textual modality with the audiovisual fusion. Next, a Feature-Based Attention Fusion Module is proposed that enables fusion by dynamically tuning the weights of both the combined and unimodal representations. It then predicts sentiment polarity using a nonlinear neural network. Finally, the experimental results on the MOSI-SpeechBrain, MOSI-IBM, and MOSI-iFlytek datasets show that the MSWOHHF outperforms several baselines, demonstrating better performance.

Ionisation of atoms determined by kappa refinement against 3D electron diffraction data.

Conventional refinement strategies used for three-dimensional electron diffraction (3D ED) data disregard the bonding effects between the atoms in a molecule by assuming a pure spherical model called the Independent Atom model (IAM) and may lead toan inaccurate or biased structure. Here we show that it is possible to perform a refinement going beyond theIAM with electron diffraction data. We perform kappa refinement which models charge transfers between atoms while assuming a spherical model. We demonstrate the procedure by analysing five inorganic samples; quartz, natrolite, borane, lutecium aluminium garnet, and caesium lead bromide. Implementation of kappa refinement improved the structure model obtained over conventional IAM refinements and provided information on the ionisation of atoms. The results were validated against periodic DFT calculations. The work presents an extension of the conventional refinement of 3D ED data for a more accurate structure model which enables charge density information to be extracted.

From megabytes to molecules: The usage of DNA computing for storage and its implications on the future

It is more important now than ever to develop more effective data storage techniques in the constantly advancing field of computers. Due to their limits in terms of durability, information density, and physical space requirements, current storage technologies, such as magnetic and optical media, are finding it difficult to keep up with the global demand for data storage, which is expected to exceed 175 trillion gigabytes by 2025. Nature’s own method of storing genetic information for a long time, deoxyribonucleic acid (DNA), provides great data density, stability, and endurance, making it a viable solution to address this issue. Through a comprehensive analysis of existing literature, this paper aims to provide a nuanced understanding of DNA computing’s current state - mainly its trajectory towards becoming a viable alternative to traditional storage methods - highlighting the history, current developments/challenges, and future prospects.