Density Information Research Articles

Prompt-supervised dynamic attention graph convolutional network for skeleton-based action recognition

Skeleton-based action recognition is a core task in the field of video understanding. Skeleton sequences are characterized by high information density, low redundancy, and clear structural information, thereby facilitating the analysis of complex relationships among human behaviors more readily than other modalities. Although existing studies have encoded skeleton data and achieved positive outcomes, they have often overlooked the precise high-level semantic information inherent in the action descriptions. To address this issue, this paper proposes a prompt-supervised dynamic attention graph convolutional network (PDA-GCN). Specifically, the PDA-GCN incorporates a prompt supervision (PS) module that leverages a pre-trained large-scale language model (LLM) as a knowledge engine and retains the generated text features as prompts to provide additional supervision during model training, enhancing the model’s ability to discern analogous actions with negligible computational cost. In addition, for the purpose of bolstering the learning of discriminative features, a dynamic attention graph convolution (DA-GC) module is presented. This module utilizes self-attention mechanism to adaptively infer intrinsic relationships between joints and integrates dynamic convolution to strengthen the emphasis on local information. This dual focus on both global context and local details further amplifies the efficiency and effectiveness of the model. Extensive experiments, conducted on the widely-used skeleton-based action recognition datasets NTU RGB+D 60 and NTU RGB+D 120, demonstrate that the PDA-GCN surpasses known state-of-the-art methods, achieving accuracies of 93.4% on the NTU RGB+D 60 cross-subject split and 90.7% on the NTU RGB+D 120 cross-subject split.

Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system

DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.

Selective signal enhancement in Fourier space as a tool for discovering ultrastructural organization of macromolecules from in situ TEM

We present a Fourier transform (FT) based analytical method that allows to obtain of ultrastructural details from TEM images at sub-nanometer scale applying a selective filtering for singular macromolecule electron microscopy density information. It can be applied to high-pressure frozen, frozen hydrated and epoxy freeze substituted and embedded biological species. Both 2D projections and orthoslices from reconstructed tomograms can be used as a source of structural information. The key to the method is to select the macromolecule or organelle of interest with an accuracy of ≥ 7 – 3 nm (depending on pixel size of initial tilt series or singular image acquisition) and explore both the central low frequency FT intensity and diffraction regions to obtain the spatial structural organization and its dimensional characteristics, respectively. We also introduce a structure-specific selective mask FT filtering approach that can significantly improve image information even in poorly contrasted TEM of resin sections without heavy metal been used. The described method elucidates chromatin architecture without the need of averaging. A zigzag symmetry of 30 nm diameter chromatin fibers which in general is a controversial topic of research has been identified for C. elegans cells in vivo with sub-nanometer details being preserved in the images.

Bio-Plausible Multimodal Learning with Emerging Neuromorphic Devices.

Multimodal machine learning, as a prospective advancement in artificial intelligence, endeavors to emulate the brain's multimodal learning abilities with the objective to enhance interactions with humans. However, this approach requires simultaneous processing of diverse types of data, leading to increased model complexity, longer training times, and higher energy consumption. Multimodal neuromorphic devices have the capability to preprocess spatio-temporal information from various physical signals into unified electrical signals with high information density, thereby enabling more biologically plausible multimodal learning with low complexity and high energy-efficiency. Here, this work conducts a comparison between the expression of multimodal machine learning and multimodal neuromorphic computing, followed by an overview of the key characteristics associated with multimodal neuromorphic devices. The bio-plausible operational principles and the multimodal learning abilities of emerging devices are examined, which are classified into heterogeneous and homogeneous multimodal neuromorphic devices. Subsequently, this work provides a detailed description of the multimodal learning capabilities demonstrated by neuromorphic circuits and their respective applications. Finally, this work highlights the limitations and challenges of multimodal neuromorphic computing in order to hopefully provide insight into potential future research directions.

DNA palette code for time-series archival data storage

Abstract The long-term preservation of large volumes of infrequently accessed cold data poses challenges to the storage community. Deoxyribonucleic Acid (DNA) is considered a promising solution due to its inherent physical stability and significant storage density. The information density and decoding sequence coverage are two important metrics that influence the efficiency of DNA data storage. In this study, we propose a novel coding scheme called DNA Palette code, which is suitable for cold data, especially time-series archival datasets. These datasets are not frequently accessed but necessitate reliable long-term storage for retrospective research. The DNA Palette code employs unordered combinations of index-free oligonucleotides (oligos) to represent binary information. It can achieve high net information density encoding and lossless decoding with low sequencing coverage. When sequencing reads are corrupted, it can still effectively recover partial information, preventing the complete failure of file retrieval. The in vitro testing of clinical brain magnetic resonance imaging (MRI) data storage, as well as simulation validations using large-scale public MRI datasets (10 GB), planetary science datasets, and meteorological datasets, demonstrate the advantages of our coding scheme, including high information density, low decoding sequence coverage, and wide applicability.

ASSESSMENT OF THE IMPACT OF WELL GRID DENSITY ON OIL RECOVERY UNDER CONDITIONS OF UNCERTAINTY

The density of the well grid is one of the most important parameters that has a significant impact on the rate and amount of oil reserves extraction. As practice shows, existing approaches to the reliable assessment and identification of the degree of influence of this indicator on oil recovery do not allow us to adequately form an idea of the dynamics of reservoir processes and their role in making managerial decisions in conditions of uncertainty. Taking into account the decrease in the number of hydrodynamic and geophysical studies conducted in relation to the total number of wells in operation at fields that are being developed for a long time or just being put into development, the issue of predicting the oil recovery coefficient according to the list of geological and field parameters remains open and requires an urgent solution, including those related to the use of unique complex algorithms. The purpose of the work is to determine the relationship between the density of the well grid and the final coefficient of oil recovery in the development of fields in natural conditions using computer modeling systems. The research methodology consists of the use of existing scientific and methodological approaches to the processing of geological and commercial information in conditions of different densities in combination with the use of various advanced systems for identifying hidden patterns. Geological and statistical models have been defined for the oil deposits of the Famensk tier of the South Tatar arch of the Volga-Ural oil and gas province, allowing for the forecasting procedure for groups of objects identified during differentiation, subject to the dynamic density of geological and field information and various technical and economic indicators of field development. A comprehensive interpretation of the nature and degree of influence of geological and technological parameters on the amount of oil reserves production has been carried out. The need for preliminary differentiation of deposits based on identification with the subsequent use of the presented geological and statistical models to reduce the risks of making erroneous decisions during activities aimed at optimizing the development of analog facilities and rational extraction of residual oil reserves is reliably substantiated.

Investigation of co-flow step emulsification (CFSE) microfluidic device and its applications in digital polymerase chain reaction (ddPCR)

HypothesisThe co-flow step emulsification (CFSE) is very sensitive to the two-phase fluid interfaces, we conjecture that the CFSE hydrodynamic model depends on several key factors and the droplet generation process can be precisely controlled, thus to obtain droplet emulsions with the “ultra-high volume fraction of inner-phase” and “flexible droplet size” characteristics. The resulting droplets are expected to be applied to droplet digital PCR (ddPCR) with “high information density” and “wide dynamic range” advances. ExperimentsBy combining numerical simulation and fluid dynamics experiments, we have investigated the crucial parameters affecting the CFSE two-phase interface and finally achieved the prediction and guidance for CFSE droplet production. FindingsWith the help of the CFSE device, multivolume droplet populations were produced on demand. Then, ddPCR tests were performed with DNA concentrations from 10 copies/μL to 20,000 copies/μL. The CFSE device owns an ultra-wide dynamic range (up to 5 orders of magnitude), showing excellent quantification ability of nucleic acid targets.

A spectroscopic analysis code for spatially resolved x-ray absorption data from the COAX platform.

Sophisticated tools such as computer vision techniques in combination with 1D lineout type analyses have been used in automating the analysis of spectral data for high energy density (HED) plasmas. Standardized automation can solve the problems posed by the complexity of HED spectra and the quantity of data. We present a spectroscopic code written for automated and streamlined analysis of spatially resolved x-ray absorption data from the COAX platform on Omega-60. COAX uses radiographs and spectroscopic diagnostics to provide shock position and density information. We also obtain the more novel spectral-derived spatial profile of the supersonic radiation flow into a low-density foam. Considerable effort has been spent modernizing our previous spectroscopic analysis method, including the development of new tools characterized by a faster runtime and minimal user input to reduce bias and a testing suite for verifying the accuracy of the various functions within the code. The new code analyzes our spectroscopic images in 1-2min, with added uncertainty and confidence.

Time-dependent probability density functions and information geometry in a stochastic prey–predator model of fusion plasmas

Time-dependent probability density functions and information geometry in a stochastic prey–predator model of fusion plasmas

Mutual Information Density of Massive MIMO Systems Over Rayleigh-Product Channels

Mutual Information Density of Massive MIMO Systems Over Rayleigh-Product Channels

Clutaxis: an information-driven source search method balancing exploration and exploitation in turbulent environments

Locating unknown emission sources in turbulent environments is a challenging yet crucial task, particularly in emergency response scenarios. Existing studies have developed information-theoretic approaches to fuse intermittent information collected by mobile sensors regarding the sources. This fused information is then used to support source-term estimation (STE) in various search algorithms. Among these, the cognitive strategy—a promising information-driven search algorithm—leverages a reward-based action selection mechanism to balance exploration and exploitation during each search step. However, this mechanism is hampered by a high computational load and rigid search trajectories, limiting its application in real-world systems. To address these issues, this paper proposes a novel information-driven search method called Clutaxis, based on a global exploration and exploitation tradeoff principle. Specifically, a particle filter is leveraged to maintain the STE. After projecting the particle filter samples onto a 2D search scene, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to extract the density information of the samples, which is then used to construct a belief source area (BSA). By leveraging the uncertainty of the BSA, Clutaxis adopts explorative or exploitative actions with no restrictions on motion direction. Through dedicated simulations, the experimental results demonstrate the robustness of Clutaxis to key parameters and its advantages in computational complexity and search performance compared to two state-of-the-art algorithms (Infotaxis and Entrotaxis) and two Clutaxis variants (Clutaxis_ER and Clutaxis_EI).

QRGB+: Advanced QR Code Generator with RGB Color Method in Python to Expand Data Capacity

The present work entitled QRGB, consists of the development of an application in Python for the generation of QR codes using the additive color generation (RGB) method. This innovative method allows increasing the density of information stored in QR codes by using three color layers (red, green and blue), each representing a different set of data. QRGB offers an efficient and secure solution for storing and transmitting large amounts of information in limited spaces, significantly improving the capabilities of traditional black and white QR codes. By using three layers of colors, QRGB codes can store up to three times more information in the same space. This technique not only increases storage capacity but also improves information security, making it difficult to forge or manipulate the code. The overlay of multiple data layers allows redundancy to be implemented, increasing the robustness of the code against damage or reading errors. QRGBs are especially useful in applications that require the transmission of large amounts of data in limited spaces, such as in the packaging industry, digital business cards, and interactive advertising. Additionally, they have great potential in areas such as document and banknote security, where the authenticity and integrity of information are crucial. These points provide a solid foundation for understanding the innovation and advantages of colored QR codes (QRGB) compared to traditional QR codes, highlighting their applicability and potential in various sectors. This article presents a novel method for encoding and decoding information using a QRGB code, which involves the generation of three independent QR codes and their superimposition according to the additive color system (RGB). The research highlights the challenges encountered during the encoding and decoding processes due to the lack of specific libraries in Python, which required the creation of a custom solution using open source tools. The implementation takes advantage of Python and its libraries: qrcode[pil] to generate QR codes with the Pillow dependency for image manipulation, Pillow to open, manipulate and save different image formats, and opencv-python to perform tasks such as image processing and object detection. Despite facing issues with color mixing and accurate information retrieval, the proposed method demonstrates a significant increase in data density within a single QR code. Future work will focus on optimizing the algorithm and exploring potential applications in data security and high-density information storage. This Python script is designed to generate and decode QR codes with a logo overlay using a graphical user interface (GUI) built with Tkinter. The script combines several functionalities, such as creating QR codes, overlaying a logo, combining QR images of different colors, and manually decoding combined QR codes.

Information density threshold of urban road traffic signs based on visual comfort

The content and density of traffic signs directly affect the operation of urban road traffic and drivers. To overcome the limitations of quantitative research on the density threshold of traffic signs on urban roads, a real vehicle experiment was conducted to record the psychological characteristics of drivers. Four psychological parameters of drivers—pupil area, fixation intensity, heart rate change rate, and heart rate variability—were explored. Subsequently, principal component analysis was used to present a new index, S, divided into 5 grade scales, to represent the driving visual comfort level. The information entropy theory was applied to quantify the amount of information on road traffic signs that are included in driving tests, and a regression relationship between the traffic sign information and comfort index S was established. The visual psychological load thresholds for different comfort levels were −2.289≤S < −1.526 for very comfortable, 1.526≤S < −0.763 for relatively comfortable, −0.763≤S ≤ 0.763 for comfortable, 0.763<S ≤ 1.526 for uncomfortable, and 1.526<S ≤ 2.289 for very uncomfortable. To maintain the visual comfort of drivers, the information density of traffic signs should be less than 0.373 bits/m and the maximum information density of traffic signs should not exceed 0.507 bits/m. This conclusion provides a reference for the rational layout of traffic signs on urban roads.

Enhancing point cloud registration with transformer: cultural heritage protection of the Terracotta Warriors

Point cloud registration technology, by precisely aligning repair components with the original artifacts, can accurately record the geometric shape of cultural heritage objects and generate three-dimensional models, thereby providing reliable data support for the digital preservation, virtual exhibition, and restoration of cultural relics. However, traditional point cloud registration methods face challenges when dealing with cultural heritage data, including complex morphological and structural variations, sparsity and irregularity, and cross-dataset generalization. To address these challenges, this paper introduces an innovative method called Enhancing Point Cloud Registration with Transformer (EPCRT). Firstly, we utilize local geometric perception for positional encoding and combine it with a dynamic adjustment mechanism based on local density information and geometric angle encoding, enhancing the flexibility and adaptability of positional encoding to better characterize the complex local morphology and structural variations of artifacts. Additionally, we introduce a convolutional-Transformer hybrid module to facilitate interactive learning of artifact point cloud features, effectively achieving local–global feature fusion and enhancing detail capture capabilities, thus effectively handling the sparsity and irregularity of artifact point cloud data. We conduct extensive evaluations on the 3DMatch, ModelNet, KITTI, and MVP-RG datasets, and validate our method on the Terracotta Warriors cultural heritage dataset. The results demonstrate that our method has significant performance advantages in handling the complexity of morphological and structural variations, sparsity and irregularity of relic data, and cross-dataset generalization.

Outlier detection using local density and global structure

Outlier detection, a critical process for enhancing data quality, has been applied to a broad spectrum of real-world applications. This study introduces a novel and effective outlier detection method that incorporates both local density and global structural information. Specifically, we employ the concept of potential energy, a well-established principle in physics, to represent local densities or neighbor distributions of data. Additionally, we also exploit the notion of hubness, a measure explored in network science, to further capture global structural information of data. To facilitate this, we construct a strongly connected digraph by introducing a virtual node along with an associated set of virtual edges. This digraph underpins a tailored Markov random walk process that is used to estimate the hubness scores of data objects. Subsequently, the importance degrees of data objects are estimated according to their potential energies and hubness scores. The data objects with lower importance degrees are deemed to be outliers. Extensive experiments conducted on thirteen publicly accessible real-world datasets against nine popular outlier detection algorithms show that the proposed method achieved encouraging and competitive performance in comparing the state-of-the-art outlier detection algorithms.

Cable Eccentricity Detection Method Based on Magnetic Field.

Amid the rapid advancement of electronic information technology, the need for cable eccentricity measurement in the industry is increasing both in China and across the globe. Current detection methods have several flaws, including high costs, insufficient accuracy, and instability. In this paper, we introduce a magnetic field-based detection method for cable eccentricity that provides high precision and cost-effectiveness. We position three pairs of magnetic field-collection modules in a circular array to gather magnetic flux density information induced by the electrified cable. We apply the law of electromagnetic induction to calculate the cable eccentricity. Our method is non-contact, preserving the cable's integrity. Our method outperforms traditional detection methods, not only in achieving greater accuracy and stability but also in significantly lowering the detection cost. Simulations and experiments show that our method's error rate under specified conditions is 0~4%, with a maximum standard deviation of 0.11, confirming its precision and stability in detecting cable eccentricity. The effectiveness of our method is influenced by two factors: lift-off value and loading current intensity. Our method presents a novel concept and a dependable strategy for the progress of cable eccentricity-detection technology.

On the dynamics of a financial system with the effect financial information

This study aims to investigate a financial system consisting of four ordinary differential equations associated with the rate of interest, investment demand, price index, and the density of financial information gained by the population. The equilibrium and local stability of the system are investigated numerically. The impact of saving amounts and the rate of investment demand increases after getting financial information on the system are discussed. The findings of the study are verified graphically. It is found that the system becomes stable if the rate of investment demand increases after getting financial information kept at a certain level, such that the savings amount is maintained at a higher level. Also, the bifurcation diagrams of the system for various significant parameters that affect the system’s stability have been depicted.

A primordial DNA store and compute engine.

Any modern information system is expected to feature a set of primordial features and functions: a substrate stably carrying data; the ability to repeatedly write, read, erase, reload and compute on specific data from that substrate; and the overall ability to execute such functions in a seamless and programmable manner. For nascent molecular information technologies, proof-of-principle realization of this set of primordial capabilities would advance the vision for their continued development. Here we present a DNA-based store and compute engine that captures these primordial capabilities. This system comprises multiple image files encoded into DNA and adsorbed onto ~50-μm-diameter, highly porous, hierarchically branched, colloidal substrate particles comprised of naturally abundant cellulose acetate. Their surface areas are over 200 cm2 mg-1 with binding capacities of over 1012 DNA oligos mg-1, 10 TB mg-1 or 104 TB cm-3. This 'dendricolloid' stably holds DNA files better than bare DNA with an extrapolated ability to be repeatedly lyophilized and rehydrated over 170 times compared with 60 times, respectively. Accelerated ageing studies project half-lives of ~6,000 and 2 million years at 4 °C and -18 °C, respectively. The data can also be erased and replaced, and non-destructive file access is achieved through transcribing from distinct synthetic promoters. The resultant RNA molecules can be directly read via nanopore sequencing and can also be enzymatically computed to solve simplified 3 × 3 chess and sudoku problems. Our study establishes a feasible route for utilizing the high information density and parallel computational advantages of nucleic acids.

Intrinsic multiferroicity in molybdenum oxytrihalides nanowires

Low-dimensional multiferroics, which simultaneously possess at least two primary ferroic order parameters, hold great promise for post-Moore electronic devices. However, intrinsic one-dimensional (1D) multiferroics with the coexistence of ferroelectricity and ferromagnetism are still yet to be realized, which will be not only crucial for exploring the interplay between low-dimensionality and ferroelectric/ferromagnetic ordering but also significant in rendering application approaches for high density information technologies. Here, we present a theoretical prediction of intrinsic multiferroicity in 1D molybdenum oxytrihalides nanowires, especially focusing on MoOBr3 nanowires which could be readily extracted from experimentally synthesized van der Waals MoOBr3 bulk materials. Due to the spatial inversion symmetry spontaneously broken by Mo atoms’ displacements, MoOBr3 nanowires exhibit 1D ferroelectricity with small coercive electric field and exceptional Curie temperature (~570 K). Additionally, MoOBr3 nanowires also possess 1D antiferroelectric metastable states. On the other hand, both ferroelectric and antiferroelectric MoOBr3 nanowires exhibit ferromagnetic ordering on account of the half-filled Mo-dyz orbitals, a moderate tensile strain (~5%) can greatly boost the spontaneous polarization (~40%) and a mild compress strain (~−2%) may readily switch the magnetic easy axis of ferroelectric MoOBr3 nanowires. Our work holds potential candidates for developing innovative devices that exploit intrinsic multiferroic properties, enabling advancements in novel electronic and spintronic applications.

Improving the accuracy of density logging results considering mineral composition for evaluation of high-level radioactive waste disposal sites

ABSTRACT Site selection and characterisation are key factors influencing the safe, stable disposal of spent nuclear fuel. Site selection can be based on geophysical surveys, and geophysical well logging is performed at the detailed survey stage. Typically, gamma – gamma density logging is conducted to continuously obtain in situ density information regarding the well depth. However, gamma – gamma density logging is often influenced by the geological characteristics of the site. The ratio of atomic number to atomic weight is required to convert the electron density obtained through density logging counts into bulk density. Because it is difficult to calculate these ratios for the entire depth, an equation based on water-filled limestone is typically used for conversion. Therefore, as the difference in the ratio of the atomic number and atomic weight increases relative to that of limestone, the difference between the converted bulk density and the actual bulk density increases. In this study, equations were derived for various mineral compositions and applied to the density logging results. Matrix density was analyzed using a pycnometer, and the ratio between the atomic number and weight was calculated according to X-ray fluorescence analysis results. This approach improved the accuracy of the density logging data and reduced density errors. The proposed method is expected to be particularly useful for estimating in situ density at high-level radioactive waste disposal sites without complex geological formations.