Preservation Of Information Research Articles

Impact of RAW vs. RGB images on low-light object detection in autonomous driving

Abstract. In the field of autonomous driving, object detection in low-light conditions presents a critical challenge. Traditional detection algorithms, reliant on RGB imagery, often suffer from diminished performance under inadequate illumination. In contrast, RAW images, which retain a higher level of intrinsic image information, have the potential to enhance detection accuracy. Therefore, the paper aims to assess whether models trained on RAW images exhibit enhanced object detection performance in low-light scenarios. To this end, experiments are conducted utilizing the Low-light Object Detection (LOD) dataset, which includes paired RAW and RGB images, with variations in lighting simulated through different exposure times. Two distinct datasets are constructed: one consisting of RAW-normal and RAW-dark images, and another comprising RGB-normal and RGB-dark images. The models evaluated in this research include YOLOv8, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, selected to ensure robustness and generalizability of the findings. The results demonstrate that models trained on RAW images significantly outperform those trained on RGB images, thus exhibiting higher accuracy both in general and under low-light conditions. The preservation of detailed image information in RAW format facilitates enhanced feature extraction, thereby improving detection accuracy and resilience under low-light conditions. These findings provide novel insights into advancing object detection methodologies for autonomous driving systems operating in challenging lighting environments.

Optimized Methyl methacrylate embedding of small and large undecalcified bones.

Methyl methacrylate (MMA) plastic embedding has been long established as a technique for the processing and histological assessment of bones. It provides the added benefit over paraffin in that it does not require decalcification of the tissue in order visualize the cellular detail, thus preserving vital information about the amount of unmineralized osteoid present in addition to the degree of mineralization in the bone. It also allows for the incorporation of dynamic histomorphometric analysis through the retention of fluorescent labels incorporated into the bone. Efficient infiltration of hard tissue is essential to the processing of bones and producing quality slides suitable for achieving usable quantifiable histology out the other end. This technique:• Updates previously published MMA embedding protocols to reflect utilization of stabilized acrylamides (over the unstabilized reagents of the past)• Outlines the techniques that are important for embedding both small (mus), medium (rattus), and large (porcine, lagomorph, human) histological samples.• Updates the clearing and infiltration processes utilized and validates quality of the sample preparation though histological staining to confirm preservation of cellular detail, mineralization information, and enzymatic activity

Transforming University Library Building Conservation: A Descriptive Study of Preservation Practices in Nigeria

The study examined the conservation of library building for effective information preservation among university libraries in Enugu State, Nigeria. Descriptive survey with population of 325 library staff made up the study. The instruments for collecting data is questionnaire, analysed and presented in mean score, standard deviation as well as percentage and t-test. Results: conservation criteria for library building are clearing and cleaning of the library building, repair of roof, windows and doors, installation of fire and smoke alarm system, and the provision of enough security personnel around the building. Library buildings are being conserved to protect resources against natural disasters like flooding, and earthquake, against man-made disaster like fire, making library resources accessible, promote an end to wasteful use of non-renewable information resources, to encourages best practices in records management, and it provides an extremely attractive environment for the use of books. Findings on the adequacy level of policy and regulatory framework shows that disaster management, storage capacity for documentary resources, appropriateness of environmental conditions are to be consider in the library. The study recommended that the university library management should provide monitoring system for environmental condition, storage capacity for digital resources and disaster preventive measure in the library.

Unsupervised Anomaly Detection via Normal Feature-Enhanced Reverse Teacher–Student Distillation

In modern industrial production, unsupervised anomaly detection methods have gained significant attention due to their ability to address the challenge posed by the scarcity of labeled anomaly samples. Among them, unsupervised anomaly detection methods based on reverse distillation (RD) have become a mainstream choice, which has attracted extensive research due to their excellent anomaly detection performance. However, there is a problem of “feature leakage” in the RD model, which may lead to non-anomalous regions being incorrectly identified as defects. To solve this problem, we propose a Normal Feature-Enhanced Reverse teacher–student Distillation (NFERD) method. Specifically, we designed and incorporated a normal feature bank (NFB) module into the basic RD network. This module stores normal features extracted by the teacher model, assisting the student model in learning normal features more efficiently, thereby addressing the problem of “feature leakage”. In addition, to effectively fuse the feature maps extracted by the student model with the feature maps in NFBs, we designed a Hybrid Attention Fusion Module (HAFM), which ensures the preservation of key information during the feature fusion process by the parallel processing of spatial and channel attention mechanisms. Through experimental verification on two publicly available datasets, i.e., MVTec and KSDD, our method outperformed the existing mainstream methods in both image-level and pixel-level anomaly detection. Specifically, we achieved an average I-AUROC score of 99.32% on MVTec and a 98.75% P-AUROC on the KSDD, showing clearer segmentation results, especially in complex scenarios. Furthermore, our method surpassed the second-best method by over 1.4% PRO on MVTec, demonstrating its effectiveness.

Preserving quantum information in f(Q) non-metric gravity cosmology

The effects of cosmological expansion on quantum bosonic states are investigated, using quantum information theory. In particular, a generic Bogoliubov transformation of bosonic field modes is considered and the state change on a single mode is regarded as the effect of a quantum channel. Properties and capacities of this channel are thus explored in the framework of f(Q) non-metric gravity. The reason is that non-metric gravity can be considered under the standard of gauge theories with all the advantages of such a formulation. As immediate result, we obtain that the information on a single-mode state appears better preserved, whenever the number of particles produced by the cosmological expansion is small. Specifically, we investigate a power law f(Q) model, leaving unaltered the effective gravitational coupling, and minimise the corresponding particle production. We thus show how to optimise the preservation of classical and quantum information, stored in bosonic mode states in the remote past. Finally, we compare our findings with those obtained in General Relativity.

Self-supervised image change detection method based on lightweight capsule network

In response to the significant impact of speckle noise on the accuracy of SAR image change detection, the high complexity of existing capsule network based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposes a self supervised image change detection method based on the Light Capsule Network (SCapsNet). First, the log ratio operator difference graph is generated, and the "pseudo label" of training samples with high confidence is obtained through the maximum inter class variance method and fuzzy C-means clustering method, which lays the foundation for self-supervised learning; Secondly, construct a three channel training sample based on the difference map of two temporal SAR images and logarithmic ratio operators to maximize the preservation of sample information; Then, SCapsNet is designed to extract training sample features through single scale convolution, and a single scale capsule network is used to mine spatial relationships between features; Finally, comparative experiments and ablation experiments were set up and tested on 5 real SAR Datasets. The experimental results show that the The advantage of this method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

Should the multi-layer transportation network structure be reduced?

ABSTRACT The increasing diversity of transportation modes and the rapid expansion of transportation networks present significant challenges for modeling multi-layer comprehensive transportation networks. It is crucial to determine whether aggregating certain layers is a viable option for balancing complexity reduction and information preservation. This decision defines the layered structures and informs subsequent analyses of these networks. Two-dimensional factors, namely topological structures and transportation attributes, are considered to enhance understanding of the similarities among network layers. The relative entropy and the Gini index are employed as metrics to assess information gain or loss resulting from layer aggregation or segregation, guiding decisions on network reduction. Furthermore, an integrated similarity measure, based on the quantum Jensen-Shannon divergence and the Gower distance, is utilized to identify the optimal aggregation sequences. Two real-world transportation networks serve as case studies. Results demonstrate that these transportation networks are more effectively maintained with layer-separated structures, preserving maximum information.

Palette‐based colour normalization for histopathology images

AbstractWith the emergence of computer‐aided diagnostic (CAD) systems, the speed of histopathological image analysis and the accuracy of cancer detection have considerably improved. However, the appearance of histopathological slides can vary depending on the consistency of tissue thickness, stain concentrations, and equipment, thus affecting the judgment of CAD systems. This study proposes a palette‐based colour normalization method for histopathology images is proposed to solve the problem of colour differences between histopathology images. The method builds a graphical user interface based on an improved palette generation algorithm and colour transfer definitions, through which the user can complete the corresponding colour modification to achieve colour normalization. The evaluation of the metrics on histopathological images shows that our method is able to achieve higher image quality and better preservation of structural information of the source image compared with four other colour normalization algorithms. The peak signal‐to‐noise ratio values obtained by the proposed method on two publicly available datasets were 24.1914 and 21.3666, and the structural similarity index matrix values were 0.9871 and 0.9760. The proposed method provides new ideas for the development and design of CAD systems.

Hypothesis on black holes as dimensional portals and information preservation

Hypothesis on black holes as dimensional portals and information preservation

Deep silicification–assisted long-term preservation of structural and genomic information across biospecies: From micro to macro

The concurrent preservation of morphological, structural, and genomic attributes within biological samples is paramount for comprehensive insights into biological phenomena and disease mechanisms. However, current preservation methodologies (e.g., cryopreservation, chemical reagent fixation, and bioplasticization) exhibit limitations in simultaneously achieving these critical combined goals. To address this gap, inspired by natural fossilization, here we propose "deep silicification," a room temperature technology that eliminates fixation requirements and overcomes the cold chain problem. By harnessing the synergy between ethanol and dimethyl sulfoxide, deep silicification significantly enhances silica penetration and accumulation within bioorganisms, thereby reinforcing structural integrity. This versatile and cost-effective approach demonstrates remarkable efficacy in preserving organismal morphology across various scales. Accelerated aging experiments underscore a 4,723-fold enhancement in genomic information storage over millennia, with whole-genome sequencing confirming nearly 100% fidelity. With its simplicity and reliability, "deep silicification" represents a paradigm shift in biological sample storage.

Preservation of emotional context in tweet embeddings on social networking sites

In communication, emotional information is crucial, yet its preservation in tweet embeddings remains a challenge. This study aims to address this gap by exploring three distinct methods for generating embedding vectors of tweets: word2vec models, pre-trained BERT models, and fine-tuned BERT models. We conducted an analysis to assess the degree to which emotional information is conserved in the resulting embedding vectors. Our findings indicate that the fine-tuned BERT model exhibits a higher level of preservation of emotional information compared to other methods. These results underscore the importance of utilizing advanced natural language processing techniques for preserving emotional context in text data, with potential implications for enhancing sentiment analysis and understanding human communication in social media contexts.

Audio Deep Fake Detection with Sonic Sleuth Model

Information dissemination and preservation are crucial for societal progress, especially in the technological age. While technology fosters knowledge sharing, it also risks spreading misinformation. Audio deepfakes—convincingly fabricated audio created using artificial intelligence (AI)—exacerbate this issue. We present Sonic Sleuth, a novel AI model designed specifically for detecting audio deepfakes. Our approach utilizes advanced deep learning (DL) techniques, including a custom CNN model, to enhance detection accuracy in audio misinformation, with practical applications in journalism and social media. Through meticulous data preprocessing and rigorous experimentation, we achieved a remarkable 98.27% accuracy and a 0.016 equal error rate (EER) on a substantial dataset of real and synthetic audio. Additionally, Sonic Sleuth demonstrated 84.92% accuracy and a 0.085 EER on an external dataset. The novelty of this research lies in its integration of datasets that closely simulate real-world conditions, including noise and linguistic diversity, enabling the model to generalize across a wide array of audio inputs. These results underscore Sonic Sleuth’s potential as a powerful tool for combating misinformation and enhancing integrity in digital communications.

Emphasizing the Social in Sociotechnical Approaches to the Digital Curation of Visual Information

ABSTRACTThis panel explores the unique challenges and opportunities presented by the expanded sociotechnical practices associated with the preservation, curation, and use of visual information and ongoing perceptions of the affordances and constraints associated with new and emerging visual information objects. The panelists and the respondent work with the curation and preservation of visual information across a variety of research areas and utilize both qualitative and quantitative methods to understand the parallel and divergent sociotechnical challenges of curating visual information across analog, digitized, and born‐digital contexts. Sites of analysis range from format‐specific identity‐representation issues to the cultural practices of media archivists, translational challenges when moving complex scientific data across various digital and analog formats, and the role of accessibility in the design and deployment of discovery systems within cultural heritage institutions. Responding to the unique and intersecting challenges produced by working with visual information across various archival and digital curation contexts, the panelist will reflect on the practical and theoretical outcomes from ongoing research projects and identify persistent and emergent issues within the digital curation of visual information.

UICE-MIRNet guided image enhancement for underwater object detection

Underwater object detection is a crucial aspect of monitoring the aquaculture resources to preserve the marine ecosystem. In most cases, Low-light and scattered lighting conditions create challenges for computer vision-based underwater object detection. To address these issues, low-colorfulness and low-light image enhancement techniques are explored. This work proposes an underwater image enhancement technique called Underwater Image Colorfulness Enhancement MIRNet (UICE-MIRNet) to increase the visibility of small, multiple, dense objects followed by underwater object detection using YOLOv4. UICE-MIRNet is a specialized version of classical MIRNet, which handles random increments of brightness features to address the visibility problem. The proposed UICE-MIRNET restrict brightness and also works on the improvement of the colourfulness of underwater images. UICE-MIRNet consists of an Underwater Image-Colorfulness Enhancement Block (UI-CEB). This block enables the extraction of low-colourful areas from underwater images and performs colour correction without affecting contextual information. The primary characteristics of UICE-MIRNet are the extraction of multiple features using a convolutional stream, feature fusion to facilitate the flow of information, preservation of contextual information by discarding irrelevant features and increasing colourfulness through proper feature selection. Enhanced images are then trained using the YOLOv4 object detection model. The performance of the proposed UICE-MIRNet method is quantitatively evaluated using standard metrics such as UIQM, UCIQE, entropy, and PSNR. The proposed work is compared with many existing image enhancement and restoration techniques. Also, the performance of object detection is assessed using precision, recall, and mAP. Extensive experiments are conducted on two standard datasets, Brackish and Trash-ICRA19, to demonstrate the performance of the proposed work compared to existing methods. The results show that the proposed model outperforms many state-of-the-art techniques.

Cross-Modality Image Translation From Brain 18 F-FDG PET/CT Images to Fluid-Attenuated Inversion Recovery Images Using the CypixGAN Framework.

PET/CT and MRI can accurately diagnose dementia but are expensive and inconvenient for patients. Therefore, we aimed to generate synthetic fluid-attenuated inversion recovery (FLAIR) images from 18 F-FDG PET and CT images of the human brain using a generative adversarial network (GAN)-based deep learning framework called the CypixGAN, which combined the CycleGAN framework with the L1 loss function of the pix2pix. Data from 143 patients who underwent PET/CT and MRI were used for training (n = 79), validation (n = 20), and testing (n = 44) the deep learning frameworks. Synthetic FLAIR images were generated using the pix2pix, CycleGAN, and CypixGAN, and white matter hyperintensities (WMHs) were then segmented. The performance of CypixGAN was compared with that of the other frameworks. The CypixGAN outperformed the pix2pix and CycleGAN in generating synthetic FLAIR images with superior visual quality. Peak signal-to-noise ratio and structural similarity index (mean ± standard deviation) estimated using the CypixGAN (20.23 ± 1.31 and 0.80 ± 0.02, respectively) were significantly higher than those estimated using the pix2pix (19.35 ± 1.43 and 0.79 ± 0.02, respectively) and CycleGAN (18.74 ± 1.49 and 0.78 ± 0.02, respectively) ( P < 0.001). WMHs in synthetic FLAIR images generated using the CypixGAN closely resembled those in ground-truth images, as indicated by the low absolute percentage volume differences and high dice similarity coefficients. The CypixGAN generated high-quality FLAIR images owing to the preservation of spatial information despite using unpaired images. This framework may help improve diagnostic performance and cost-effectiveness of PET/CT when MRI scan is unavailable.

Quantum visual feature encoding revisited

Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantum machine learning. Investigating the root cause, we uncover that the existing quantum encoding design fails to ensure information preservation of the visual features after the encoding process, thus complicating the learning process of the quantum machine learning models. In particular, the problem, termed the “Quantum Information Gap” (QIG), leads to an information gap between classical and corresponding quantum features. We provide theoretical proof and practical examples with visualization for that found and underscore the significance of QIG, as it directly impacts the performance of quantum machine learning algorithms. To tackle this challenge, we introduce a simple but efficient new loss function named Quantum Information Preserving (QIP) to minimize this gap, resulting in enhanced performance of quantum machine learning algorithms. Extensive experiments validate the effectiveness of our approach, showcasing superior performance compared to current methodologies and consistently achieving state-of-the-art results in quantum modeling.

DEPICT-seq: Single-Cell Transcriptomic Analysis of Rare Cell Subsets Isolated via Nucleic Acid Cytometry.

The ability to dive deep into specific rare cell populations is critical for understanding tissue physiology and pathology across various biological domains. As single-cell RNA-seq flourishes, many newly discovered cell subtypes are defined by their transcriptomic markers. However, our ability to retrieve and analyze cells based on their nucleic acid markers remains underdeveloped. Here, we present Double Emulsion PCR-Initiated Cell sorting and Transcriptomic Sequencing (DEPICT-seq), a high-throughput droplet nucleic acid cytometry method that integrates batch cell fixation for cellular information preservation, double emulsion digital PCR-based cell sorting to target nucleic acid markers of interest, and in-depth full-length transcriptomic analyses at single-cell resolution. We utilize DEPICT-seq to isolate and characterize T cell receptor (TCR)-engineered T cells within a mixed population and also demonstrate a variation of the workflow by incorporating an RNase H-dependent PCR step to enrich full-length TCR sequences for paired single-cell TCR sequencing and transcriptomic profiling.

Varðveisla gagna í stjórnsýslunni

Mikið af upplýsingum verður til hjá stjórnsýslunni eða berst henni með einum eða öðrum hætti. Reglulega koma upp álitamál um varðveislu þessara upplýsinga og eyðingu þeirra. Í greininni er fjallað um þau ákvæði laga nr. 77/2014 um opinber skjalasöfn sem mæla fyrir um það hvaða skjöl stjórnvöldum ber að varðveita í því skyni að skila þeim á síðari stigum til opinberra skjalasafna og hver hafi eftirlit og yfirstjórn með þeirri framkvæmd. Helstu niðurstöður eru eftirfarandi: Í fyrsta lagi að öll skjöl sem hafa orðið til og tilheyra starfsemi afhendingarskyldra aðila falla undir gildissvið laganna óháð formi þeirra eða hvernig þau urðu til, nema sérlög leiði til annarrar niðurstöðu. Í öðru lagi að skjölin ber að varðveita nema fyrirmæli og heimildir Þjóðskjalasafns Íslands, hvað varðar skjalavörslu eða grisjun skjala, eða sérlög, leiði til annarrar niðurstöðu. Í þriðja lagi að það er Þjóðskjalasafn Íslands sem hefur eftirlit með framkvæmd laganna. Vegna þess hversu víðfeðm skilgreiningin er á hugtakinu skjal í lögum um opinber skjalasöfn þá hafa framangreindar niðurstöður mikla þýðingu fyrir störf stjórnsýslunnar.

Design of a Near-Field Synthetic Aperture Radar Imaging System Based on Improved RMA

Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts.

Regarding the question of usage of open-source evidence in criminal proceedings

The article is devoted to the study of the issue of using open-source evidence in criminal proceedings. Attention is drawn to the lack of a universally recognized definition of the term “open-source information”, a list of fundamental features of this type of information is provided, among which are: 1) accessibility of the information to the general public; 2) the author of the information may not be determined; 3) access to information can be provided both on a paid and free basis. The lack of an adequate volume of legislation aimed at regulating the procedure for usage of open-source evidence is emphasized. A study of the Berkeley Protocol, which was created jointly by the University of California, Berkeley School of Law and representatives of the United Nations, was carried out. In particular, the following is outlined: a list of the principles enshrined in the Berkeley Protocol, divided into professional, methodological and ethical, their content; the investigation process put forward in the Berkeley Protocol, that consists of the following stages: 1) online inquiries; 2) preliminary assessment; 3) collection of information; 4) preservation of information; 5) verification of information; 6) investigative analysis. Problems that may arise in the process of using open-source evidence are emphasized. The requirements of propriety, admissibility and reliability of open-source evidence are studied. The state of legal regulation of the issue of the usage of open-source evidence in national legislation is considered. The specific features of electronic (digital) evidence are singled out, which distinguish them from other types of evidence and determine the need to separate them in the criminal procedural legislation as a different type of evidence. The practice of national and international judicial institutions related to the use of open-source evidence is considered. The special role of this type of evidence for establishing the corpus delicti or circumstances of crimes against the foundations of national security, especially since the beginning of full-scale Russian aggression against Ukraine, is emphasized.