Recovery Of Information Research Articles

Compressed sensing for rapid tabletop X-ray absorption spectroscopy

X-ray Absorption Fine Structure (XAFS) spectroscopy is an analytical technique of chemical insight. Improvements to this technique are continuously carried out for reduction of the acquisition time and increasing the resolution, especially for studying operando processes. The accurate reconstruction of XAFS signals requires that the collected data are noise-free to extract elemental structure information in post-processing. A novel approach to rapid sampling (encoded measurement) is validated conceptually. The technique relies on sparse signal recovery, i.e. using compressed sensing to reconstruct under-sampled signals. The proof of concept is demonstrated by using a python code to reconstruct under-sampled XAFS spectra. A case study of transition metal samples and their oxides is considered for this purpose. The results demonstrate a robust recovery of chemical information from a 30% sampled signal for the near-edge region analysis and from a 45% sampled signal for extended-edge region analysis. The application of this procedure is important for demonstrating the feasibility of tabletop operation using laboratory X-ray sources which are not continuously tunable.

Quantitation of human mitochondrial DNA and whole mtGenomes sequencing of fingernail/hair shaft samples

Abstract The analysis of mitochondrial DNA (mtDNA) is suitable for fingernail/hair shaft samples in forensic science. A successful mtDNA forensic analysis depends on the quantity and quality of the mtDNA. The application of massively parallel sequence (MPS) techniques to the analysis of mtDNA has the potential to improve the recovery of genetic information from difficult forensic specimens and to increase the discrimination potential of mtDNA by capturing and comparing full mtGenomes. In this study, we constructed mtDNA-specific standard curves using real-time PCR. The 105-base pair target sequence facilitates the amplification of degraded DNA and is minimally homologous to nonhuman mtDNA. The results indicated that the assay enables the absolute quantification of mtDNA down to 10 copies of mtDNA and provides a dynamic range of seven orders of magnitude. Whole mtGenome sequencing experiments demonstrated that as few as 2000 mtDNA copies resulted in a successful full region amplification and sequencing. Moreover, the frequency of point heteroplasmy from one donor showed that hairs from the same donor have been found to differ within and among themselves and from other tissues, which could impact the interpretation of the results obtained in a forensic investigation.

Development of a novel five dye insertion/deletion (INDEL) panel for ancestry determination.

The use of genetic markers, specifically Short Tandem Repeats (STRs), has been a valuable tool for identifying persons of interest. However, the ability to analyze additional markers including Single Nucleotide Polymorphisms (SNPs) and Insertion/Deletion (INDELs) polymorphisms allows laboratories to explore other investigative leads. INDELs were chosen in this study because large panels can be differentiated by size, allowing them to be genotyped by capillary electrophoresis. Moreover, these markers do not produce stutter and are smaller in size than STRs, facilitating the recovery of genetic information from degraded samples. The INDEL Ancestry Informative Markers (AIMs) in this study were selected from the 1000 Genomes Project based on a fixation index (FST) greater than 0.50, high allele frequency divergence, and genetic distance. A total of 25 INDEL-AIMs were optimized and validated according to SWGDAM guidelines in a five-dye multiplex. To validate the panel, genotyping was performed on 155 unrelated individuals from four ancestral groups (Caucasian, African, Hispanic, and East Asian). Bayesian clustering and principal component analysis (PCA) were performed revealing clear separation among three groups, with some observed overlap within the Hispanic group. Additionally, the PCA results were compared against a training set of 793 samples from the 1000 Genomes Project, demonstrating consistent results. Validation studies showed the assay to be reproducible, tolerant to common inhibitors, robust with challenging casework type samples, and sensitive down to 125 pg. In conclusion, our results demonstrated the robustness and effectiveness of a 25 loci INDEL system for ancestry inference of four ancestries commonly found in the United States.

A quantum of information in black hole evaporation

Black holes evolve by evaporation of their event horizon. While this process is believed to be unitary, there is no consensus on the recovery of information in black hole entropy. A missing link is a quantum of information in black hole evaporation. Distinct from Hawking radiation, we identify evaporation in entangled pairs by P2 topology of the event horizon consistent with the Bekenstein–Hawking entropy in a uniformly spaced horizon area. It derives by continuation of P2 in Rindler spacetime prior to gravitational collapse, subject to a tight correlation of the fundamental frequency of quasi-normal-mode ringing in gravitational and electromagnetic radiation. Information extraction from entangled pairs by detecting one over the surface spanned by three faces of a large cube carries a quantum of information 2kBlog3 upon including measurement of spin.

Highly parallelizable path sampling with minimal rejections using asynchronous replica exchange and infinite swaps

Capturing rare yet pivotal events poses a significant challenge for molecular simulations. Path sampling provides a unique approach to tackle this issue without altering the potential energy landscape or dynamics, enabling recovery of both thermodynamic and kinetic information. However, despite its exponential acceleration compared to standard molecular dynamics, generating numerous trajectories can still require a long time. By harnessing our recent algorithmic innovations-particularly subtrajectory moves with high acceptance, coupled with asynchronous replica exchange featuring infinite swaps-we establish a highly parallelizable and rapidly converging path sampling protocol, compatible with diverse high-performance computing architectures. We demonstrate our approach on the liquid-vapor phase transition in superheated water, the unfolding of the chignolin protein, and water dissociation. The latter, performed at the ab initio level, achieves comparable statistical accuracy within days, in contrast to a previous study requiring over a year.

Correlating forensic data for enhanced network crime investigations: Techniques for packet sniffing, network forensics, and attack detection

<p class="MDPI18keywords">In today’s digitally saturated world, digital devices are frequently involved in criminal events as targets, mediums, or witnesses. Forensic investigations encompass the collection, recovery, analysis, and presentation of information stored on network devices, with specific relevance to network crimes. Such investigations often necessitate the use of diverse analysis tools and methods. This study introduces techniques that support digital investigators in correlating and presenting information derived from forensic data, with a primary focus on packet sniffing, network forensics, and attack detection. By leveraging these methodologies, investigators aim to achieve more valuable reconstructions of events or actions, resulting in enhanced case conclusions. The study emphasizes the importance of understanding how malware operates within the context of the Internet. It explores packet sniffing techniques to capture and analyze network data, enabling investigators to detect and trace the origins of malicious activities. Additionally, it delves into the realm of network forensics, proposing effective methods for gathering evidence from network devices and reconstructing digital events. Furthermore, the study covers the significance of attack detection in network crime investigations. It highlights techniques to identify and analyze attack patterns, facilitating the identification of perpetrators and their motivations. By correlating information obtained from forensic data, investigators can obtain comprehensive insights into the nature and impacts of network crimes. Overall, this study aims to arm digital investigators with the knowledge and tools necessary to navigate the complexities of packet sniffing, network forensics, and attack detection. By incorporating these techniques into their investigations, investigators can achieve more robust reconstructions of events, draw well-informed conclusions, and contribute to the successful resolution of network crime cases.</p>

Information retrieval from Hawking radiation in the non-isometric model of black hole interior: Theory and quantum simulation

The nonisometric holographic model of the black hole interior stands out as a potential resolution of the long-standing black hole information puzzle since it remedies the friction between the effective calculation and the microscopic description. In this study, combining the final-state projection model, the nonisometric model of black hole interior, and Hayden-Preskill thought experiment, we investigate the information recovery from decoding Hawking radiation and demonstrate the emergence of the Page time in this setup. We incorporate the effective modes into the scrambling inside the horizon, which are usually disregarded in Hayden-Preskill protocols, and show that the Page time can be identified as the transition of information transmission channels from the EPR projection to the local projections. This offers a new perspective on the Page time. We compute the decoupling condition under which retrieving information is feasible and show that this model computes the black hole entropy consistent with the quantum extremal surface calculation. Assuming the full knowledge of the dynamics of the black hole interior, we show how Yoshida-Kitaev decoding strategy can be employed in the modified Hayden-Preskill protocol. Furthermore, we perform experimental tests of both probabilistic and Grover’s search decoding strategies on the seven-qubit IBM quantum processors to validate our analytical findings and confirm the feasibility of retrieving information in the nonisometric model. This study would stimulate more interest to explore black hole information problem on the quantum processors. Published by the American Physical Society 2024

FIVE-YEAR MORTALITY FOLLOWING TRAUMATIC CENTRAL CORD SYNDROME IN WALES

AimsTraumatic central cord syndrome (CCS) typically follows a hyperextension injury and results in a motor impairment affecting the upper limbs more than the lower limbs, with occasional sensory impairment and urinary retention seen. Current evidence on mortality and long-term outcomes is limited. The primary aim of this study is to assess the five-year mortality of CCS, and to determine any difference in mortality between management groups or age.Patients and MethodsPatients ≥18 years with traumatic CCS between January 2012 and December 2017 in Wales were identified. Patient demographics and injury, management and outcome data was collected. Statistical analysis was performed to assess mortality and between group differences.Results65 patients were identified (66.2% male, mean age 63.9 years). At five-years follow-up, 32.3% (n=21) of CCS patients were dead. 6 (9.2%) patients had died within 31 days of their injury. 69.2% (n=45) of patients were managed conservatively and there was no significant difference (p=0.062) in age between conservatively and surgically managed patients. Kaplan-Meier analysis revealed no significant difference in mortality between patients managed conservatively compared to those managed surgically (log rank test, p=0.819). However, there was a significant difference (p=0.001) in mortality between the different age groups (<50 years vs 50–70 years vs >70 years). At five-years follow up, 55.6% of the patient group aged >70 years at time of injury were dead. Respiratory failure was the most common cause of death (n=9, 42.9%).ConclusionAlmost one third of patients with traumatic CCS in Wales were dead at five years following their injury. Management type did not significantly affect mortality, however age at time of injury did. Further work assessing the long-term functional outcomes of surviving patients is needed, to allow more reliable prognostic information and functional recovery predictions to be given.

Application of Uterine Artery Embolization in Patients With Placenta Accreta Spectrum After Abdominal Aortic Balloon Occlusion.

To evaluate the application of different uterine artery embolization procedures under balloon occlusion of the abdominal aorta in patients with Placenta Accreta Spectrum (PAS) undergoing cesarean section. A retrospective analysis was performed on clinical data from 72 patients who underwent uterine artery embolization for hemostasis during cesarean section with PAS. The patients were divided into two groups according to the embolization method used during surgery: group A (n = 43) underwent uterine artery embolization by withdrawing the balloon and inserting a Cobra catheter into the uterine artery for embolization, while group B (n = 29) underwent uterine artery embolization with a Cobra catheter inserted via contralateral puncture of the femoral artery and balloon occlusion. General information, surgical data, and postoperative recovery were compared between the 2 groups. The bleeding and transfusion volumes were lower in group B than in group A and the differences between the 2 groups were statistically significant. There were no significant differences in surgical duration, number of embolized vessels, length of hospital stay, postoperative complications, or menstrual recovery between the 2 groups. For patients with PAS undergoing cesarean section, uterine artery embolization for hemostasis is preferably performed by inserting a Cobra catheter via contralateral puncture of the femoral artery under abdominal aortic balloon occlusion.

FPGA realization of an image encryption system using the DCSK-CDMA technique

This paper describes a four-wing chaotic oscillator-based DCSK-CDMA modulation technique for image encryption and decryption. The system consists of a transmission module for the encrypted and modulated color matrices, and a reception module for the decrypted and demodulated original data. Among our main contributions is the integration for the first time in the state of the art of DCSK chaotic modulation techniques with the CDMA communication scheme, as well as the implementation of our DCSK and CDMA algorithms in VHDL language for Xilinx FPGA cards. The DCSK-CDMA technique enables demodulation and decryption without any loss in the original data. Other contributions arising from this investigation are the encryption process implemented using DCSK-CDMA techniques based on a four-wing chaotic oscillator and the realization on the FPGA of a chaos-based communications system for the secure transmission of images of grayscale and RGB formats using DCSK-CDMA techniques. The system architecture in this work was designed using fixed-point binary arithmetic, with the application of the 4th order Runge–Kutta numerical method for the four-wing chaotic oscillator. The analysis of the correlation coefficients between the original and encrypted information indicates the values of 5.367×10−4 and −2.205×10−7 for grayscale and RGB images, respectively, with a full recovery of the original information. The results of simulations in MATLAB/Simulink coincide with the implementation of the complete system in VHDL on the Xilinx Artix-7 AC701 board.

Decoupling-style monaural speech enhancement with a triple-branch cross-domain fusion network

Monaural speech enhancement aims to remove background noise from noisy speech signals captured by a single microphone. In recent years, several cross-domain monaural speech enhancement methods are developed to leverage both waveform and harmonic information. However, these methods fall short in fully capturing the dependencies between the time domain and time-frequency (T-F) domain, as well as in harnessing the benefits of the target decoupling strategy. This paper proposes a causal encoder-decoder-based Triple-branch Cross-domain Fusion Network (TCF-Net), which effectively processes speeches by leveraging both time domain and T-F domain features. The proposed approach enables the parallel recovery of magnitude and phase information to alleviate the compensation problem between them. TCF-Net forms a triple-branch network by collaboratively reconstructing the enhanced spectrum with a complex spectrum branch and a magnitude spectrum branch, while incorporating time-domain information with a waveform compensation branch. To fully leverage the information from three domains, Triple-domain Fusion Modules (TFMs) are inserted in each intermediate layer of the model to extract and merge the information from two T-F domain branches and one time domain branch. The TFMs generate masks to progressively compensate for the magnitude of the two T-F domain branches and promote information interaction, further restoring the magnitude of the clean speech. Experimental results demonstrate that TCF-Net outperforms state-of-the-art (SOTA) cross-domain methods and target decoupling methods under causal configuration in all evaluation metrics, which validates the importance of the proposed cross-domain information fusion strategy and target decoupling strategy.

Multi-Attention Learning and Exposure Guidance Toward Ghost-Free High Dynamic Range Light Field Imaging.

Due to sensor limitations, the light field (LF) images captured by the LF camera suffer from low dynamic range and are prone to poor exposure. To solve this problem, combining multi-exposure technology with LF camera imaging can achieve high dynamic range (HDR) LF imaging. However, for dynamic scenes, this approach tends to produce disturbing ghosting artifacts and destroy the parallax structure of the generated results. To this end, this paper proposes a novel ghost-free HDR LF imaging method using multi-attention learning and exposure guidance. Specifically, the proposed method first designs a multi-scale cross-attention module to achieve efficient multi-exposure LF feature alignment. After that, a dual self-attention-driven Transformer block is constructed to excavate the geometric information of LF and fuse the aligned LF features. In particular, exposure masks derived from middle-exposure are introduced in the feature fusion to guide the network to focus on information recovery in low- and high-brightness regions. Besides, a local compensation module is integrated to cope with local alignment errors and refine details. Finally, a multi-objective reconstruction strategy combined with exposure masks is employed to restore high-quality HDR LF images. Extensive experimental results on the benchmark dataset show that the proposed method generates HDR LF results with high spatial-angular quality consistency and outperforms the state-of-the-art methods in quantitative and qualitative comparisons. Furthermore, the proposed method can enhance the performance of existing LF applications, such as depth estimation.

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Abstract This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.

HSGAN: Hyperspectral Reconstruction From RGB Images With Generative Adversarial Network.

Hyperspectral (HS) reconstruction from RGB images denotes the recovery of whole-scene HS information, which has attracted much attention recently. State-of-the-art approaches often adopt convolutional neural networks to learn the mapping for HS reconstruction from RGB images. However, they often do not achieve high HS reconstruction performance across different scenes consistently. In addition, their performance in recovering HS images from clean and real-world noisy RGB images is not consistent. To improve the HS reconstruction accuracy and robustness across different scenes and from different input images, we present an effective HSGAN framework with a two-stage adversarial training strategy. The generator is a four-level top-down architecture that extracts and combines features on multiple scales. To generalize well to real-world noisy images, we further propose a spatial-spectral attention block (SSAB) to learn both spatial-wise and channel-wise relations. We conduct the HS reconstruction experiments from both clean and real-world noisy RGB images on five well-known HS datasets. The results demonstrate that HSGAN achieves superior performance to existing methods. Please visit https://github.com/zhaoyuzhi/HSGAN to try our codes.

Unsupervised Vision‐Based Structural Anomaly Detection and Localization with Reverse Knowledge Distillation

Most of vision‐based methods for structural damage detection rely on supervised learning, requiring a substantial number of labeled images for model training, which is labor‐intensive and time‐consuming. To address these challenges, this study introduces a vision‐based structural anomaly detection and localization approach using unsupervised learning and reverse knowledge distillation. The proposed model incorporates a teacher model, a student model, and a trainable one‐class bottleneck embedding module. The asymmetrical architecture of the teacher and student models forms an encoder‐decoder structure for parameter transfer and feature extraction. The student network receives a specific embedding from the teacher network as input and target, facilitating the recovery of multiscale information from the teacher. Training images only contain the undamaged structures, and the teacher model, a pretrained model, instructs the student model to remember their undamaged features to detect and localize damages in unseen testing images. Through experiments, including a comparison among five candidate backbones for pretrained teacher models based on the residual network and testing across various structural damage types, the optimal model is identified, demonstrating good performance in both anomaly detection and localization. Furthermore, the model’s generalization performance is thoroughly validated, confirming its efficacy across diverse scenarios.

Multi‐UAV Cooperative Search for Moving Targets With Impaired Communication Using Improved Gray Wolf Optimizer

The study investigates the problem of collaborative target search by multiple unmanned aerial vehicles (UAVs) under the condition of communication information corruption. The problem involves the influence of target movement and environmental obstacles on the update of the UAV’s local target probability map (TPM), as well as communication information corruption among UAVs due to packet loss and resulting local information inconsistency. The objective is to efficiently plan paths for the UAV swarm to discover more targets. To address the above issues, this paper proposes the following methods. Firstly, an information diffusion model is introduced to simulate the TPM changes caused by target movement and the discovery of obstacles based on a two‐dimensional Gaussian distribution, thereby correcting the real‐time target information held by UAVs. Secondly, a radial basis function neural network (RBFNN) is employed to recover the corrupted information, and a weighted averaging method is used to fuse the information from multiple UAVs, achieving consensus among the UAVs. Lastly, the original gray wolf optimizer (GWO) is enhanced by incorporating strategies such as Levy flight and the nonlinear convergence factor. Subsequently, the improved GWO is integrated with the model predictive control (MPC) for UAV trajectory planning, enabling a more efficient search for optimal paths. Experimental results demonstrate that the proposed information recovery method outperforms the compared methods by 24.7% in terms of performance. The proposed comprehensive search approach shows higher efficiency in searching for more targets within a specified time, and it also exhibits advantages in terms of area coverage and other aspects.

AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery

AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery

FDR-TransUNet: A novel encoder-decoder architecture with vision transformer for improved medical image segmentation

The U-shaped and Transformer architectures have achieved exceptional performance in medical image segmentation and natural language processing, respectively. Their combination has also led to remarkable results but still suffers from enormous loss of image features during downsampling and the difficulty of recovering spatial information during upsampling. In this paper, we propose a novel encoder-decoder architecture for medical image segmentation, which has a flexibly adjustable hybrid encoder and two expanding paths decoder. The hybrid encoder incorporates the feature double reuse (FDR) block and the encoder of Vision Transformer (ViT), which can extract local and global pixel localization information, and alleviate image feature loss effectively. Meanwhile, we retain the original class-token sequence in the Vision Transformer and develop an additional corresponding expanding path. The class-token sequence and abstract image features are leveraged by two independent expanding paths with the deep-supervision strategy, which can better recover the image spatial information and accelerate model convergence. To further mitigate the feature loss and improve spatial information recovery, we introduce successive residual connections throughout the entire network. We evaluated our model on the COVID-19 lung segmentation and the infection area segmentation tasks. The mIoU index increased by 1.5 points and 3.9 points compared to other models which demonstrates a performance improvement.

A Y-shaped network based single-shot absolute phase recovery method for fringe projection profilometry

Fringe projection profilometry (FPP) is a widely used non-contact 3D measurement method. Though maturing in the last decade, it remains a significant challenge when facing the phase unwrapping of measured object surfaces in a single-shot measurement setting. With the rapid development of deep learning techniques, the adoption of a data-driven approach is gaining popularity in the field of optical metrology. This study proposes a new absolute phase recovery method based on the devised single-stage deep learning network. The aim is to ensure high-quality absolute phase recovery from a single-shot fringe projection measurement. Unlike most existing approaches, where the numerators and denominators of the wrapped phases and the fringe orders are predicted in various stages, the proposed method acquires the wrapped phases and the corresponding fringe orders within a single network, i.e. it can predict both wrapped phases and the corresponding fringe orders directly and simultaneously from the single fringe pattern projected in the single-shot mode based on a unified Y-shaped network. Experiments on benchmark datasets and models have demonstrated the effectiveness and efficiency of the technique, especially in terms of high-quality recovery of absolute phase information by using the lightweight single-stage network, and enabling the FPP-based phase 3D measurements in an online manner.

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed.