Information Streams Research Articles

Multisensory mechanisms of gait and balance in Parkinson's disease: an integrative review.

Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population. Posture and gait control does not happen automatically, as previously believed, but rather requires continuous involvement of central nervous mechanisms. To effectively exert control over the body, the brain must integrate multiple streams of sensory information, including visual, vestibular, and somatosensory signals. The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work. Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults. Insufficient emphasis, however, has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance. In the present work, we review the contributions of somatosensory, visual, and vestibular modalities, along with their multisensory intersections to gait and balance in older adults and patients with Parkinson's disease. We also review evidence of vestibular contributions to multisensory temporal binding windows, previously shown to be highly pertinent to fall risk in older adults. Lastly, we relate multisensory vestibular mechanisms to potential neural substrates, both at the level of neurobiology (concerning positron emission tomography imaging) and at the level of electrophysiology (concerning electroencephalography). We hope that this integrative review, drawing influence across multiple subdisciplines of neuroscience, paves the way for novel research directions and therapeutic neuromodulatory approaches, to improve the lives of older adults and patients with neurodegenerative diseases.

A sharing rule for multi-period interest-sensitive insurance contracts

As sales of interest-sensitive products grow in insurance markets, determining a sharing rule for allocating investment returns between a policyholder and an insurer is crucial. This paper discusses a theoretical sharing rule for a multi-period contract, reflecting that the insurer’s investment efforts are unobservable and that a stream of information inferring the quality of the efforts exists. Our sharing rule is developed by selecting multi-period excess returns as available information and maximizing the expected utility of the policyholder. The empirical analysis shows that the effect of multi-period excess returns on insurers’ sharing patterns is consistent with the theoretical findings.

Image Contour Detection Based on Visual Pathway Information Transfer Mechanism

Based on the coding mechanism and interactive features of visual information in the visual pathway, a new method of image contour detection is proposed. Firstly, simulating the visual adaptation characteristics of retinal ganglion cells, an adaptation & sensitization regulation model (ASR) based on the adaptation-sensitization characteristics is proposed, which introduces a sinusoidal function curve modulated by amplitude, frequency and initial phase to dynamically adjusted color channel response information and enhance the response of color edges. Secondly, the color antagonism characteristic is introduced to process the color edge responses, and the obtained primary contour responses is fed forward to the dorsal pathway across regions. Then, the coding characteristics of the “angle” information in the V2 region are simulated, and a double receptive fields model (DRFM) is constructed to compensate for the missing detailed contours in the generation of primary contour responses. Finally, a new double stream information fusion model (DSIF) is proposed, which simulates the dorsal overall contour information flow by the across-region response weighted fusion mechanism, and introduces the multi-directional fretting to simulate the fine-tuning characteristics of ventral detail features simultaneously, extracting the significant contours by weighted fusion of dorsal and ventral information streams. In this paper, the natural images in BSDS500 and NYUD datasets are used as experimental data, and the average optimal F-score of the proposed method is 0.72 and 0.69, respectively. The results show that the proposed method has better results in texture suppression and significant contour extraction than the comparison method.

Retracted: Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation

Retracted: Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation

EESSO: Exploiting Extreme and Smooth Signals via Omni-frequency learning for Text-based Person Retrieval

Most of the existing methods manage to tackle the problem of text-based person retrieval from the spatial-wise perspective. In this paper, we manage to address the problem of this task from a novel perspective, namely, the frequency-wise perspective. To this end, we propose to Exploit Extreme and Smooth Signals via Omni-frequency learning (EESSO) through a jointly optimized multi-stream architecture. It consists of a Spatial Information Stream (SIS), an Extreme Signal Stream (ESS) and a Smooth Signal Stream (SSS). EESSO aims to excavate the complementary effect between spatial-wise and frequency-wise features, so as to achieve a superior performance. A novel Uncertainty-Guided Mutual Learning Mechanism (UG-MLM) is utilized during training, which not only enables the three streams to communicate with and learn from each other, but also models the data-related heteroscedastic uncertainty as a weight for knowledge transference, and hence enables each stream to adaptively allocate knowledge from the other two streams. A large number of experiments are carried out on the widely-used CUHK-PEDES, RSTPReid and ICFG-PEDES datasets to verify the effectiveness of EESSO. Through the experimental results, it may not be hard to find that EESSO has achieved the state-of-the-art performance in supervised, weakly supervised and cross-domain text-based person retrieval settings.

An Object-Oriented Approach to the Modular Design of Mechatronic Systems

The design of modern mechatronic systems involves multidisciplinary and high-level integration, creating challenging and complex tasks for industrial designers and engineers. Their development requires intricate interaction between heterogeneous subsystems, and necessitates coordinated trans-sectoral cooperation between mechanical, electronic, and software engineers. Although efforts have been made to create efficient methods to guide the design of mechatronic systems, no existing solutions solve the complexity of design problems experienced during their design. This article, therefore, proposes a structured step-by-step approach to guide the design of mechatronic systems using an object-oriented methodology. Three information streams are employed that flow through the logical layers of a mechatronic system to track requirements and define modular specifications during the design process. The proposed approach benefits those developing mechatronic systems by improving collaboration, minimizing errors, reducing chances of design failure, and helping meet the desired functionality and goal of multidisciplinary integration. To evaluate the proposed approach and demonstrate the practicability of the systematic framework, we present a case study of a library book returning system. Our results highlight the capability of the proposed approach to guarantee top–down consistency and traceability of derived specifications among logical layers and contribute to the successful integration of requirements during the design process of mechatronic systems.

Bio-Intelligent Machine Tool: Vision and Steps Towards Realisation

Artificial intelligence enables machines to increase their autonomy. A concept of a bio-intelligent manufacturing machine has been developed. This is characterised by a self-learning expert system and a sensor strategy which are intensively integrated with each other. The machine tool control is at any time completely aware of the process performance, the environmental conditions and the machine status. It exploits further information streams from human operators and other machines in a federated learning approach and is connected to the MES system of the company. The expert system learns from human operators by mere commanding the operation. It is capable to generate new process parameters and deal with incomplete process data by adding the missing information.

The Interactions of Temporal and Sensory Representations in the Basal Ganglia.

In rodents and primates, interval estimation has been associated with a complex network of cortical and subcortical structures where the dorsal striatum plays a paramount role. Diverse evidence ranging from individual neurons to population activity has demonstrated that this area hosts temporal-related neural representations that may be instrumental for the perception and production of time intervals. However, little is known about how temporal representations interact with other well-known striatal representations, such as kinematic parameters of movements or somatosensory representations. An attractive hypothesis suggests that somatosensory representations may serve as the scaffold for complex representations such as elapsed time. Alternatively, these representations may coexist as independent streams of information that could be integrated into downstream nuclei, such as the substantia nigra or the globus pallidus. In this review, we will revise the available information suggesting an instrumental role of sensory representations in the construction of temporal representations at population and single-neuron levels throughout the basal ganglia.

SOFTWARE IMPLEMENTATION OF ALGORITHMS FOR SELECTING THE OUTLINES OF OBJECTS IMAGES IN INTELLECTUAL VIDEO SURVEILLANCE SYSTEMS

The rapid advancement in digital image acquisition and transmission technologies poses challenges in processing vast volumes of video information streams. Images serve as crucial sources of decision-making information across a wide range of tasks. Key objectives addressed by intelligent video surveillance systems include object identification, trajectory determination, object speed measurement, and real-time detection of critical events for securing premises. Among the primary operations in intelligent video surveillance systems is the extraction of object contours from images, as contours contain essential information for object recognition based on their shape. This approach excludes internal image points, significantly reducing the amount of processed information and enabling real-time image analysis. Contour analysis encompasses methods for selecting, describing, and processing image contours to describe, store, compare, and search for objects based on their external contours. This methodology effectively addresses fundamental challenges in pattern recognition, such as transferring, rotating, and scaling object images. The paper discusses prominent contour analysis methods and presents software implementations of algorithms for extracting object outlines in intelligent video surveillance systems. A promising future direction involves synthesizing algorithms that extract object contours using a two-scale statistical image model.

Real-Time data integration in diagnostic devices for predictive modeling of infectious disease outbreaks

The integration of real-time data in diagnostic devices is transforming the landscape of infectious disease management by enabling predictive modeling of outbreaks. Traditional diagnostic approaches, often constrained by delayed data processing and analysis, fall short in rapidly evolving scenarios like infectious disease outbreaks. Recent advancements in real-time data integration are addressing these limitations, offering significant improvements in predictive accuracy and timely response. Real-time data integration leverages continuous streams of information from various sources, including diagnostic devices, electronic health records, and environmental sensors, to provide a comprehensive view of disease dynamics. By incorporating this data into predictive models, healthcare professionals can better anticipate and manage outbreaks. For instance, integrating data from wearable sensors and mobile health applications allows for continuous monitoring of health indicators, which can signal early warning signs of disease spread. This approach enhances the ability to detect patterns and anomalies that might indicate an impending outbreak. Moreover, predictive modeling powered by real-time data facilitates proactive interventions. By analyzing trends and correlations in real-time, these models can forecast potential outbreak scenarios, optimize resource allocation, and guide public health responses. For example, predictive models can forecast the spread of diseases based on current infection rates and social behavior patterns, enabling targeted interventions and timely public health advisories. Despite the benefits, the integration of real-time data into diagnostic devices presents several challenges. Ensuring data accuracy and consistency, maintaining privacy and security, and managing the vast amounts of data generated are critical issues that need addressing. Advanced data analytics and machine learning techniques are essential for processing and interpreting this data effectively. Additionally, interoperability among various data sources and systems is crucial for a seamless integration process. In summary, the integration of real-time data in diagnostic devices offers a powerful tool for predictive modeling of infectious disease outbreaks. It enhances the accuracy of forecasts, improves response strategies, and ultimately contributes to more effective management of public health crises. As technology advances, addressing the associated challenges will be key to maximizing the potential of this innovative approach. Keywords: Real-Time, Data Integration, Diagnostic Devices, Predictive Modeling, Infectious Disease Outbreaks.

The information culture of teenagers in the context of modern education paradigm

The growing information streams and expanding information space have made information easy accessible while the need for its filtration, critical reevaluation, and clear-cut assessment criteria emerged. The younger generation has to master skills of retrieving, analyzing and using reliable information to support their lives, career and social success. Being skillful Internet users, the teenagers yet demonstrate the low level of information culture. However, the requirement to educate information culture is formulated in The Federal State Educational Standard of Comprehensive Secondary Education (FGOS) in the disciplines “Mathematics”, “Informatics” and “Literature”. The results of the regional stage of the All-Russian Olympiad of School Students – the republican Olympiad in the personal information culture (Republic of Buryatia) – have demonstrated non-conformity of teenagers’ performance with the FGOS standard. No similar Olympiads are held in other RF regions, which makes the study particularly valuable. The authors emphasize that the Olympiad in this subject area has to be implemented in other regions. They applied several research methods to assess information literacy, namely empirical (observation, comparison) and theoretical (analysis, generalization). The analysis is based on students’ answers. The newness of the study lies in characterizing information culture of Buryat schoolchildren and specifying major challenges, substantiating and extending obtained data, and suggesting new vectors of information culture regional studies.

Scan-to-HBIM: automated transformation of point clouds into 3D BIM models for the digitization and preservation of historic buildings

Three-dimensional digital models of historical buildings must seamlessly integrate a wide array of data from diverse sources, including point clouds, alphanumeric information, 2D and 3D representations, BIM, GIS, images, sensor data, and real-time information streams, among other heterogeneous resources. Centralizing and presenting this multifaceted information cohesively is fundamental for fostering a comprehensive understanding and analysis of these historical monuments. In this context, the article introduces an automated process (Scan-to-HBIM) tailored specifically to transform point clouds into threedimensional models within BIM environments. This innovative approach not only enhances the accuracy in representing historic buildings but also significantly amplifies the level of detail (LoD), enabling a more nuanced representation of architectural structures. The article delves into the Scan-to-HBIM process, elucidating its relevance in the broader context of digitization and preservation of historic buildings, with a particular focus on its application to a sample point cloud. Furthermore, it underscores the pivotal advantages of this approach, emphasizing its proficiency in managing diverse datasets, elevating the level of detail, and exploring practical applications in both BIM and GIS realms.

Orchestration of saccadic eye-movements by brain rhythms in macaque Frontal Eye Field

Visual perception has been suggested to operate on temporal ‘chunks’ of sensory input, rather than on a continuous stream of visual information. Saccadic eye movements impose a natural rhythm on the sensory input, as periods of steady fixation between these rapid eye movements provide distinct temporal segments of information. Ideally, the timing of saccades should be precisely locked to the brain’s rhythms of information processing. Here, we investigated such locking of saccades to rhythmic neural activity in rhesus monkeys performing a visual foraging task. We found that saccades are phase-locked to local field potential oscillations (especially, 9–22 Hz) in the Frontal Eye Field, with the phase of oscillations predictive of the saccade onset as early as 100 ms prior to these movements. Our data also indicate a functional role of this phase-locking in determining the direction of saccades. These findings show a tight—and likely important—link between oscillatory brain activity and rhythmic behavior that imposes a rhythmic temporal structure on sensory input, such as saccadic eye movements.

Benchmarking Perception to Streaming Inputs in Vision-Centric Autonomous Driving

In recent years, vision-centric perception has played a crucial role in autonomous driving tasks, encompassing functions such as 3D detection, map construction, and motion forecasting. However, the deployment of vision-centric approaches in practical scenarios is hindered by substantial latency, often deviating significantly from the outcomes achieved through offline training. This disparity arises from the fact that conventional benchmarks for autonomous driving perception predominantly conduct offline evaluations, thereby largely overlooking the latency concerns prevalent in real-world deployment. Although a few benchmarks have been proposed to address this limitation by introducing effective evaluation methods for online perception, they do not adequately consider the intricacies introduced by the complexity of input information streams. To address this gap, we propose the Autonomous driving Streaming I/O (ASIO) benchmark, aiming to assess the streaming input characteristics and online performance of vision-centric perception in autonomous driving. To facilitate this evaluation across diverse streaming inputs, we initially establish a dataset based on the CARLA Leaderboard. In alignment with real-world deployment considerations, we further develop evaluation metrics based on information complexity specifically tailored for streaming inputs and streaming performance. Experimental results indicate significant variations in model performance and ranking under different major camera deployments, underscoring the necessity of thoroughly accounting for the influences of model latency and streaming input characteristics during real-world deployment. To enhance streaming performance consistently across distinct streaming input features, we introduce a backbone switcher based on the identified streaming input characteristics. Experimental validation demonstrates its efficacy in perpetually improving streaming performance across varying streaming input features.

Fusion of dual modalities of non-invasive sensory feedback for object profiling with prosthetic hands.

Either non-invasive somatotopic or substitute sensory feedback is capable of conveying a single modality of sensory information from prosthetic hands to amputees. However, the neurocognitive ability of amputees to integrate multi-modality sensory information for functional discrimination is unclear. The purpose of this study was to assess the fusion of non-invasive somatotopic tactile and substitute aperture feedbacks for profile perception of multiple physical features during grasping objects. Two left transradial amputees with somatotopic evoked tactile sensation (ETS) of five fingers participated in the study. The tactile information of prosthetic hand was provided to amputees by the ETS feedback elicited on the stump projected finger map. Hand aperture information was conveyed to amputees with substitute electrotactile stimulation on the forearm or upper arm. Two types of sensory feedback were integrated to a commercial prosthetic hand. The efficacy of somatotopic ETS feedback on object length identification task was assessed with or without substitute aperture stimulation. The object size identification task was utilized to assess how ETS stimulation at the stump may affect aperture perception with stimulation on the ipsilateral upper arm or forearm. Finally, the task of identifying combined length and size was conducted to evaluate the ability of amputees to integrate the dual modalities of sensory feedback for perceiving profile features. The study revealed that amputee subjects can effectively integrate the ETS feedback with electrotactile substitutive feedback for object profile discrimination. Specifically, ETS was robust to provide object length information with electrotactile stimulation at either the forearm or upper arm. However, electrotactile stimulation at the upper arm for aperture perception was less susceptible to the interference of ETS stimulation than at the forearm. Amputee subjects are able to combine somatotopic ETS and aperture feedbacks for identifying multi-dimensional features in object profiling. The two sensory streams of information can be fused effectively without mutual interference for functional discrimination.

Deep learning enabled blockchain based electronic heathcare data attack detection for smart health systems

The idea of networked personal medical devices is a component of contemporary Smart Health Systems (SHS). These gadgets offer remote observing and the exchange of wellbeing information, which enormously further develop the patient's personal satisfaction while at the same time reducing treatment expenses for both the patient and the medical care suppliers (telemedicine). For individuals' wellbeing, a cutting edge individual wellbeing record framework is fundamental. Here, there are still difficulties with information mix from different EHRs, information interoperability, and guaranteeing that admittance to information is totally under the power of the patient. Some security issues are caused by the Network. To settle these issues, we propose a novel profound learning-based framework that circuits state of the art decentralized innovations like IPFS and blockchain with wellbeing information interoperability principles and advances like FHIR's APIs. In this review, we show that correspondence between private clinical gadgets is as a matter of fact powerless to different cyber attacks. We show how an outer assailant could involve man-in-the-center, replay, bogus information infusion, and refusal of-administration assaults to block delicate wellbeing information stream by capturing the correspondence of the individual clinical gadget. We likewise suggest an Interruption Recognition Framework (IDS), GAN, to additional screen traffic on private clinical gear and spot attacks against them. Our extensive investigation shows that GAN, with an F1-score of 98 % and an accuracy of 98.7 %, can successfully and accurately recognise numerous assaults on personal medical equipment.

Visual experience induces 4–8 Hz synchrony between V1 and higher-order visual areas

Visual perceptual experience induces persistent 4-8Hz oscillations in the mouse primary visual cortex (V1), encoding visual familiarity. Recent studies suggest that higher-order visual areas (HVAs) are functionally specialized and segregated into information streams processing distinct visual features. However, whether visual memories are processed and stored within the distinct streams is not understood. We report here that V1 and lateromedial (LM), but not V1 and anterolateral, become more phase synchronized in 4-8Hz after the entrainment of visual stimulus that maximally induces responses in LM. Directed information analysis reveals changes in the top-down functional connectivity between V1 and HVAs. Optogenetic inactivation of LM reduces post-stimulus oscillation peaks in V1 and impairs visual discrimination behavior. Our results demonstrate that 4-8Hz familiarity-evoked oscillations are specific for the distinct visual features and are present in the corresponding HVAs, where they may be used for the inter-areal communication with V1 during memory-related behaviors.

Retrieval of leaf-level fluorescence quantum efficiency and NPQ-related xanthophyll absorption through spectral unmixing strategies for future VIS-NIR imaging spectroscopy

Current and future vegetation imaging spectroscopy satellites will bring a new data stream of information, of high scientific value to refine existing remote sensing products, and develop new ones. The sensors on board ESA's Fluorescence Explorer (FLEX) will cover the entire 500–780 nm range, designed to track the photosynthetic energy partitioning based on the key pigment players in the light reactions. To quantify the actual photosynthetic efficiency unambiguously, the dynamic pigment absorption behavior is crucial to complement the fluorescence information. An important role is given by the xanthophylls, regulating the non-photosynthetic quenching (NPQ) behavior which affects the 500–600 nm range. Therefore, this work focused on the development of a non-negative least squares (NNLS) spectral unmixing algorithm for reflectance (500–780 nm) retrieving the effective absorbance of individual pigments, i.e., Chlorophyll (Chl) a and b, beta-Carotene and xanthophylls. The NNLS fitting was applied to fit the total effective absorbance at leaf level, linearly composed of pigment and background absorption coefficient spectra. The model succeeded to obtain spectral fitting errors generally below 20% across the 500–780 nm range. Further, we focused on the further use of the effective absorbance by Chlorophyll a to calculate the absorbed photosynthetically active radiation or APAR Chl a. This product was combined with the total emitted fluorescence flux (670–780 nm), expressed in photon flux units, to obtain the fluorescence quantum efficiency (FQE), capturing the stress-related fluorescence quenching in the light reactions. Finally, we applied the leaf-based algorithm to foreseen FLEX image products simulated by the FLEX End-to-End Scene Generator. We were able to retrieve APAR Chl a with a RMSE of 85.5 μmol m−2 s−1 (NNLS with additional upper fitting constraints) and 158.2 μmol m−2 s−1 (regular NNLS), and FQE retrievals could be obtained with an R2 of 0.93 and 0.90, respectively. While the subtle xanthophyll absorption could be meaningfully fitted at the leaf scale, further improvements to the algorithms and an understanding of the physiological mechanisms are needed to deal with the complexity at larger scales. Given several challenges to be overcome, the proposed bottom-up strategy using specific pigment absorbance unmixing for (imaging) spectroscopy demonstrates the ongoing developments to complement the fluorescence product, with the aim to provide an unambiguous estimate on the actual carbon sequestration of vegetation.

Treatment and outcomes in breast cancer patients: A cross section study from the EUSOMA breast centre network

IntroductionThe present study was designed to describe tumour features and treatments for patients with breast cancer. It also aimed at assessing the risk of distant metastases in relation to biological profiles, disease stages and treatment. MethodsData were analysed from 81,882 patients in the EUSOMA database (disease stages at diagnosis 0-IV; median age 61 years; range 20–100 years). All patients were treated between January 2016 and December 2021 in 53 Breast Centres within the EUSOMA certification process in 13 European countries. Cases were classified as HR+ /HER2-, HR+ /HER2 + , HR-/HER2 + or HR-/HER2- and data were analysed accordingly. ResultsUnivariable and multivariable analyses for distant metastases were conducted on a subset of 38,119 cases with information on whether or not they had developed them. Potential determinants included sub-group type, Ki67 value, disease stage, adjuvant systemic therapies and post-operative radiation therapy. In multivariable analysis, the HR-/HER2 + and HR-/HER2- sub-groups were associated with a higher risk of distant metastases than HR+ /HER2–. Ki67 > 20 % and advanced stage disease also carried a high risk. Radiation therapy emerged as a protective factor against distant metastases. ConclusionsPresent results show a large patient database offers an information stream that can be applied to reduce uncertainties in clinical practice. Database parameters need to be updated dynamically for outcome monitoring. Molecular prognostic factors, gene-expression signatures, tumour-infiltrating lymphocytes and circulating tumoral DNA should be added.

A novel family of online censoring based complex-valued least mean kurtosis algorithms

In this paper, we propose a novel family of online censoring (OC) based complex-valued least mean kurtosis (CLMK) algorithms by inspiring the advantages of the online censoring strategy and kurtosis-based cost function. We first develop important members of this family of algorithms such as OC based CLMK (OC-CLMK) and augmented CLMK (OC-ACLMK). These algorithms censor less informative complex-valued data streams in an online manner and keep only the most informative ones for performing their weight vector updates. Thus, they reduce the costs of data processing without markedly affecting performance accuracy. However, they do not take into account possible outliers that disturb their performances considerably. Therefore, we also develop robust members such as robust OC-CLMK (ROC-CLMK) and OC-ACLMK (ROC-CLMK) algorithms, which censor less informative complex-valued data streams as well as possible outliers. The convergence analyses of the proposed algorithms in the sense of Lyapunov are also presented to derive the bounds of their step sizes in a compact form. The simulation results on large-scale system identification and regression scenarios affirm the mentioned attractive features of the proposed algorithms. This study also shows that the noteworthy properties of the proposed algorithms will provide important contributions to the processing of complex-valued big data streams.