Space Compression Research Articles

Cross-Modal Hashing Method With Properties of Hamming Space: A New Perspective.

Cross-modal hashing (CMH) has attracted considerable attention in recent years. Almost all existing CMH methods primarily focus on reducing the modality gap and semantic gap, i.e., aligning multi-modal features and their semantics in Hamming space, without taking into account the space gap, i.e., difference between the real number space and the Hamming space. In fact, the space gap can affect the performance of CMH methods. In this paper, we analyze and demonstrate how the space gap affects the existing CMH methods, which therefore raises two problems: solution space compression and loss function oscillation. These two problems eventually cause the retrieval performance deteriorating. Based on these findings, we propose a novel algorithm, namely Semantic Channel Hashing (SCH). First, we classify sample pairs into fully semantic-similar, partially semantic-similar, and semantic-negative ones based on their similarity and impose different constraints on them, respectively, to ensure that the entire Hamming space is utilized. Then, we introduce a semantic channel to alleviate the issue of loss function oscillation. Experimental results on three public datasets demonstrate that SCH outperforms the state-of-the-art methods. Furthermore, experimental validations are provided to substantiate the conjectures regarding solution space compression and loss function oscillation, offering visual evidence of their impact on the CMH methods.

EvoAI enables extreme compression and reconstruction of the protein sequence space.

Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here we establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 1048. The extreme compressibility of the space informs both applied biomolecular design and understanding of natural evolution.

AFSC: Adaptive Fourier Space Compression for Anomaly Detection

AFSC: Adaptive Fourier Space Compression for Anomaly Detection

Decoding aging and cognitive functioning through spatiotemporal EEG patterns: Introducing spatiotemporal information-based similarity analysis.

Exploring spatiotemporal patterns of high-dimensional electroencephalography (EEG) time series generated from complex brain system is crucial for deciphering aging and cognitive functioning. Analyzing high-dimensional EEG series poses challenges, particularly when employing distance-based methods for spatiotemporal dynamics. Therefore, we proposed an innovative methodology for multi-channel EEG data, termed as Spatiotemporal Information-based Similarity (STIBS) analysis. The core of this method is to first perform state space compression of multi-channel EEG time series using global field power, which can provide insight into the dynamic integration of spatiotemporal patterns between the steady states and non-steady states of brain. Subsequently, we quantify the pairwise differences and non-randomness of spatiotemporal patterns using an information-based similarity analysis. Results demonstrated that this method holds the potential to serve as a distinguishing marker between young and elderly on both pairwise differences and non-randomness indices. Young individuals and those with higher cognitive abilities exhibit more complex macrostructure and non-random spatiotemporal patterns, whereas both aging and cognitive decline lead to more randomized spatiotemporal patterns. We further extended the proposed analytics to brain regions adversarial STIBS (bra-STIBS), highlighting differences between young and elderly, as well as high and low cognitive groups. Furthermore, utilizing the STIBS-based XGBoost model yields superior recognition accuracy in aging (93.05%) and cognitive functioning (74.29%, 64.19%, and 80.28%, respectively, for attention, memory, and compatibility performance recognition). STIBS-based methodology not only contributes to the ongoing exploration of neurobiological changes in aging but also provides a powerful tool for characterizing the spatiotemporal nonlinear dynamics of the brain and their implications for cognitive functioning.

Amplitude variation with azimuth direct inversion for effective pressure-sensitivity parameters in horizontal transversely isotropic media

Effective pressure prediction is of great significance for fracturability evaluation in unconventional reservoirs. Quantitative characterization of effective pressure remains a great challenge in the inversion prediction of subsurface media. Our goal is to develop a novel Bayesian amplitude varying with azimuth (AVAZ) inversion method to estimate fracture weaknesses and effective pressure for a horizontal transversely isotropic (HTI) medium. Using Schoenberg’s linear slip model and pore space compressibility theory, an anisotropic saturated stiffness matrix directly characterized by effective pressure is derived based on a weak-contrast interface separating two weakly anisotropic half-spaces of HTI medium. Based on the perturbation stiffness matrix and scattering theory, we derive a PP-wave reflection coefficient approximation directly characterized by fracture weaknesses and effective pressure-sensitive parameters, which establishes a quantitative relationship between seismic reflection coefficient and effective pressure. The analysis of accuracy and sensitivity is performed to verify the reliability and applicability of the constructed reflection coefficient. And then, the azimuth-amplitude-difference forward solver is constructed based on seismic reflection coefficients to eliminate the influence of isotropic background perturbations on seismic reflection coefficients and reduce the condition number of the forward solver. Finally, we present a novel Bayesian azimuth-amplitude-difference AVAZ inversion method for transversely isotropic media constrained by Cauchy and Gaussian regularization to directly estimate the fracture weaknesses and effective pressure-sensitive parameters. The application of synthetic data and field data verifies that the inversion results are in good agreement with the actual data, indicating that the method can effectively realize the stable prediction of anisotropic parameters and effective pressure-sensitive parameters of complex media.

Transformation of informal elder care practices and mobilities during the pandemic

ABSTRACT The COVID-19 pandemic revealed that when formal care services closed down, informal care burden was unequally transferred to women. Women across the world became part of “caregiver pools”. Using textual archive material collected by the Finnish Literature Society, we analyse the transformation of informal elder care practices in Finland. We adopt a care mobility and care process perspective to analyse how informal elder care was transformed due to social distancing measures that restricted ageing individuals and their family members’ local and translocal mobilities. We find that over 70-year-olds had to adapt their daily mobilities according to risk assessment. Caregiving provided by mostly female family members was replaced by digital care at a distance, which reinforced existing inequalities in care. We argue that the pandemic simultaneously brought about both a time–space compression and what we call a time–space expansion that affected the everyday practices and mobilities of care.

The journey metaphor in Huawei mobile phone commercials

Abstract This paper investigates the creative use of the structural, embodied journey metaphor within the genre of video advertising using Conceptual Metaphor Theory (CMT). Conducting a qualitative analysis of eight Huawei mobile phone commercials, I examine how multimodal resources — visual, verbal and written language, as well as music and sound — are orchestrated to construct and communicate the metaphorical narrative of using a Huawei mobile phone in terms of a journey. The analysis reveals a heavy reliance on visual elements, supported by the strategic use of sound and music, to create metaphors that communicate progress and exploration — themes central to the journey concept. This paper shows how the journey metaphor in audio-visual advertising diverges from its purely verbal varieties by drawing on medium-specific capabilities such as time and space compression, and on hyperbole. It is argued that the journey metaphor’s embodied nature and its potency in aligning the mobile phone with consumer aspirations contribute to the universal appeal and understanding of the commercials.

Multi-Color Spaceplates in the Visible.

The ultimate miniaturization of any optical system relies on the reduction or removal of free-space gaps between optical elements. Recently, nonlocal flat optic components named "spaceplates" were introduced to effectively compress space for light propagation. However, space compression over the visible spectrum remains beyond the reach of current spaceplate designs due to their inherently limited operating bandwidth and functional inefficiencies in the visible range. Here, we introduce "multi-color" spaceplates performing achromatic space compression at three distinct color channels across the visible spectrum to markedly miniaturize color imaging systems. In this approach, we first design monochromatic spaceplates with high compression factors and high transmission amplitudes at visible wavelengths based on a scalable structure and dielectric materials widely used in the fabrication of meta-optical components. We then show that the dispersion-engineered combination of monochromatic spaceplates with suitably designed transmission responses forms multicolor spaceplates that function achromatically. The proposed multicolor spaceplates, composed of amorphous titanium dioxide and silicon dioxide layers, efficiently replace free-space volumes with compression ratios as high as 4.6, beyond what would be achievable by a continuously broadband spaceplate made of the same materials. Our strategy for designing monochromatic and multicolor spaceplates, along with the presented theoretical and computational results, show that strong space-compression effects can be achieved in the visible range. Our findings may ultimately enable a new generation of ultrathin optical devices for various applications.

Consuming placelessness: a geographic critique of current Antarctic tourism development

This paper explores the ongoing development of products, activities, and experiences in the rapidly expanding Antarctic tourism industry and their potential implications for the production of Antarctic places. Informed by theories of place and placelessness, the paper identifies three particular place framings informed by tourism diversification: the compression of time and space, the detachment of Antarctica from Earth, and the progressive consumption of wilderness through luxury and comfort. The paper argues that these dynamics may contribute to a detachment from the ways in which Antarctic places are situationally and materially assembled, leading to the abstraction, simplification, and obfuscation of socio-material relations making for a place. The possible consequence is the transformation of the Antarctic into a placeless space of consumption. By emphasizing the detachment facilitated by certain prominent activities and infrastructure, the paper contributes to debates about tourism destination development, urging consideration of how tourism can be shaped to foster an appreciation of places in their own right.

Time-varying moisture characteristics and C-S-H gel properties of concrete in dry and hot environments

The construction of deeply buried tunnels has intensified concerns regarding shrinkage cracking and the long-term performance deterioration of lining concrete in dry-hot environments. These issues are related to the dynamic interconversion of internal moisture states, the changing moisture behavior over time, and the evolution of calcium silicate hydrate (C-S-H) properties. However, the mechanisms underlying these phenomena are not fully understood. This study investigates the dynamics of moisture content across different states, the source and composition of evaporated water, as well as the mechanism of change in the microscopic properties of C-S-H in concrete subjected to dry-hot conditions (40℃ and 60℃) and standard curing environments through tests on water loss rates, chemically bound water, and low-field nuclear magnetic resonance. The findings reveal that at 40℃ and 60℃, hydration products were generated rapidly before mold removal, leading to significant increases in chemically bound water, interlayer water, and gel water, while capillary water was notably consumed. After mold removal, the rate of chemically bound water increase slowed, while interlayer water, gel water, and capillary water evaporated significantly, with gel water comprising 87.27 % and 83.51 % of the evaporated water at 40℃ and 60℃, respectively. From day 1 to day 28, interlayer water, gel water, and capillary water contents decreased significantly, with reductions of up to 85.98 % at 60℃. In dry-hot environments, moisture primarily exists as chemically bound water and gel water, contrasting with standard curing conditions where capillary water is also present. The study concludes that drying of C-S-H gels leads to both positive effects, such as interlayer space compression and increased indentation modulus, and negative effects, including increased microporosity and reduced matric densification, ultimately contributing to the deterioration of long-term mechanical properties.

Системно-синергетический анализ и синтез управляемого процесса резания

Currently, good progress has been produced in the creation of machine tools with CNC computer systems. They are able to ensure the coincidence of program and the trajectories of the actuators with high accuracy. But when manufacturing parts with the same accuracy it becomes possible in rare circumstances. This is because of the necessity to take into account the entire controlled dynamic cutting system when manufacturing parts, while the quality of the part depends on various physical interactions under process work. In contrast to the well-known research and development, the paper focuses on the fact that the modeling level is based on a system-synergetic representation, which includes the procedure of expansion – compression of the state space. The system takes into account elastic deformations of its elements, evolutionary changes in their properties, uncontrollable disturbances, revealing physical interactions. This is a complex system, where its individual coordinates of the state depend on programmable trajectories first of all, secondly, they are characterized by internal connections and self-organization, and finally, they affect the output properties of cutting. The output properties include parts quality parameters and specified costs for parts manufacture. The article describes the main provisions of the synergetic system analysis and synthesis of a dynamic cutting system control, provides its mathematical modeling, views a simple procedure for analyzing and synthesizing a model. The example of practical application has been also observed.

Informatics-enhanced prediction of failure strength in skeletal muscle tissue

Skeletal muscles are susceptible to injury during daily activities and competitive sports. Reliable prediction of tissue failure strength is a major challenge due to the large uncertainty in the mechanical behavior of skeletal muscle tissue. The present study reveals a strong correlation between histological characteristics and skeletal muscle failure strength by means of mechanical and histological experiments. We propose a data-driven hybrid modeling approach that enables an effective integration of data science and informatics tools to capture tissue failure strength. Uncertainty in the tissue failure strength is propagated into the posterior information of reduced model parameters via the Bayesian inference framework and parameter space compression. A histological enhancement to the softening hyperelasticity model is made by linking a quantified tissue-scale histological characteristic and stiff model parameters using artificial neural networks. The model is applied to skeletal muscle tissue from different species and sites to assess its predictive capabilities for physiological differences. The results show that the approach can achieve reliable predictions of skeletal muscle tissue failure strength. The proposed approach can be extended to different scales to enrich the understanding of structure–property linkages for biomaterials.

Piezoelectric Biomedical Material, Poly-L-Lactic Acid (PLLA), in the Treatment of Spinal Epidural Compression: Diagnosis, Monitoring, and Postoperative Support

This study aims to design an intelligent biomedical piezoelectric material for monitoring the pressure in the spinal epidural space caused by postoperative hematoma compression (spinal epidural hematoma). Hematoma compression in the spinal epidural space is a clinically challenging condition that is difficult to diagnose, and failure to address it within forty-eight hours may lead to severe consequences. Given its urgency, prompt and accurate diagnosis and intervention are crucial. Due to its elusive diagnostic nature, patients often miss the opportune time for treatment. Therefore, this study utilizes poly L-lactic acid (PLLA), an FDA-approved highly biocompatible biomaterial. After undergoing high-pressure polarization, PLLA exhibits excellent piezoelectric properties, making it an ideal material for monitoring intracavitary pressure. Additionally, since postoperative hematoma formation is a primary cause of spinal epidural membrane compression, this study integrates urokinase, a potent thrombolytic protein, with PLLA electrospun fibers, enabling preliminary treatment upon pressure detection.This intelligent organic biomedical material not only enables real-time monitoring of postoperative spinal epidural space pressure but also passively utilizes ultrasound to release urokinase from PLLA fibers for initial treatment when pressure is detected. Furthermore, the application of electrospun PLLA biomedical material makes the material more easily moldable and simplifies the manufacturing process, expanding its potential applications. In summary, this technology combines expertise in organic materials science, electrochemistry, and clinical medicine, offering an innovative approach to addressing the crisis posed by hematoma compression in the spinal epidural space. In future clinical applications, this technology holds the promise of providing earlier and more effective treatment for patients, potentially becoming a significant breakthrough in the fields of medical electronics and biomedical engineering. Keywords: Piezoelectric Biomaterial, Pressure Monitoring Sensor, Drug Control Release, Postoperative Support, Diagnostic Innovation

Surface Susceptibility Synthesis of Spatially Dispersive Metasurfaces for Space Compression and Spatial Signal Processing

Surface Susceptibility Synthesis of Spatially Dispersive Metasurfaces for Space Compression and Spatial Signal Processing

Актуализация подходов к исследованию неоднородности экономического пространства

The article distinguishes the categories of “heterogeneity of economic space” and “interregional social differentiation”, as well as the ways of their assessment. On the basis of the theories of new economic geography and center-periphery theory, the nature of spatial heterogeneity is revealed. The heterogeneity of Russia’s economic space is analyzed through the prism of two key properties of space, i.e., density and connectivity. It is shown that the spatial heterogeneity in terms of population density is growing against the background of decreasing differentiation of space in terms of economic density, measured as GRP per 1 km 2 and volume of investment in fixed capital per 1 km 2 , which can be explained by the fact that the economy is in a phase of recession. A positive correlation between population density and highway density is revealed, which can be explained by the discreteness of space. Based on empirical data, the authors conclude that communicative compression of economic space is characteristic of regions with high population density and economic density and regions with extremely low population density and transport connectivity. In the first case, it is evidence of high business activity in the territory, while in the second case it is a way to increase the availability of services. Based on the comparison of population density and economic density of space, four types of territories were identified: territories of spatial compression; territories with growing population density against the background of high economic density of space; and territories characterized by high population density with relatively low density of economic space; territories with relatively low population density and a trend for its further reduction against the background of high economic density of space. The priorities of spatial development are defined for each type of territory.

Nonequilibrium formulation of varying-temperature bit erasure

Landauer’s principle states that erasing a bit of information at fixed temperature T costs at least kBTln⁡2 units of work. Here we investigate erasure at varying temperature, to which Landauer’s result does not apply. We formulate bit erasure as a stochastic nonequilibrium process involving a compression of configuration space, with physical and logical states associated in a symmetric way. Erasure starts and ends at temperature T, but temperature can otherwise vary with time in an arbitrary way. Defined in this way, erasure is governed by a set of nonequilibrium fluctuation relations that show that varying-temperature erasure can done with less work than kBTln⁡2 . As a result, erasure and the complementary process of bit randomization can be combined to form a work-producing engine cycle.

Challenging Political Representation in the Era of Big Data

This article deals with the problem of the change in the theoretical conception and practical realisation of political representation in the era of big data, i.e. in the 20th and 21st century, when IT data processes have become an almost integral part of the political life of citizens. Based on the concepts of the philosophers F. Ankersmit and J. Baudrillard, the article analyses why, despite the arguments of critical data studies, the forms of political representation affected by big data are still considered objective. After presenting research on the characteristics of political representation in the information technology-dominated public sphere and smart cities, the article identifies signs of the decline of contemporary political representation: the compression of public space and the legitimation processes of datafication facilitated by big data in the smart city. The continuation of the article’s theme and the analysis of the problems are recommended to be pursued in the field of post-political discussions.

A local search approach to protocol verification

Verification of parameterized protocols is of great interest in the area of formal verification, mainly due to the practical importance of such systems. Explicit-state model checking is an essential approach in which states for a system are maintained in explicit form, as are all state transitions. In order to reduce the search space to a minimum, we propose a local search strategy. In detail, first, heuristic functions are designed to guide the searching of bugs more effectively, which exploits information extracted from invariants and rulesets, to improve the calculation of a distance between two states. Second, candidate solutions with poor scores are dropped for further compression of the state space explored. Especially, we apply successfully our local search algorithm to the reachable analysis of a real-world implementation of TileLink protocol.

Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia.

Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer's disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R2 = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function.

Permeability of surface clay-bearing sediments in Shenhu Area of South China Sea

The migration of fluids, such as the dissociation of natural gas hydrates, can affect the stability of the seabed and even induce submarine geological disasters and change the marine environment. The migration of fluids in sediments depends on its permeability properties. Therefore, it is crucial to investigate the sediment permeability and its influencing factors. This paper investigates the particle size, porosity, pore size distribution, and permeability of surface clay-bearing sediments in the Shenhu Area of the South China Sea by physical property tests, computed tomography (CT) scanning tests, constant rate of strain (CRS) consolidation tests, and nuclear magnetic resonance (NMR) tests. The permeability of the surface sediments in the Shenhu Area ranges from 3.32 × 10−15–6.76 × 10−14 m2. The pore size mainly ranges between 10−5 and 10−1 mm, exhibiting a bimodal distribution with peaks from 10−4 and 10−2 mm. The increase in clay content leads to enhanced permeability due to the bimodal distribution of pore sizes, which will cause the enlargement of pores. As the pore size increases, the sediment permeability also increases. Sediment permeability linearly decreases with increasing effective axial stress and deformation due to compression of the pore space. Additionally, a new model for calculating permeability with different sediment properties is established, which improves the accuracy of permeability calculations for clay-bearing sediments compared to previous research. The findings of our study can provide a reference for exploring the influence of sediment parameters on fluid migration mechanisms in clay-bearing sediments.