Space Compression Research Articles

Replicated Data Management Using Scaled Segment Chain in Unstable IoT Environments

IoT edge gateway reduces cloud computing's overload that redirect sensor data to remote servers. For reliable and efficient IoT gateway, column-based flash memory has become a reasonable storage due to its space efficiency and compression performance. This paper introduces recent IoT network and edge computing technology. It proposes efficient replication management called Context-mapped Segment Submirroring to support stable data services for sensor data in the edge-based IoT environment. Sensor context scaling and chained segment submirroring schemes are presented to improve the reliability and performance using IoT edge gateway. In the chained submirroring scheme, the sensor data are kept in the space-efficient storage of IoT edge. Consequently, sensor data transmission and mirroring storage cost can be minimized. The simulation results show that the proposed scheme outperforms the traditional scheme in respect of operation throughput and its response time.

Effect of stress on permeability of clay silty cores recovered from the Shenhu hydrate area of the South China Sea

Hydrate saturation and effective stress are the most important key factors in controlling permeability by altering the pore structure of hydrate reservoirs. However, the understanding of the dependence of the permeability on these factors in fine-grained hydrate reservoirs is not comprehensive due to limited experimental conditions. In this study, clay silty cores recovered from the Shenhu hydrate area of the South China Sea were used as skeletons to synthesize tetrahydrofuran (THF) hydrate-bearing sediments with hydrate saturations of 0–0.6. A series of step-by-step loading and water permeability measurements were conducted on a customized permeability measurement system. The results showed that the water permeability was in the range of 0.005–4 mD, approximately 0.03–1.47 mD near the value of the in situ effective stress of ∼2 MPa. The water permeability increased with increasing effective porosity and conformed to a modified Kozeny-Carmen equation, while the effective porosity increased exponentially with hydrate saturation and effective stress. Permeability stress sensitivity analyses showed that the normalized permeability stress sensitivity coefficient increased with the increase of hydrate saturation. Pore space compression and pore throat clogging were the important factors in the reduction in the water permeability, which dropped by approximately 95% when the effective stress increased from 0 to 8 MPa. This study provides insight into the evolution of pore space and permeability during gas recovery from hydrate-bearing clay silty sediments.

Data-driven bus timetabling with spatial-temporal travel time

PurposeTravel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.Design/methodology/approachThe authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.FindingsCase studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.Originality/valueThis study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

FVC: An End-to-End Framework Towards Deep Video Compression in Feature Space.

Deep video compression is attracting increasing attention from both deep learning and video processing community. Recent learning-based approaches follow the hybrid coding paradigm to perform pixel space operations for reducing redundancy along both spatial and temporal dimentions, which leads to inaccurate motion estimation or less effective motion compensation. In this work, we propose a feature-space video coding framework (FVC), which performs all major operations (i.e., motion estimation, motion compression, motion compensation and residual compression) in the feature space. Specifically, a new deformable compensation module, which consists of motion estimation, motion compression and motion compensation, is proposed for more effective motion compensation. In our deformable compensation module, we first perform motion estimation in the feature space to produce the motion information (i.e., the offset maps). Then the motion information is compressed by using the auto-encoder style network. After that, we use the deformable convolution operation to generate the predicted feature for motion compensation. Finally, the residual information between the feature from the current frame and the predicted feature from the deformable compensation module is also compressed in the feature space. Motivated by the conventional codecs, in which the blocks with different sizes are used for motion estimation, we additionally propose two new modules called resolution-adaptive motion coding (RaMC) and resolution-adaptive residual coding (RaRC) to automatically cope with different types of motion and residual patterns at different spatial locations. Comprehensive experimental results demonstrate that our proposed framework achieves the state-of-the-art performance on three benchmark datasets including HEVC, UVG and MCL-JCV.

Training of sparse and dense deep neural networks: Fewer parameters, same performance.

Deep neural networks can be trained in reciprocal space by acting on the eigenvalues and eigenvectors of suitable transfer operators in direct space. Adjusting the eigenvalues while freezing the eigenvectors yields a substantial compression of the parameter space. This latter scales by definition with the number of computing neurons. The classification scores as measured by the displayed accuracy are, however, inferior to those attained when the learning is carried in direct space for an identical architecture and by employing the full set of trainable parameters (with a quadratic dependence on the size of neighbor layers). In this paper, we propose a variant of the spectral learning method as in Giambagli etal. [Nat. Commun. 12, 1330 (2021)2041-172310.1038/s41467-021-21481-0], which leverages on two sets of eigenvalues for each mapping between adjacent layers. The eigenvalues act as veritable knobs which can be freely tuned so as to (1) enhance, or alternatively silence, the contribution of the input nodes and (2) modulate the excitability of the receiving nodes with a mechanism which we interpret as the artificial analog of the homeostatic plasticity. The number of trainable parameters is still a linear function of the network size, but the performance of the trained device gets much closer to those obtained via conventional algorithms, these latter requiring, however, a considerably heavier computational cost. The residual gap between conventional and spectral trainings can be eventually filled by employing a suitable decomposition for the nontrivial block of the eigenvectors matrix. Each spectral parameter reflects back on the whole set of internode weights, an attribute which we effectively exploit to yield sparse networks with stunning classification abilities as compared to their homologs trained with conventional means.

Computed tomographic myelography for assessment of the cervical spinal cord in ataxic warmblood horses: 26 cases (2015-2017).

To quantify the degree of dural compression and assess the association between site and direction of compression and articular process (AP) size and degree of dural compression with CT myelography. 26 client-oriented horses with ataxia. Spinal cord-to-dura and AP-to-cross-sectional area of the C6 body ratios (APBRs) were calculated for each noncompressive site and site that had > 50% compression of the subarachnoid space. Site of maximum compression had the largest spinal cord-to-dura ratio. Fisher exact test and linear regression analyses were used to assess the association between site and direction of compression and mean or maximum APBR and spinal cord-todura ratio, respectively. Mean ± SD spinal cord-to-dura ratio was 0.31 ± 0.044 (range, 0.20 to 0.41) for noncompressive sites and 0.44 ± 0.078 (0.29 to 0.60) for sites of maximum compression. Sites of maximum compression were intervertebral and extra-dural, most frequently at C6 through 7 (n = 10), followed by C3 through 4 (6). Thirteen horses had dorsolateral and lateral compression at the AP joints, secondary to AP (n = 7) or soft tissue proliferation (6). Site significantly affected direction of compression, and directions of compression from occiput through C4 were primarily ventral and lateral, whereas from C6 through T1 were primarily dorsal and dorsolateral. No linear relationship was identified between mean or maximum APBR and spinal cord-to-dura ratio. CT myelography may be useful for examination of horses with suspected cervical compressive myelopathy. Degree of compression can be assessed quantitatively, and site of compression significantly affected direction of compression.

GPU acceleration of rank-reduced coupled-cluster singles and doubles.

We have developed a graphical processing unit (GPU) accelerated implementation of our recently introduced rank-reduced coupled-cluster singles and doubles (RR-CCSD) method. RR-CCSDintroduces a low-rank approximation of the doubles amplitudes. This is combined with a low-rank approximation of the electron repulsion integrals via Cholesky decomposition. The result of these two low-rank approximations is the replacement of the usual fourth-order CCSDtensors with products of second- and third-order tensors. In our implementation, only a single fourth-order tensor must be constructed as an intermediate during the solution of the amplitude equations. Owing in large part to the compression of the doubles amplitudes, the GPU-accelerated implementation shows excellent parallel efficiency (95% on eight GPUs). Our implementation can solve the RR-CCSDequations for up to 400 electrons and 1550 basis functions-roughly 50% larger than the largest canonical CCSDcomputations that have been performed on any hardware. In addition to increased scalability, the RR-CCSDcomputations are faster than the corresponding CCSDcomputations for all but the smallest molecules. We test the accuracy of RR-CCSDfor a variety of chemical systems including up to 1000 basis functions and determine that accuracy to better than 0.1% error in the correlation energy can be achieved with roughly 95% compression of the ov space for the largest systems considered. We also demonstrate that conformational energies can be predicted to be within 0.1kcal mol-1 with efficient compression applied to the wavefunction. Finally, we find that low-rank approximations of the CCSDdoubles amplitudes used in the similarity transformation of the Hamiltonian prior to a conventional equation-of-motion CCSDcomputation will not introduce significant errors (on the order of a few hundredths of an electronvolt) into the resulting excitation energies.

Study on the nonlinear response and shear behavior of RC columns under lateral impact

In the present study, in view of the problem of nonlinear response caused by vehicle-RC pier collision, a new test system which can realize lateral impact is proposed, and two types of 1:3 scale RC components in five groups are subject to the lateral impact test at a velocity of 4.5 m/s. The system’s design characteristics represent the effect of stirrup spacing and axial compression ratio on the dynamic response of RC columns. In addition, the nonlinear response results of RC columns, such as impact force, acceleration, lateral displacement, failure mode and degree, are given in the test. Digital Image Correlation (DIC) technology is applied to the test. Next, the lateral strain response of RC columns during impact is measured, and the strain change process in every representative region is calculated. The data outlined in this study provide a reasonable basis for evaluating the shear capacity of RC columns and developing a finite element model (FEM). Finally, a typical FEM of vehicle-pier collision scenario is developed, while an improved equation for calculating the axial compression ratio-shear increment and a simplified calculation value of dynamic shear capacity of the RC pier are proposed.

Evaluation and optimization analysis of high-speed rail network structure in Northeast China under the background of northeast revitalization

The construction of high-speed rail (HSR) network has promoted the social-economic ties of cities, accelerated the compression of time and space, and changed the pattern of regional development. In this paper, with the adoption of the operation frequency data of HSR from 12306 website, and based on the HSR connection strength model and social network analysis model, as well as according to the HSR connection strength, HSR network density, centrality, agglomeration subgroup, and other indicators, we analyzed the characteristics of HSR network structure in Northeast China. Results show that the number of HSR cities in Northeast China is small, cities in HSR network generally exhibit weak connectivity, and the existence of HSR network marginalizes cities such as Ulanhot, Baicheng, and Songyuan, which significantly reduce the overall network connectivity of Northeast China. The overall centrality of HSR network in Northeast China is characterized by “one axis, four edges”; specifically, the one axis is located in Harbin-Dalian transportation line and the four edges are located on both sides of the main axis of Harbin-Dalian transportation line. Eight agglomeration subgroups (four double city subgroups and four multi city subgroups) have formed in Northeast China. The core status of Shenyang in HSR network is improved significantly, and “one axis and two wings” HSR network in Liaoning Province is improved significantly. With the gradual expansion of Chaoyang-Fuxin, Dandong-Benxi, and Jilin-Yanji branch networks, the “point axis” HSR network mode in Northeast China has gradually developed and matured. In the future, it is recommended to rely on eight agglomerating subgroups to encrypt HSR network structure, create secondary node central cities, and gradually build a new pattern of opening up in Northeast China.

Speeding up, slowing down. Language, temporality and the constitution of migrant workers as labour force

ABSTRACT This article offers an original ethnographic documentation of employability schemes targeting migrants in contemporary Italy. It argues that analysts’ current theorisations of time and space compression do not help us understand the multiple temporalities that migrants are subjected to when crossing borders, including those of labour market regimes. This ethnographic account is informed by a scholarship of migration that has extensively documented how the acceleration of movement and access to language, citizenship or work co-exist with experiences of waiting, elongation, withdrawal and delay – processes that complicate our understanding of the temporal regimes migrants are subjected to. Through a thick documentation of the experiences of unemployed migrants, job counsellors and other social actors in employability programmes in Rome, this article argues that both speeding up and slowing down are technologies of temporal management, including time–space compression, elongation and partitioning. These technologies regulate the time and speed of migrants’ incorporation into the labour market and allow the performance of processes of differential inclusion.

Explicit Modeling of Personal Space for Improved Local Dynamics in Simulated Crowds

Crowd simulation demands careful consideration in regard to the classic trade-off between accuracy and efficiency. Particle-based methods have seen success in various applications in architecture, military, urban planning, and entertainment. This method focuses on local dynamics of individuals in large crowds, with a focus on serious games and entertainment. The technique uses an area-based penalty force that captures the infringement of each entity's personal space. This method does not need a costly nearest-neighbor search and allows for an inherently data-parallel implementation capable of simulating thousands of entities at interactive frame rates. The algorithm reproduces personal space compression around motion barriers for moving crowds and around points of interest for static crowds.

Theoretical and methodological approaches revealing the current challenges to the spatial development of regions in the steppe zone of Russia

To detect the current challenges to spatial development, we attempted to identify and theoretically explain the basic notions of stages of challenges’ formation. Such concepts as “challenge,” “danger,” “treat,” and “risk” were defined. The revealed determinants were classified according to five blocks of challenges, and multi-scale levels of their formation were detected. Eighteen region-subjects of the RF steppe mesoresion were chosen as the territory of the study. The methodological approach to see challenges using assessments of the rate of increase (2019 to 2010) was offered. There are 28 principal indicators grouped in blocks: inter-regional contrasts (economic development and a state of the social sphere), the imbalance of the natural-ecological and socio-economic frameworks, a compression of developed space, the formation of seats of potential ecological catastrophes, and threat of food security. The table of the rate of increase was designed. Based on this table, we could reveal prevailing negative processes and increased risk of challenge’s emergence in every region. According to the study results, the examined challenges in the least degree emerged in the Republics of Adygei and Kalmykia. In the most degree (or more evident), they displayed themselves in the Republic of Bashkortostan, Volgogradskaya, and Chelyabinskaya oblasts.Some suggestions were stated to work out long-term steppe region development plans in the frame of economic-geographical and socio-economic research.

An Expensive Multi-Objective Optimization Algorithm Based on Decision Space Compression

Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space.

Effect of conservation of spatial volume on electromagnetism

This essay presents an alternate mechanism for electromagnetism and provides arguments supporting new concepts of this phenomenon. Electricity and magnetism are defined and influenced by the electric permittivity ε o and magnetic permeability μ o physical constants of the vacuum. This essay's concepts are predicated on the notion that the free-space vacuum appears to be composed of an energy field with characteristics of an elastic medium termed the “spatial energy field” or SEF. It is proposed that the geometric volume of the SEF requires conservation, and this is achieved via stretching or compression of space exhibiting wavelike qualities. The SEF, representing spacetime, has specific properties related to the emergence of an electrical field force via the Poynting vector. Magnetic field force appears to emanate from the SEF based on the Lorentz force. Space (SEF) appears to mediate energy fields, such as electrical and magnetic fields, and provides a framework for the transmission of electromagnetic waves in addition to momentum-gravitational waves. Conservation of spatial volume influences these phenomena.

Botulinum Toxin Treatment for Thoracic Outlet Syndrome Induced by Subclavius Muscle Hypertrophy

Thoracic outlet syndrome (TOS) is frequently caused by bone abnormalities and congenital or acquired soft-tissue alterations. Among these, isolated Subclavius Muscle (SM) hypertrophy represents a rare condition that could lead to a reduction in costoclavicular space and brachial plexus compression. A 47-year-old forest ranger with a history of gun shooting during animal hunting and training sessions of skeet shooting for 20 years developed TOS due to ultrasonography-detected isolated SM hypertrophy, successfully treated with an ultrasound-guided Botulinum Toxin (BTX)-A injection. In our patient, ultrasonography of the brachial plexus has allowed SM hypertrophy to be recognized and to perform BTX-A injection just in the muscle, with a complete resolution of the symptoms.

Ensemble-Based Electrical Resistivity Tomography with Data and Model Space Compression

Inversion of electrical resistivity tomography (ERT) data is an ill-posed problem that is usually solved through deterministic gradient-based methods. These methods guarantee a fast convergence but hinder accurate assessments of model uncertainties. On the contrary, Markov Chain Monte Carlo (MCMC) algorithms can be employed for accurate uncertainty appraisals, but they remain a formidable computational task due to the many forward model evaluations needed to converge. We present an alternative approach to ERT that not only provides a best-fitting resistivity model but also gives an estimate of the uncertainties affecting the inverse solution. More specifically, the implemented method aims to provide multiple realizations of the resistivity values in the subsurface by iteratively updating an initial ensemble of models based on the difference between the predicted and measured apparent resistivity pseudosections. The initial ensemble is generated using a geostatistical method under the assumption of log-Gaussian distributed resistivity values and a Gaussian variogram model. A finite-element code constitutes the forward operator that maps the resistivity values onto the associated apparent resistivity pseudosection. The optimization procedure is driven by the ensemble smoother with multiple data assimilation, an iterative ensemble-based algorithm that performs a Bayesian updating step at each iteration. The main advantages of the proposed approach are that it can be applied to nonlinear inverse problems, while also providing an ensemble of models from which the uncertainty on the recovered solution can be inferred. The ill-conditioning of the inversion procedure is decreased through a discrete cosine transform reparameterization of both data and model spaces. The implemented method is first validated on synthetic data and then applied to field data. We also compare the proposed method with a deterministic least-square inversion, and with an MCMC algorithm. We show that the ensemble-based inversion estimates resistivity models and associated uncertainties comparable to those yielded by a much more computationally intensive MCMC sampling.

A Practical Highly Paralleled ReRAM-Based DNN Accelerator by Reusing Weight Pattern Repetitions

Resistive random access memory (ReRAM)-based processing-in-memory (PIM) architecture has been designed to accelerate deep neural networks (DNNs) by concurring computation and memory barriers. To further improve memory and computation efficiency, the weight sparsity characteristic has been explored to optimize the ReRAM-based DNN accelerators. However, these designs only focus on compressing zero weights to eliminate ineffectual computation. In this article, we thoroughly analyze the weight distribution characteristics of several typical DNN models and observe many nonzero weight pattern repetitions (WPRs). Therefore, there is an opportunity to further improve the performance and energy efficiency by reusing these WPR. We propose a novel ReRAM-based accelerator—PattPIM, to achieve space compression and computation reuse by exploring DNN WPR based on practical ReRAM crossbars. In PattPIM, we propose a configurable WPR-aware DNN engine and a WPR-to-OU mapping scheme to save both space and computation resources. An intraprocessing engine (PE) pipeline is designed to improve the parallelism of the computation process. Furthermore, we adopt an approximate weight pattern transform algorithm to improve the DNN WPR ratio to enhance the reuse efficiency with negligible accuracy loss. Our evaluation with 6 DNN models shows that the proposed PattPIM delivers significant performance improvement, ReRAM resource efficiency and energy saving.

Contributions of pictorial and binocular cues to the perception of distance in virtual reality

We assessed the contribution of binocular disparity and the pictorial cues of linear perspective, texture, and scene clutter to the perception of distance in consumer virtual reality. As additional cues are made available, distance perception is predicted to improve, as measured by a reduction in systematic bias, and an increase in precision. We assessed (1) whether space is nonlinearly distorted; (2) the degree of size constancy across changes in distance; and (3) the weighting of pictorial versus binocular cues in VR. In the first task, participants positioned two spheres so as to divide the egocentric distance to a reference stimulus (presented between 3 and 11 m) into three equal thirds. In the second and third tasks, participants set the size of a sphere, presented at the same distances and at eye-height, to match that of a hand-held football. Each task was performed in four environments varying in the available cues. We measured accuracy by identifying systematic biases in responses and precision as the standard deviation of these responses. While there was no evidence of nonlinear compression of space, participants did tend to underestimate distance linearly, but this bias was reduced with the addition of each cue. The addition of binocular cues, when rich pictorial cues were already available, reduced both the bias and variability of estimates. These results show that linear perspective and binocular cues, in particular, improve the accuracy and precision of distance estimates in virtual reality across a range of distances typical of many indoor environments.

The Enactment of Fast and Slow Time Regimes by Urban Retail and Consumer Services

This article discusses the enactment of conflicting time regimes in contemporary urban retail and consumer services. We draw on the theories of time–space compression, social acceleration, and the fast–slow dichotomy to argue that retail and consumer services act on urban life by enacting two apparently conflicting time regimes: fast time and slow time. Although retail and consumer services are not able to establish urban time regimes by themselves, they enact time regimes in their stores by offering the temporal resources that consumers need to perform their preferred timestyles. These temporal resources stem from the store’s concepts, sales model and management strategies, and ambiances. We draw on our ongoing field research in Colinas do Cruzeiro, an upper middle-class suburban neighborhood in greater Lisbon, Portugal, which has included field surveys, nonparticipant observation, and semistructured interviews. Our findings identify three time regimes that retail and consumer services in Colinas do Cruzeiro enact in their stores. This finding allows us to understand the processes through which retail, consumer services, and urban rhythms tend to synchronize. We discuss the geographical implications of understanding the processes that underpin the enactment of time regimes in contemporary urban retail and consumer services.

Studies of mortuary value system and its behavior: Columbarium Niche burial and natural burial as study cases in Taiwan

Aim: This study focuses on current Taiwanese mortuary behavior and realizes the gap between mortuary value system and mortuary behavior, and explores the two formers relations with grief care. Eventually, this study provide mortuary policy suggestion to Taiwan government for reference.Method: This study adopts secondary analysis, field interview, and data thematic analysis. This study aims family members who lost their beloved within three years. There are 13 participators. 8 of participators adopted columbarium niche burial and 5 adopts natural burial. Findings: 1) The strategy of funeral arrangement is mostly made of lineal relatives; to be environmental friendly and sanitation and not to be troublesome to the bereaved are mainly reasons to choose burial ways. 2) The corpse mostly sent to a funeral home and erect the deceaseds memorial tablet; the number of people who do not send obituary and simplify total ritual to only 7 days have increased; and traditional wiping and bathing rites to the deceased seems losing its real function. 3) It is prejudicial to grief care while there is a gap between mortuary value and mortuary behavior.Implications/Novel Contribution: Overall, the research findings will provide for the Taiwan government to formulate natural burial policy. The government has to consider grief care of the deceaseds family while it considers the goal of environmental protection and sanitation. Besides, this research attempts to arouse funeral business, academy, and the governments attention to the negative influence on time and space compression of mortuary arrangement and then improve current situation.