Compressed Pattern Research Articles

Proportional Examination of Pf Mechanical Composition of Concrete Produced with Washed and Unwashed Gravel

Concrete is a very important material in the Nigerian construction industry as over 90% of her storey buildings are made from reinforced concrete. Local gravel is being increasingly used as coarse aggregate in place of the more expensive crushed granite in the production of concrete in parts of the country where it is found. Hence, this study aims at comparing the mechanical properties of concrete produced with washed and unwashed gravel to identify the most suitable aggregate for concrete. The use of two types of course aggregates for different works is examined in this study. Typical strength concrete is being made from various aggregates and their impact on various characteristics to the subsequent concrete. Compressive strength is the most vital property of concrete. In this paper, two forms of coarse aggregates washed gravel and unwashed gravel were utilized. Sharp sand is the fine aggregate. Initial laboratory examination was carried out to establish the appropriateness of utilizing the aggregates for construction purpose. Particle size distribution (sieve analysis) and slump test were investigated. Mix ratio (1:2:4 & 1:1.5:3) was used for this work and mix structures were analyzed by absolute weight technique. A total of 25 cubes (150×150×150mm) were cast to permit the compressive strength to be observed at 7, 14 and 21 days. Test results revealed that concrete produced from unwashed gravel has better workability than washed gravel. Higher compressive strength at all periods was observed with concrete produced from washed gravel aggregate. Compressive strength patterns were suggested as a result of age at curing.

Cohesive fracture model of rocks based on multi-scale model and Lennard-Jones potential

With the aim of modelling the energy dissipation phenomenon during the initiation and propagation of cracks, a novel cohesive fracture model is proposed in this study based on the multi-scale model of rocks and the Lennard-Jones potential between non-bonding molecules. The proposed model establishes the corresponding relationship of deformation in the multi-scale model of rocks and suggests that the fracture energy is essentially the manifestation of the transformation of deformation energy into potential energy between molecules. First, the multi-scale model of rocks is established based on the structural characteristics and fracture characteristics of rocks, and the corresponding relation of deformation at different scales is analysed. Thereafter, the force and potential energy equations of the cohesive fracture model corresponding to the tensile and shear processes are established. Finally, the accuracy of the cohesive fracture model is verified through three numerical simulations. The results indicate that the cohesive fracture model can accurately fit the theoretical values and experimental results in the Mode-I and Mode-II tests. In the uniaxial compression test, the cohesive fracture model can accurately simulate the uniaxial compressive strength and fracture pattern of rocks.

Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework.

In this paper, we are interested in the data gathering for Wireless Sensor Networks (WSNs). In this context, we assume that only some nodes are active in the network, and that these nodes are not transmitting all the time. On the other side, the inactive nodes are considered to be inexistent or idle for a long time period. Henceforth, the sink should be able to recover the entire data matrix whie using the few received measurements. To this end, we propose a novel technique that is based on the Matrix Completion (MC) methodology. Indeed, the considered compression pattern, which is composed of structured and random losses, cannot be solved by existing MC techniques. When the received reading matrix contains several missing rows, corresponding to the inactive nodes, MC techniques are unable to recover the missing data. Thus, we propose a clustering technique that takes the inter-nodes correlation into account, and we present a complementary minimization problem based-interpolation technique that guarantees the recovery of the inactive nodes’ readings. The proposed reconstruction pattern, combined with the sampling one, is evaluated under extensive simulations. The results confirm the validity of each building block and the efficiency of the whole structured approach, and prove that it outperforms the closest scheme.

WITHDRAWN: A novel generative adversarial block truncation coding schemes for high rated image compression on E-learning resource environment

Abstract Image compression is a useful technique that is inevitable to store any images in compressed pattern. The compressed image is reconstructed using image retrieval process for any applications. This technique ensures storage efficiency in various content management applications. Particularly, E-Learning resource environment deals with more space complexity. The E-Learning storage space complexity can be reduced with the help of image compression techniques. There are different types of image compression techniques invented for building compressed resources. Block Truncation Coding (BTC) is a type of lossy compression technique for reducing grey scale quantities using blocking and quantizing phases. This is an effective technique for compressing the images. In extend, Absolute Moment BTC (AMBTC) technique is proposed for minimizing the reconstruction error rate of standard BTC model. The image blocks generation and compression are the main phases of BTC model. This can be applied for both colour images and grey scale images. However, the conventional BTC procedures lacks for edge reconstructions and noise reductions in the output images. This problem creates BTC with lack of inaccuracy in complex image compression platforms such as E-Learning image sources. In the scope, the need for more active and generative image compression procedures is necessary to improve BTC principles. This work proposes highly complex and novel Generative Adversarial BTC (GA-BTC) compression models with multiple variances. GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of both GA models and BTC principles improve the quality of block constructions and reconstructions significantly. This work evaluates the model complexity and efficiency using various E-Learning images (Colour and Grey Scale) with different compression quality measurements.

Explosively driven dynamic compaction of granular media

This paper reports experimental investigations into the dynamic compaction of particle rings subjected to moderate explosions confined in a radial Hele-Shaw cell. The findings reveal marked transitions in the flow regimes corresponding to the evolution of the transient pressure fields inside the granular medium induced by unsteady gas infiltration. As the pressure fields evolve from being localized to diffusive with a substantial reduction in intensity, three sequent flow regimes with distinct rheologies are identified. Specifically, these flow regimes are found to be governed by the localized strong pressure field, then the competition between the diffusive pressure field and wall friction, and finally, solid stresses in the presence of rarefaction waves. A Bingham-type rheology can adequately describe the granular compaction when the pressure gradients remain the dominant driving forces, whereas the frictional nature of the granular flows becomes increasingly significant as the solid stresses set in. As the pressure gradients phase out, rarefaction decompaction commences. However, this only manages to relax the innermost layers of the compacted particles due to a distinctive compressive deformation pattern, giving rise to a discontinuous flow field. These findings shed light on the rheology of dense granular flows subjected to unsteady pressure loadings involving diverse flow–particle and particle–particle interactions.

External Mechanical Cues Reveal a Katanin-Independent Mechanism behind Auxin-Mediated Tissue Bending in Plants.

Tissue folding is a central building block of plant and animal morphogenesis. In dicotyledonous plants, hypocotyl folds to form hooks after seedling germination that protects their aerial stem cell niche during emergence from soil. Auxin response factors and auxin transport are reported to play a key role in this process. Here, we show that the microtubule-severing enzyme katanin contributes to hook formation. However, by exposing hypocotyls to external mechanical cues mimicking the natural soil environment, we reveal that auxin response factors ARF7/ARF19, auxin influx carriers, and katanin are dispensable for apical hook formation, indicating that these factors primarily play the role of catalyzers of tissue bending in the absence of external mechanical cues. Instead, our results reveal the key roles of the non-canonical TMK-mediated auxin pathway, PIN efflux carriers, and cellulose microfibrils as components of the core pathway behind hook formation in the presence or absence of external mechanical cues.

Simulation Study on Geometric Anisotropic Estimators for Spatial Point Process

The method by Fry for detecting geometric anisotropy in stationary spatial point pattern is investigated. We quantify anisotropy by stretching and compressing the point process about the axis. Using a simulated Strauss point pattern, we first fit ellipsoids to the compressed pattern of pairwise difference vectors to estimate the direction of anisotropy. The strength of compression and the regularity of the point process are varied at different times and the corresponding effect on the estimated ellipsoids is investigated as the level of contour ellipsoid increases. Parameters and contours good for estimation were discussed.

Low-velocity impact response and breakage characteristics of hollow brittle particles

Low-velocity impact responses of typical hollow brittle particles (HBPs) were tested with a strain-limited loading technique to investigate the particle breakage characteristics at both macro and particle levels. The quasi-static compressions were also conducted for comparison. Effects of strain rate and particle size distribution on the stress-strain response, particle breakage and macro deformation behaviors were discussed. Results show that the strength, plateau stress and energy dissipation of hollow brittle particles all exhibited significant strain rate sensitivity. The particle size gradations after single-impact compression were obtained through the laser diffraction technique. Quantitative analysis on the fragmentations and the Hardin relative breakage shown that the dynamic breakage ratio and the breakage extent of the hollow particles at the same strain were larger than that under quasi-static compressions. Moreover, it was found that the Hardin relative breakage generated by specific energy dissipation decreased with the strain rate. The reduction of fracture energy dissipation efficiency and the rate-dependent breakage response of particle breakage are the intrinsic reasons for bulk-scale strain rate effects of HBPs. Finally, the macro deformation behaviors of hollow brittle particles were analyzed through digital image correlation (DIC) technique. It was observed that there exists a competition mechanism between the boundary effect and the inertial effect in dominating the compression patterns. The deformation mechanism was sensitive to the loading velocity.

Facial Nerve Indentation in Hemifacial Spasm: An Analysis of Factors Contributing to the Formation of and Consequent Effects Associated with Indentation

An indentation, designating a furrowed hole on the facial nerve, has been used in many studies for locating pathophysiology and assessing relevant clinical outcomes after microvascular decompression for hemifacial spasm (HFS). In this study, we sought to elucidate the contributing factors forming indentation on the facial nerve and the consequent effect of having indentation on the clinical course. We divided the patients into 2 groups: group A, the patients who had no indentation on the root exit zone of the facial nerve; and group B, the patients who had an indentation. Demographic data, intraoperative findings, and clinical outcomes were analyzed from retrospective review of the medical records. Of the 132 patients, 47.0% had an indentation on the facial nerve. Our statistical analyses showed that the preoperative symptom period, compression location, and compression pattern were associated with the occurrence of the indentation. Also, we showed that HFS reappearance developed more frequently in patients in group B, who needed more time for the resolution of HFS. The final clinical outcome was less influenced by the existence of the indentation, although it was slightly poorer for group B than for group A. The indentation on the facial nerve was associated with longer duration of symptoms, the presence of compression in the proximal segment of the root exit zone, and loop-type pattern of compression. More patients with indentation experienced the HFS reappearance phenomenon, which lasted longer than in those who had no indentation.

Optimal skeleton and reduced Huffman trees

A skeleton Huffman tree is a Huffman tree from which all full subtrees of depth h≥1 have been pruned. Skeleton Huffman trees are used to save storage and enhance processing time in several applications such as decoding, compressed pattern matching and wavelet trees for random access. A reduced skeleton tree prunes the skeleton Huffman tree further to an even smaller tree. The resulting more compact trees can be used to further enhance the time and space complexities of the corresponding algorithms. However, it is shown that the straightforward ways of basing the constructions of a skeleton tree as well as that of a reduced skeleton tree on a canonical Huffman tree do not necessarily yield the least number of nodes. New algorithms for achieving such trees are given.

Numerical investigation of biomechanically coupled growth in cortical folding.

Cortical folding-the process of forming the characteristic gyri (hills) and sulci (valleys) of the cortex-is a highly dynamic process that results from the interaction between gene expression, cellular mechanisms, and mechanical forces. Like many other cells, neurons are sensitive to their mechanical environment. Because of this, cortical growth may not happen uniformly throughout gyri and sulci after the onset of cortical folding, which is accompanied by patterns of tension and compression in the surrounding tissue. Here, as an extension of our previous work, we introduce a biomechanically coupled growth model to investigate the importance of interaction between biological growth and mechanical cues during brain development. Our earlier simulations of cortical growth consisted of a homogeneous growing cortex attached to an elastic subcortex. Here, we let the evolution of cortical growth depend on a geometrical quantity-the mean curvature of the cortex-to achieve preferential growth in either gyri or sulci. As opposed to the popular pre-patterning hypothesis, our model treats inhomogeneous cortical growth as the result of folding rather than the cause. The model is implemented numerically in a commercial finite element software Abaqus/Explicit in Abaqus reference manuals, Dassault Systemes Simulia, Providence (2019) by writing user-defined material subroutine (VUMAT). Our simulations show that gyral-sulcal thickness variations are a phenomenon particular to low stiffness ratios. In comparison with cortical thickness measurements of [Formula: see text] human brains via a consistent sampling scheme, our simulations with similar cortical and subcortical stiffnesses suggest that cortical growth is higher in gyri than in sulci.

Real-time adversarial GAN-based abnormal crowd behavior detection

Detecting abnormal events in the crowd is a challenging problem. Insufficient samples make those traditional model-based methods cannot cope with sophisticated anomaly monitoring. Therefore, we design a real-time generative adversarial network plus an add-on encoder to deal with the continually changing environment. After the generator reconstructs the compressed pattern to generate the design to the latent vector, a discriminator is used to construct better videos by minimizing the adversarial loss function. We calculated the abnormal score by the distance between the two underlying patterns encoded by the first and the second encoders. The unusual event is detected when the anomaly score is above the threshold. To accelerate the processing efficiency, we introduced the grouped pointwise convolution method to decrease the computing complexity. The frame-level and video-level experiments on the benchmark dataset show the accuracy and reliance of our approach. The acceleration approach can increase the efficiency of the network with only limited accuracy loss.

Faculty Salary Inversion, Compression, and Market Salary Gap in California State University Business Schools

Extant literature has examined salary compression and inversion in US academic institutions including arguments that compression constitutes a form of age discrimination. We assess these phenomena via estimated full rank salaries (across nine campuses in the teaching-oriented California State University (CSU) system) based on the notion that in the absence of compression and inversion, forecasted full rank salaries should be comparable across faculty ranks. Our results show that salary compression is present across all sampled Colleges of Business (COB), but not in other academic CSU colleges. In addition, the patterns of compression support the notion that salaries in CSU COBs are becoming more inverted as the gap between market and current salaries increases. Recommendations for policy and collective bargaining negotiations are also offered.

OMNI: A Framework for Integrating Hardware and Software Optimizations for Sparse CNNs

Convolution neural networks (CNNs) as one of today's main flavor of deep learning techniques dominate in various image recognition tasks. As the model size of modern CNNs continues to grow, neural network compression techniques have been proposed to prune the redundant neurons and synapses. However, prior techniques disconnect the software neural networks compression and hardware acceleration, which fail to balance multiple design parameters, including sparsity, performance, hardware area cost, and efficiency. More concretely, prior unstructured pruning techniques achieve high sparsity at the expense of extra performance overhead, while prior structured pruning techniques relying on strict sparse patterns lead to low sparsity and extra hardware cost. In this article, we propose OMNI, a framework for accelerating sparse CNNs on hardware accelerators. The innovation of OMNI stems from that it uses hardware amenable on-chip memory partition patterns to seamlessly engage the software CNN model compression and hardware CNN acceleration. To accelerate the compute-intensive convolution kernel, a promising hardware optimization approach is memory partition, which divides the original weight kernels into several groups so that the different hardware processing elements can simultaneously access the weight. We exploit the memory partition patterns including block, cyclic, or hybrid as a means of CNN compression patterns. Our software CNN model compression balances the sparsity across different groups and our hardware accelerator employs hardware parallelization coordinately with the sparse patterns, leading to a desirable compromise between sparsity and performance. We further develop performance models to help the designers to quickly identify the pattern factors subject to an area constraint. Last, we evaluate our design on application specific integrated circuit (ASIC) and field-programmable gate array (FPGA) platform. Experiments demonstrate that OMNI achieves 3.4×- 6.2× speedup for the modern CNNs, over a comparably ideal dense CNN accelerator. OMNI shows 114.7× energy efficiency improvement compared with GPU platform. OMNI is also evaluated on Xilinx ZC706 and ZCU102 FPGA platforms, achieving 41.5 GOP/s and 125.3 GOP/s, respectively.

A Study on the Compression and Major Pattern Extraction Method of Origin-Destination Data with Principal Component Analysis

A Study on the Compression and Major Pattern Extraction Method of Origin-Destination Data with Principal Component Analysis

Structural compressibility in 3d printed abrasion protection structures

Abrasion protection in exposed areas is a common method of extending the life cycle of textile products. To achieve sufficient abrasion resistance, the textile products are usually coated or partially printed with hard products. But hard materials lead to stiffening and can have a negative effect on the haptics.
 The aim of this study is the application of abrasion-resistant and compressible print patterns directly to textile substrates by 3D printing. The compressibility is achieved by the inner structure, which, in contrast to conventional coatings, is not 100 % filled. In the investigations, four thermoplastic materials were tested for their abrasion resistance and compressible properties. Fabrics made of cotton or polyester/cotton blends were used as textile substrates. Different approaches were taken to create the structures, some of which can only be realized using 3D printing technology.
 The abrasion resistance of the substrates could be increased from 50 abrasion cycles to over 8,000 cycles with the help of the 3D printed patterns. It was possible to print 3D structures that could be compressed in thickness by up to 53 %. The combination of both properties, abrasion resistance and compressibility, can be used for new types of individual products with good wearing comfort and functionality that meet the requirements, especially in the areas of functional, protective and sports clothing.

3D variable-density SPARKLING trajectories for high-resolution T2*-weighted magnetic resonance imaging.

We have recently proposed a new optimization algorithm called SPARKLING (Spreading Projection Algorithm for Rapid K-space sampLING) to design efficient compressive sampling patterns for magnetic resonance imaging (MRI). This method has a few advantages over conventional non-Cartesian trajectories such as radial lines or spirals: i) it allows to sample the k-space along any arbitrary density while the other two are restricted to radial densities and ii) it optimizes the gradient waveforms for a given readout time. Here, we introduce an extension of the SPARKLING method for 3D imaging by considering both stacks-of-SPARKLING and fully 3D SPARKLING trajectories. Our method allowed to achieve an isotropic resolution of 600 μm in just 45 seconds for T2∗-weighted ex vivo brain imaging at 7 Tesla over a field-of-view of 200 × 200 × 140 mm3 . Preliminary in vivo human brain data shows that a stack-of-SPARKLING is less subject to off-resonance artifacts than a stack-of-spirals.

Seawater sea-sand Engineered Cementitious Composites (SS-ECC) for marine and coastal applications

Engineered Cementitious Composites (ECC) have great potential in marine and coastal applications to extend the service life of infrastructures. In this study, the feasibility of using seawater and sea-sand to produce both normal- and high-strength ECC was explored. Some fundamental properties of seawater sea-sand ECC (SS-ECC) were evaluated, including setting time, 28-day compressive strength, tensile performance, and crack pattern under tension. Normal-strength SS-ECC with compressive strength of 58 MPa, tensile strength of 5 MPa and tensile strain capacity of 4%, as well as high-strength SS-ECC with compressive strength of 137 MPa, tensile strength of 8 MPa and tensile strain capacity of 5% were produced. Using seawater and sea-sand slightly increased the compressive strength (12%), and marginally decreased the tensile strength (6%) and tensile strain capacity (18%) of normal-strength ECC, while it has almost no effects on these properties of high-strength ECC. Additionally, using seawater and sea-sand decreased the crack width of normal-strength ECC, but increased that of high-strength ECC. These findings provide insights into future design and applications of ECC in high-performance marine and coastal infrastructures.

Unsupervised Idealization of Nano-Electronic Sensors Recordings with Concept Drifts: An Information Theory Approach for Non-Stationary Single-Molecule Data Analysis

Unsupervised Idealization of Nano-Electronic Sensors Recordings with Concept Drifts: An Information Theory Approach for Non-Stationary Single-Molecule Data Analysis

Compression of Cerebellar Functional Gradients in Schizophrenia.

Our understanding of cerebellar involvement in brain disorders has evolved from motor processing to high-level cognitive and affective processing. Recent neuroscience progress has highlighted hierarchy as a fundamental principle for the brain organization. Despite substantial research on cerebellar dysfunction in schizophrenia, there is a need to establish a neurobiological framework to better understand the co-occurrence and interaction of low- and high-level functional abnormalities of cerebellum in schizophrenia. To help to establish such a framework, we investigated the abnormalities in the distribution of sensorimotor-supramodal hierarchical processing topography in the cerebellum and cerebellar-cerebral circuits in schizophrenia using a novel gradient-based resting-state functional connectivity (FC) analysis (96 patients with schizophrenia vs 120 healthy controls). We found schizophrenia patients showed a compression of the principal motor-to-supramodal gradient. Specifically, there were increased gradient values in sensorimotor regions and decreased gradient values in supramodal regions, resulting in a shorter distance (compression) between the sensorimotor and supramodal poles of this gradient. This pattern was observed in intra-cerebellar, cerebellar-cerebral, and cerebral-cerebellar FC. Further investigation revealed hyper-connectivity between sensorimotor and cognition areas within cerebellum, between cerebellar sensorimotor and cerebral cognition areas, and between cerebellar cognition and cerebral sensorimotor areas, possibly contributing to the observed compressed pattern. These findings present a novel mechanism that may underlie the co-occurrence and interaction of low- and high-level functional abnormalities of cerebellar and cerebro-cerebellar circuits in schizophrenia. Within this framework of abnormal motor-to-supramodal organization, a cascade of impairments stemming from disrupted low-level sensorimotor system may in part account for high-level cognitive cerebellar dysfunction in schizophrenia.