Generic Block Research Articles

Synergistic deployment of green infrastructure and reflective pavements for mitigation of UHI within urban blocks

Urbanisation is increasing globally, leading to the Urban Heat Island (UHI) effect, where urban areas experience higher temperatures than nearby rural regions. Mitigating UHI is a significant challenge with far-reaching implications. Green Infrastructure (GI) and reflective pavements have been shown to be effective for UHI mitigation, but extensive use of each can be impractical. By synergistically combining the benefits of both strategies, we can potentially achieve more effective UHI mitigation. Additionally, optimizing their configuration in relation to the surrounding urban morphology can also play a crucial role. This study explored the optimal distribution and configuration of GI and reflective pavements to maximise the cooling effect and cost efficiency in a generic urban block consisting of a regular grid of 25 buildings. A computational fluid dynamics (CFD) based numerical model is developed by solving the steady Reynolds-Averaged Navier-Stokes (RANS) equations to study the impact of GI and reflective pavements on temperature distributions. The climate of Ahmedabad, India, a hot and dry city, was considered. Various configurations of GI and reflective pavements were evaluated, involving a large central park, smaller distributed parks and converting only part of the roads into reflective ones. The findings indicated that the presence of a large central park and partial use of reflective pavements on only half the roads could reduce peak canopy air temperature by 0.16–0.33 °C, with a significantly higher benefit-to-cost ratio as compared to other configurations. These insights contribute to urban design by offering evidence-based solutions for temperature management, promoting sustainability, and enhancing urban quality of life.

Improving small object detection via cross-layer attention

Small object detection is a fundamental and challenging topic in the computer vision community. To detect small objects in images, several methods rely on feature pyramid networks (FPN), which can alleviate the conflict between resolution and semantic information. However, the FPN-based methods also have limitations. First, existing methods only focus only on regions with close spatial distance, hindering the effectiveness of long-range interactions. Second, element-wise addition ignores the different perceptive fields of the two feature layers, thus causing higher-level features to introduce noise to the lower-level features. To address these problems, we propose a cross-layer attention (CLA) block as a generic block for capturing long-range dependencies and reducing noise from high-level features. Specifically, the CLA block performs feature fusion by factoring in both the channel and spatial dimensions, which provides a reliable way of fusing the features from different layers. Because CLA is a lightweight and general block, it can be plugged into most feature fusion frameworks. On the COCO 2017 dataset, we validated the CLA block by plugging it into several state-of-the-art FPN-based detectors. Experiments show that our approach achieves consistent improvements in both object detection and instance segmentation, which demonstrates the effectiveness of our approach.

Alternative splicing liberates a cryptic cytoplasmic isoform of mitochondrial MECR that antagonizes influenza virus.

Viruses must balance their reliance on host cell machinery for replication while avoiding host defense. Influenza A viruses are zoonotic agents that frequently switch hosts, causing localized outbreaks with the potential for larger pandemics. The host range of influenza virus is limited by the need for successful interactions between the virus and cellular partners. Here we used immunocompetitive capture-mass spectrometry to identify cellular proteins that interact with human- and avian-style viral polymerases. We focused on the proviral activity of heterogenous nuclear ribonuclear protein U-like 1 (hnRNP UL1) and the antiviral activity of mitochondrial enoyl CoA-reductase (MECR). MECR is localized to mitochondria where it functions in mitochondrial fatty acid synthesis (mtFAS). While a small fraction of the polymerase subunit PB2 localizes to the mitochondria, PB2 did not interact with full-length MECR. By contrast, a minor splice variant produces cytoplasmic MECR (cMECR). Ectopic expression of cMECR shows that it binds the viral polymerase and suppresses viral replication by blocking assembly of viral ribonucleoprotein complexes (RNPs). MECR ablation through genome editing or drug treatment is detrimental for cell health, creating a generic block to virus replication. Using the yeast homolog Etr1 to supply the metabolic functions of MECR in MECR-null cells, we showed that specific antiviral activity is independent of mtFAS and is reconstituted by expressing cMECR. Thus, we propose a strategy where alternative splicing produces a cryptic antiviral protein that is embedded within a key metabolic enzyme.

Cross-Scale Edge Purification Network for salient object detection of steel defect images

Salient object detection has achieved great success in natural scene images, but there is big room for exploration in steel defect images (SDIs). Moreover, the unique characteristic of SDIs makes salient object detection in SDIs (SDI-SOD) a challenging task, and many representative methods for natural scene images struggle to get satisfactory results in SDI-SOD. Existing SDI-SOD methods usually ignore the edge information, and focus only on enhancing the feature interaction of different layers. To this end, we propose a specialized Cross-Scale Edge Purification Network (CSEPNet) to explore the correlations of features at different scales for SDI-SOD. To be specific, our CSEPNet is based on the general encoder–decoder architecture. First, we adopt a generic Convolutional Block Attention Module (CBAM) to refine and enhance the features from the encoder. Then, we propose the Cross-Scale Calibration Module (CSCM) and Cross-Scale Feature Interweaving Module (CSFIM) to capture the relationship of inter-layer features and intra-layer features, respectively. In CSCM, adjacent features of different scales are effectively calibrated by each other and are re-calibrated by their collective weight map to generate informative features. In CSFIM, two branches of features interweave and fuse with their corresponding edge information purified to generate robust features. Besides, we employ deep supervision and use a hybrid loss to guide the training process. We perform comprehensive experiments on the public SD-saliency-900 dataset, and demonstrate that our method is superior to 26 state-of-the-art methods, including both traditional and CNN-based ones. The code and results of our method are available at https://github.com/showmaker369/CSEPNet.

A methodology for thermal simulation of interconnects enabled by model reduction with material property variation

A thermal simulation methodology is developed for interconnects enabled by a data-driven learning algorithm accounting for variations of material properties, heat sources and boundary conditions (BCs). The methodology is based on the concepts of model order reduction and domain decomposition to construct a multi-block approach. A generic block model is built to represent a group of interconnect blocks that are used to wire standard cells in the integrated circuits (ICs). The blocks in this group possess identical geometry with various metal/via routings. The data-driven model reduction method is thus applied to learn material property variations induced by different metal/via routings in the blocks, in addition to the variations of heat sources and BCs. The approach is investigated in two very different settings. It is first applied to thermal simulation of a single interconnect block with similar BCs to those in the training of the generic block. It is then implemented in multi-block thermal simulation of a FinFET IC, where the interconnect structure is partitioned into several blocks each modeled by the generic block model. Accuracy of the generic block model is examined in terms of the metal/via routings, BCs and thermal discontinuities at the block interfaces.

HintStor: A Framework to Study I/O Hints in Heterogeneous Storage

To bridge the giant semantic gap between applications and modern storage systems, passing a piece of tiny and useful information, called I/O access hints, from upper layers to the storage layer may greatly improve application performance and ease data management in storage systems. This is especially true for heterogeneous storage systems that consist of multiple types of storage devices. Since ingesting external access hints will likely involve laborious modifications of legacy I/O stacks, it is very hard to evaluate the effect and take advantages of access hints. In this article, we design a generic and flexible framework, called HintStor, to quickly play with a set of I/O access hints and evaluate their impacts on heterogeneous storage systems. HintStor provides a new application/user-level interface, a file system plugin, and performs data management with a generic block storage data manager. We demonstrate the flexibility of HintStor by evaluating four types of access hints: file system data classification, stream ID, cloud prefetch, and I/O task scheduling on a Linux platform. The results show that HintStor can execute and evaluate various I/O access hints under different scenarios with minor modifications to the kernel and applications.

DBGC: Dimension-Based Generic Convolution Block for Object Recognition.

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs.

CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems

In order to combat the impulse noise and channel time-frequency selective fading, it is essential to use channel interleavers. Among them, the block interleaver has been widely used in terrestrial broadcasting systems such as DVB-T2 and ATSC 3.0, since it can better reflect the mapping relationship of data units on time-frequency resources. Due to the limited value of the interleaving structure parameter, the existing block interleavers cannot fully exploit time and frequency diversities, which leads to a loss of partial diversity gains. To address this problem, this paper proposes a generic block interleaver for channel interleaving through jointly considering the time and frequency diversities. First, row crossing is performed according to a certain interval, which will increase the interleaving distance; then, cyclic shift is used to achieve a greater time diversity gain. The proposed method can achieve flexible parameter configuration, which can effectively increase the scalability of the parameters and make the interleaver more versatile. Theoretical analysis shows that a larger interleaving distance is obtained and the multiplicity of minimum distance is significantly reduced. The simulation results of the fixed and mobile reception scenarios in the terrestrial broadcasting systems demonstrate the effectiveness of proposed interleaver compared to existing block interleavers.

Efficient transformation capabilities of single database private block retrieval

Private information retrieval (PIR) is one of the promising techniques to preserve user privacy in the presence of trusted-but-curious servers. The information theoretically private query construction assures the highest user privacy over curious and unbounded computation servers. Therefore, the need for information-theoretic private retrieval was fulfilled by various schemes in a variety of PIR settings. But, there is a lack of efficient encryption switching scheme which supports efficient switching between information-theoretic to/from computationally bounded PIRs. We propose a combination of new bit connection methods called rail-shape and signal-shape and new quadratic residuosity assumption based family of trapdoor functions for generic single database private block retrieval (PBR). The main goal of this work is to show that the possibility of mapping from computationally bounded privacy to information theoretic privacy or vice-versa in a single database setting using newly constructed bit connection and trapdoor function combinations. Notably, the proposed schemes are single round, memoryless and plain database schemes (at their basic constructions).

Lightweight Single Image Super-Resolution With Similar Feature Fusion Block

Convolutional neural network-based image super-resolution methods have achieved great success in recent years. However, the huge memory and computational costs make most of the existing methods difficult to be applied to resource-constrained scenarios such as edge devices. To tackle this problem, we propose a generic, lightweight and efficient feature fusion block to replace the commonly used 1*1 convolution. In addition, we propose the enhanced shallow residual blocks to improve the super-resolution performance. By combining these two novel blocks, we design an efficient similar feature fusion network for single image super-resolution, based on the observation that cross-layer features of the same channel usually have high similarities. The similar feature fusion block utilizes similarity as a guide for feature clustering, enabling efficient and high-performance cross-layer feature fusion. On the other hand, the enhanced shallow residual blocks are used as the base feature extraction model for the network to improve super-resolution performance in conjunction with the feature fusion module. In the enhanced shallow residual blocks, we combine convolution with identity connection to maintain the similarity of cross-layer features that are fed into the similar feature fusion block. The spatial attention mechanism is also introduced to reinforce the useful spatial features. Experimental results on the benchmark datasets show that the proposed method can achieve comparable results to state-of-the-art methods with a small number of parameters.

Generic Constraint-based Block Modeling using Constraint Programming

Block modeling has been used extensively in many domains including social science, spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled it as a mixed integer programming problem, but were not scalable. Subsequent work relaxed the discrete optimization requirement, and showed that adding constraints is not straightforward in existing approaches. In this work, we present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches, in both constrained and unconstrained settings, for an exhaustive search and for a type of local search called Large Neighborhood Search. We show its use in the analysis of real datasets. Finally, we show an application of the CP framework for model selection using the Minimum Description Length principle.

HiGCIN: Hierarchical Graph-Based Cross Inference Network for Group Activity Recognition.

Group activity recognition (GAR) is a challenging task aimed at recognizing the behavior of a group of people. It is a complex inference process in which visual cues collected from individuals are integrated into the final prediction, being aware of the interaction between them. This paper goes one step further beyond the existing approaches by designing a Hierarchical Graph-based Cross Inference Network (HiGCIN), in which three levels of information, i.e., the body-region level, person level, and group-activity level, are constructed, learned, and inferred in an end-to-end manner. Primarily, we present a generic Cross Inference Block (CIB), which is able to concurrently capture the latent spatiotemporal dependencies among body regions and persons. Based on the CIB, two modules are designed to extract and refine features for group activities at each level. Experiments on two popular benchmarks verify the effectiveness of our approach, particularly in the ability to infer with multilevel visual cues. In addition, training our approach does not require individual action labels to be provided, which greatly reduces the amount of labor required in data annotation.

Numerical simulation for cross-ventilation flow of generic block sheltered by urban-like block array

This study reports the results of large eddy simulations with the standard Smagorinsky model of the velocity fields within a cross-ventilation model sheltered by block arrays. A very fine resolution of 1/100 of the building height was adopted. The objectives are to understand the sheltering effect of the surrounding building array and quantify the ventilation rate in the cross-ventilation model. Two types of block arrays and opening positions were simulated. Comparisons of the numerical and experimental results justify that the present simulations reproduced the characteristics of the mean as well as turbulent flows introduced in the ventilation model. In addition, the simultaneous observations of the outdoor and indoor velocities enabled us to conclude that the change in the outdoor flow patterns caused a dramatic change in the indoor velocity distributions, although the opening position was identical in all such cases. Using these detailed flow fields, we compared three types of ventilation rates: the net, gross, and instantaneous ventilation rates. Moreover, we estimated the instantaneous ventilation rate using a random variable following the Gaussian distribution. The ventilation due to the turbulent inflow was crucial in the model with its opening on the lateral side of the block, whereas the mean flow primarily determined the ventilation rate for the block with its opening on the streamwise walls. In both cases, the estimation model could predict the instantaneous ventilation rate well with a maximum overestimation of only 3%. This means that the model was justified for all the present array and opening conditions.

Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city

This paper presents the results of an investigation on the relationship between urban block typology, solar energy harvesting potential and building energy use efficiency in the context of the tropical high-density city Singapore. Thirty generic urban block cases in six typologies that represent a diverse range of urban forms were examined through simulation-based studies under the same planning conditions and simulation assumptions so as to rule out the impact of non-design related factors. Several key planning and geometric parameters which capture the formal characteristics of the urban blocks were examined as independent variables, and the dependent variables include the performance indicators on solar energy harvesting potential and building net energy use intensity that capture the dual benefits of photovoltaic (PV) systems in reducing building cooling loads and offsetting local plug loads with electricity generated on site. The results indicate that, under the same planning conditions and design premises, differences in urban block typology could lead to up to 200% increase in solar energy harvesting potential and electricity generated from rooftop PV, twelve times higher rate of reduction in building cooling loads, tow times higher rate of reduction in net purchased electricity, and 25% lower building net energy use intensity. The courtyard and hybrid urban block typologies consistently outperform the other typologies, especially the commonly implemented tower and slab blocks, and they benefit the most from PV deployment in the tropics. The comparison between an existing residential precinct and a hypothetical alternative hybrid urban block further demonstrated the significant impact of urban design on efficiency of PV electricity generation and building energy use efficiency. In addition to the significant economic benefit in reduction in utility cost and environmental implication in terms of equivalent reduction in CO2 emissions on urban scale, this study highlights the crucial role that urban design plays in terms of maximising on-site renewable energy production such as solar energy. The significant planning and geometric parameters in relation to the performance indicators provide insight as reference for establishing solar energy friendly urban planning and architectural design guidelines. The methodology and technical workflow as developed can support reliable and efficient feasibility studies, especially in the early stage of urban planning and architectural design. Integrated with other performance evaluations, they can facilitate decision-making on implementation of renewable energy integrated green building technologies and passive design strategies.

Velocity and scalar concentrations with low occurrence frequencies within urban canopy regions in a neutrally stable shear flow over simplified urban arrays

The unsteadiness of urban airflow influences rare events of high wind speeds or high scalar concentrations. Therefore, this study uses large-eddy simulations to investigate the geometrical impact of generic block arrays on the statistical features of wind speeds and scalar concentrations within urban canopy regions. Six types of urban-like arrays with uniform and non-uniform block heights are considered, and probability density functions of wind speeds and scalar concentrations are derived based on the flow and concentration distributions within urban canopy regions. Exceeding wind speeds and scalar concentrations are determined through cumulative probability densities. The non-uniform spatial distribution of scalar concentrations is found to be correlated with the velocity magnitude distribution. In particular, airflows with strong wind speeds near tall blocks contribute to a reduction in the scalar concentration in that region. The probability density functions of velocity magnitude for arrays with height variations become long-tailed as the horizontally averaged wind speeds increase. Accordingly, the probability density at high scalar concentrations is lower for arrays with height variations. Finally, the exceeding wind speeds and scalar concentrations for arrays with height variation can be expressed as linear functions of the mean wind speed.

Time-resolved particle image velocimetry for cross-ventilation flow of generic block sheltered by urban-like block arrays

In this work, time-resolved particle image velocimetry is used for indoor velocity determination of cross-ventilation flow sheltered by urban-like arrays, to elucidate the instantaneous and statistical characteristics of the velocity fields. Combinations of two opening positions relative to the main stream flow and two surrounding array types are considered, yielding four cross-ventilation flow cases for comparison. For the streamwise window condition, the instantaneous velocity distributions differ significantly according to the outdoor block arrangement. Intermittency of the flow introduction through the windward opening and the indoor flow distributions are also confirmed based on temporal variation of the velocities. These differences in flow distribution are statistically correct according to the temporally averaged flow distributions. For the lateral window condition, instantaneous flow introduction is found to occur intermittently and alternately through each opening. Moreover, the flow introduction angle differs according to the outdoor arrangement, for both the instantaneous and mean flow distributions, indicating the importance of the surrounding-block sheltering effects on the indoor flow distributions. In addition to this elucidation of the indoor flow characteristics, the datasets obtained in this work are valuable for validating future numerical analyses of the cross-ventilation flow of a generic block under sheltered conditions.

Layer model description of the SH seismic response in anisotropically-idealized cities

ABSTRACT A city, modeled as a periodic (along one direction) assembly of blocks, infinitely long along another direction, covering a soft layer over a hard half space, lends itself to a rigorous theoretical analysis of its shear-horizontal (SH) response to a seismic body wave. This theory gives rise to two sets of linear equations, the first for the amplitudes of the waves in a generic block, and the second for the amplitudes of the waves in the hard half space. The first set is solved numerically to furnish reference solutions. The second set is submitted to low-frequency, dense city, approximation procedure whereby the displacement fields are just those for a site/city configuration in which the city is a homogeneous layer of the same thickness as the height of the blocks in the city. A simple formula is derived for the constitutive properties of this layer in terms of those of the blocks. The homogeneous layer model displacement transfer functions and spectral absorptance reproduce quite well the corresponding rigorous numerical functions throughout the generic block or surrogate layer of the city. Due to its simplicity, the homogeneous layer model enables theoretical predictions of key features of the site/city seismic response.

Experimental investigation of generic ice block trajectories under freestream conditions

An experimental database of trajectories featuring idealized, simplified artificial ice blocks under freestream conditions has been created. It includes a variation of key parameters such as the ice block geometry, density, freestream velocity and release angle of attack. The ice block’s position and attitude were optically tracked using a system of two high-speed cameras and a dedicated post-processing routine. The work performed in the scope of the EU co-funded research project STORM aims at the validation and further development of current simulation tools for ice release and ice block trajectories in terms of their ability to cope with the high complexity and randomness of this phenomenon. These tools of different level of fidelity are currently used during the aircraft certification process to estimate the threat of large ice pieces released from the aircraft’s surface impacting into downstream components. The major experimental findings are discussed in this article alongside first promising results from numerical trajectory simulations.

Generic high resolution PET detector block using 12 × 12 SiPM array

With the advancement of the Silicon Photomultiplier (SiPM) technology, these devices are becoming the mainstream for use in high resolution PET detectors especially where the compatibility with magnetic field is critical. Large-area SiPM arrays are now widely used in high resolution PET detector modules. The challenges are emerged in the readout and timing circuitry by the increase of pixel density in the area. The application-specific integrated circuits (ASICs) are developed to readout arrays with standard anode/cathode outputs. SiPM detection principle, which produces dark current noise in the inactive pixels, makes the multiplexing techniques more challenging rather than the PMT signals. In this work a new generic PET detector block using SiPM arrays with three outputs (anode, cathode, and fast) is presented which is suitable for applications in small animal, brain, and clinical PET imaging. The block detector accepts a 12 × 12 array with both standard and fast output signals with an overall detector dimension of 50 × 50 mm2. A new version of the scrambled crosswire readout (SCR) method was implemented to reduce 144 ‘fast outputs’ to 9 tile signals and 144 standard outputs to 16 energy channels. The tile signals are also used to generate time pickoff information for timing resolution. We implemented time-to-digital converter (TDC) in Xilinx’s SPARTAN6 field programmable gate array (FPGA) by using the 64-tap delay line. This low cost chip performs all required processes for position, energy, time, and interface architecture. The attached 24 × 24 LYSO:Ce array with 2 × 2 × 10 mm3 pixels is imaged and pixels are resolved clearly in the room temperature. The measured energy resolution of the detector block after calibration for all crystal pixels is 12.4% at 511 keV. Also, the coincidence resolving time for two identical modules was measured at 1.85 ns.

Chosen-Key Distinguishers on 12-Round Feistel-SP and 11-Round Collision Attacks on Its Hashing Modes

Since Knudsen and Rijmen proposed the known-key attacks in ASIACRYPT 2007, the open-key model becomes more and more popular. As the other component of the open-key model, chosen-key model was applied to the full attacks on AES-256 by Biryukov et al. in CRYPTO 2009. In this paper, we explore how practically the chosen-key model affect the real-world cryptography and show that 11-round generic Feistel-SP block cipher is no longer safe in its hashing modes (MMO and MP mode) as there exist collision attacks. This work improves Sasaki and Yasuda’s collision attacks by 2 rounds with two interesting techniques. First, we for the first time use the available degrees of freedom in the key to reduce the complexity of the inbound phase, which extends the previous 5-round inbound differential to a 7-round one. This results in a 12-round chosen-key distinguisher of Feistel-SP block cipher. Second, inspired by the idea of Wang et al., we construct collisions using two blocks. The rebound attack is used in the second compression function. We carefully balance the freedom of the first block and the complexity of the rebound attack, and extend the chosen-key attack to a 11-round collision attack on its hashing modes (MMO and MP mode).