Dynamic Unary Convolution in Transformers

It is uncertain whether the power of transformer architectures can complement existing convolutional neural networks. A few recent attempts have combined convolution with transformer design through a range of structures in series, where the main contribution of this paper is to explore a parallel design approach. While previous transformed-based approaches need to segment the image into patch-wise tokens, we observe that the multi-head self-attention conducted on convolutional features is mainly sensitive to global correlations and that the performance degrades when these correlations are not exhibited. We propose two parallel modules along with multi-head self-attention to enhance the transformer. For local information, a dynamic local enhancement module leverages convolution to dynamically and explicitly enhance positive local patches and suppress the response to less informative ones. For mid-level structure, a novel unary co-occurrence excitation module utilizes convolution to actively search the local co-occurrence between patches. The parallel-designed Dynamic Unary Convolution in Transformer (DUCT) blocks are aggregated into a deep architecture, which is comprehensively evaluated across essential computer vision tasks in image-based classification, segmentation, retrieval and density estimation. Both qualitative and quantitative results show our parallel convolutional-transformer approach with dynamic and unary convolution outperforms existing series-designed structures

Effects of structural parameters on flow boiling performance of reentrant porous microchannels

National Nature Science Foundation of China [51275180]; National Nature Science Foundation of Guangdong Province [S2012030006231]Flow boiling within advanced microchannel heat sinks provides an efficient and attractive method for the cooling of microelectronics chips. In this study, a series of porous microchannels with Omega-shaped reentrant configurations were developed for application in heat sink cooling. The reentrant porous microchannels were fabricated by using a solid-state sintering method under the replication of specially designed sintering modules. Micro wire electrical discharge machining was utilized to process the graphite-based sintering modules. Two types of commonly used copper powder in heat transfer devices, i.e., spherical and irregular powder, with three fractions of particle sizes respectively, were utilized to construct the porous microchannel heat sinks. The effects of powder type and size on the flow boiling performance of reentrant porous microchannels, i.e., two-phase heat transfer, pressure drop and flow instabilities, were examined under boiling deionized water conditions. The test results show that enhanced two-phase heat transfer was achieved with the increase of particle size for the reentrant porous microchannels with spherical powder, while the reversed trend existed for the counterparts with irregular powder. The reentrant porous microchannels with irregular powder of the smallest particle size presented the best heat transfer performance and lowest pressure drop

Cryogenic in-memory computing using tunable chiral edge states

Energy-efficient hardware implementation of machine learning algorithms for quantum computation requires nonvolatile and electrically-programmable devices, memristors, working at cryogenic temperatures that enable in-memory computing. Magnetic topological insulators are promising candidates due to their tunable magnetic order by electrical currents with high energy efficiency. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a chiral edge state-based cryogenic in-memory computing scheme. On the one hand, the chiral edge state can be tuned from left-handed to right-handed chirality through spin-momentum locked topological surface current injection. On the other hand, the chiral edge state exhibits giant and bipolar anomalous Hall resistance, which facilitates the electrical readout. The memristive switching and reading of the chiral edge state exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks based on magnetic topological memristors demonstrate a software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing memristor technologies. Our results may inspire further topological quantum physics-based novel computing schemes.Comment: 33 pages, 12 figure

Experimental study on LBL beams

Six specimens were made and tested to study the mechanical properties of LBL beams. The mean ultimate loading value is 68.39 MPa with a standard deviation of 6.37 MPa, giving a characteristic strength (expected to be exceeded by 95% of specimens) of 57.91 MPa, and the mean ultimate deflection is 53.3 mm with a standard deviation of 5.5 mm, giving the characteristic elastic modulus of 44.3 mm. The mean ultimate bending moment is 20.18 kN.m with a standard deviation of 1.88 kN.m, giving the characteristic elastic modulus of 17.08 kN.m. The mean elastic modulus is 9688 MPa with a standard deviation of 1765 MPa, giving the characteristic elastic modulus of 6785 MPa, and the mean modulus of rupture is 93.3 MPa with a standard deviation of 8.6 MPa, giving the characteristic elastic modulus of 79.2 MPa. The strain across the cross-section for all LBL beams is basically linear throughout the loading process, following standard beam theory

Graphene-Based Nanocomposites for Energy Storage

Since the first report of using micromechanical cleavage method to produce graphene sheets in 2004, graphene/graphene-based nanocomposites have attracted wide attention both for fundamental aspects as well as applications in advanced energy storage and conversion systems. In comparison to other materials, graphene-based nanostructured materials have unique 2D structure, high electronic mobility, exceptional electronic and thermal conductivities, excellent optical transmittance, good mechanical strength, and ultrahigh surface area. Therefore, they are considered as attractive materials for hydrogen (H2) storage and high-performance electrochemical energy storage devices, such as supercapacitors, rechargeable lithium (Li)-ion batteries, Li–sulfur batteries, Li–air batteries, sodium (Na)-ion batteries, Na–air batteries, zinc (Zn)–air batteries, and vanadium redox flow batteries (VRFB), etc., as they can improve the efficiency, capacity, gravimetric energy/power densities, and cycle life of these energy storage devices. In this article, recent progress reported on the synthesis and fabrication of graphene nanocomposite materials for applications in these aforementioned various energy storage systems is reviewed. Importantly, the prospects and future challenges in both scalable manufacturing and more energy storage-related applications are discussed

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Exploring the relationship between leadership and followership of Chinese project managers

Purpose – Followership is the free will recognition of leadership in the commitment towards realization of the collectively adopted organization vision and culture. The purpose of this paper is to identify the relationship between project managers’ leadership and their followership. Most project managers are both leaders and followers at the same time, but research typically investigates only their leadership. This ignores followership as an important aspect in understanding and predicting behavior, and further in the selection of project managers. Design/methodology/approach – The method used for this paper is explanatory and a deductive, through which the above research hypothesis is tested using quantitative techniques. Data are collected through a nation-wide survey in China. Data analysis is done through Factor Analysis, Canonical Correlation Analysis and Multiple Regression Analysis. Findings – The results show that transformational leadership is positively correlated with transformational followership and transactional followership, and that transactional leadership is negatively correlated with transactional followership. Research limitations/implications – The paper supports a deeper investigation into leadership and followership theories. A model for both leadership and followership is developed. The findings from this paper will help organizations in choosing their project managers. Originality/value – The originality lies in the new way to examine the relationship between leadership and followership. It is the first study of this type on project managers. Its value lies in a new perspective towards the relationship between leadership and followership in project management

Experimental Investigation on the Mechanical Properties of Vault Void Lining in Highway Tunnels and Steel Plate Strengthening

In the present study, large-scale specimens based on the tunnel prototype were prepared and static load tests were carried out to investigate the damage caused by lining voids. Based on the strengthening scheme of the tunnel, the strengthened specimens were prepared to explore the strengthening effect on the strengthening structure. The strengthening structure is made of a steel plate fixed with chemical anchor bolts and two-component epoxy adhesive. By analyzing the failure mode, structural deformation, and the relationship between load and strain, the damage caused by vault void with various void heights was analyzed and the obtained results were verified through the experiment. Moreover, the enhancement of the bearing capacity and stiffness of the structure strengthened by surface bonding steel was studied. The obtained results show that the damage caused by the lining void mainly occurs at the void boundary. The damage appears as multiple longitudinal cracks. The crack starts from the lower surface and develops radially. Using chemical anchor bolts and two-component epoxy adhesive to bond the steel plate on the lining surface, the damage can be reduced, and the bearing capacity of the structure can be improved effectively when the void height is a quarter of the second lining thickness, the number of cracks is reduced from 14 to 5 after steel plate strengthening, and the length of the longest crack is reduced from 13.2 cm to 8.3 cm, reduced by 37.12%. The steel plate strengthening also reduces the strain of the lower steel bar at the void boundary from 1130.58 με to 555.12 με, and the strain decreases by 50.89%. The experimental results show that the position where the void has the greatest impact on the lining is at the void boundary. Therefore, when steel plates are used to strengthen the void lining, the void boundary should be emphasized, which makes the strengthening more accurate and saves the cost of treatment

Fire risk analysis of runway excursion accidents in high-plateau airport

The risk assessment of runway excursion accidents in the high-plateau airport is a significant part of the airport operations and risk management. This article proposes a method to evaluate the risk of runway excursion accidents in the high-plateau airport with the probability and severity estimations of runway excursion in the high-plateau airport. Firstly, the probability estimation is calculated by combining the correction model and the Bayesian network. The probability correction model considers the runway length required for takeoff and landing, specific ambient temperature, and wind speeds in the high-plateau airport. Then, a high-plateau airport simulation evacuation model of evacuation capacity is established by the VR experiment, and the severity of evacuation in the high-plateau airport is evaluated, combining the endurance of fire products. Finally, based on probability and severity, the quantitative calculation value of risk is given. We also utilize the model on a case study to find the effect of temperature, wind speed, and altitude on this risk index. The results show that the risk of runway excursion accidents in the high-plateau airport is greatly affected by temperature and wind speed. The experimental airport's risk value in February is about 11.8 times of that in September, and the risk value of the high-plateau airport is 7.32 times higher than that in a plain airport. The model successfully simulates the various scenarios at a high-plateau airport and other airports at different altitudes. It is proved that the fire risk of high-plateau airport runway excursion accidents should be paid attention to and provides scientific guidance for the airport's aviation safety management based on the actual characteristics of a high-plateau airport

Pedestrian Trajectory Prediction in Extremely Crowded Scenarios

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion. In this study, a novel trajectory prediction model is proposed to capture the relative motion of pedestrians in extremely crowded scenarios. Trajectory sequences and human interaction are first represented with relative motion and then integrated to our model to predict pedestrians’ trajectories. The proposed model is based on Long Short Term Memory (LSTM) structure and consists of an encoder and a decoder which are trained by truncated back propagation. In addition, an anisotropic neighborhood setting is proposed instead of traditional neighborhood analysis. The proposed approach is validated using trajectory data acquired at an extremely crowded train station in Tokyo, Japan. The trajectory prediction experiments demonstrated that the proposed method outperforms existing methods and is stable for predictions of varying length even when the model is trained with a controlled short trajectory sequence