Leader qualities as factor of successful preparation of specialist

Здійснено теоретичний аналіз наукового феномену «лідерство» з позицій сучасних наукових підходів, на основі вивченої вітчизняної та зарубіжної соціально-психологічної літератури, обґрунтовано лідерські якості особистості як вектор успішної професійної підготовки фахівця. Особистісне лідерство розглядається як внутрішній рівень який визначає потребу у особистісному зростанні та саморозвитку. Виокремлено такі важливі функції неформальних лідерів як компенсаторська, яка проявляється у ліквідуванні недоліків у діяльності керівників, функцією персоніфікації функціонально-рольових відносин. Окрему увагу приділено характеристиці феномену «лідерської присутності» та його розвитку.Ключові слова: лідер, керівник, харизма, якості особистості, коментаторський, функціональність. The article presents a theoretical analysis of scientific phenomenon of "leadership" from the standpoint of modern scientific approaches based on learned domestic and foreign social-psychological literature, substantiates leadership personality as a vector of successful professional training. Personal leadership is viewed as an internal standard that defines the need for personal growth and self-development The author outlines such important functions of the informal leaders as compensatory function, which manifests itself in the elimination of shortcomings in the activities of managers, by the function of personalization of functional-role relations. Special attention is paid to the characteristics of the phenomenon of "leadership presence" and its development. Keywords: leader, manager, charisma, personality traits commentator, functionality

Effect of LiYO2 on the synthesis and pressureless sintering of Y2SiO5

Y2SiO5 has potential applications as a high-temperature structural ceramic and environmental/thermal barrier coating. In this work, we synthesized single-phase Y2SiO5 powders utilizing a solid–liquid reaction method with LiYO2 as an additive. The reaction path of the Y2O3/SiO2/LiYO2 mixture with variation in temperatures and the role of the LiYO2 additive on preparation process were investigated in detail. The powders obtained by this method have good sinterability. Through a pressureless sintering process, almost fully dense Y2SiO5 bulk material was achieved with a very high density of 99.7% theoretical

Voxel-based Urban Vegetation Volume Analysis with LiDAR Point Cloud

The 3D volume and spatial distribution of urban vegetation are highly related to the delivery of multiple ecosystem services. However, due to the intricate vegetation structure, little research has been conducted to visualize and model the 3D spatial structure of urban vegetation. This study proposes an automated voxel-based modeling method to visualize and quantify the urban vegetation volume with LiDAR point cloud and performs a case study of the No.6 Middle School campus in Hengyang City, Hunan Province, China. The PointCNN model is used to perform semantic segmentation of the LiDAR data to extract the tree points. Then the points are voxelized into a 3D volume model with 1m×1m×1m cells. The result shows that the total vegetation volume of the area is 61,192m³, accounting for 37.28% of the total voxelized study area. The green space in front of the north teaching buildings has the largest proportion of vegetation volume, 19,366m³, accounting for 68.37% of the vegetation volume of the whole campus, due to the diverse vegetation and complex structure. The automated segmentation voxel modeling process could provide an efficient way to represent the spatial distribution of urban greenery. With an adjustable voxel size, the model could be adapted to various scales from regional to neighborhood. The model could also be used to analyze the green space structure at the human scale, as well as the interactions between green space and the surrounding environment, and to provide spatial data for the evaluation of multiple ecosystem services

Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing

Carpool-style ridesharing, compared to traditional solo ride-hailing, can reduce traffic congestion, cut per-passenger carbon emissions, reduce parking infrastructure, and provide a more cost-effective way to travel. Despite these benefits, ridesharing only occupies a small percentage of the total ride-hailing trips in cities. This study integrates big trip data with machine learning and eXplainable AI (XAI) to understand the factors that influence willingness to take shared rides. We use the City of Chicago as a case study, and results show that users tend to adopt ridesharing for longer distance trips, and the cost of a trip remains the most important factor. We identify a strong diurnal pattern that people prefer to request shared trips during the morning and afternoon peak hours. We also find socio-economic disparities: users who requested trips from neighbourhoods with a high percentage of non-white, a low median household income, a low percentage of bachelor’s degrees, and high vehicle ownership are more likely to share a ride. The findings and the XAI-based analytical framework presented in this study can help transportation network companies and local governments understand ridesharing behaviour and suggest new strategies and policies to promote the proportion of ridesharing for more sustainable and efficient city transportation

Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity

Current deep-learning models for object recognition are known to be heavily biased toward texture. In contrast, human visual systems are known to be biased toward shape and structure. What could be the design principles in human visual systems that led to this difference? How could we introduce more shape bias into the deep learning models? In this paper, we report that sparse coding, a ubiquitous principle in the brain, can in itself introduce shape bias into the network. We found that enforcing the sparse coding constraint using a non-differential Top-K operation can lead to the emergence of structural encoding in neurons in convolutional neural networks, resulting in a smooth decomposition of objects into parts and subparts and endowing the networks with shape bias. We demonstrated this emergence of shape bias and its functional benefits for different network structures with various datasets. For object recognition convolutional neural networks, the shape bias leads to greater robustness against style and pattern change distraction. For the image synthesis generative adversary networks, the emerged shape bias leads to more coherent and decomposable structures in the synthesized images. Ablation studies suggest that sparse codes tend to encode structures, whereas the more distributed codes tend to favor texture. Our code is host at the github repository: \url{https://github.com/Crazy-Jack/nips2023_shape_vs_texture}Comment: Published as NeurIPS 2023 (Oral

CofiFab: Coarse-to-fine fabrication of large 3D objects

This paper presents CofiFab, a coarse-to-fine 3D fabrication solution, which combines 3D printing and 2D laser cutting for cost-effective fabrication of large objects at lower cost and higher speed. Our key approach is to first build coarse internal base structures within the given 3D object using laser-cutting, and then attach thin 3D-printed parts, as an external shell, onto the base to recover the fine surface details. CofiFab achieves this with three novel algorithmic components. First, we formulate an optimization model to compute fabricatable polyhedrons of maximized volume, as the geometry of the internal base. Second, we devise a new interlocking scheme to tightly connect laser-cut parts into a strong internal base, by iteratively building a network of nonorthogonal interlocking joints and locking parts around polyhedral corners. Lastly, we also optimize the partitioning of the external object shell into 3D-printable parts, while saving support material and avoiding overhangs. These components also consider aesthetics, stability and balancing in addition to cost saving. As a result, CofiFab can efficiently produce large objects by assembly. To evaluate its effectiveness, we fabricate objects of varying shapes and sizes, where CofiFab significantly improves compared to previous methods

Full-range Gate-controlled Terahertz Phase Modulations with Graphene Metasurfaces

Local phase control of electromagnetic wave, the basis of a diverse set of applications such as hologram imaging, polarization and wave-front manipulation, is of fundamental importance in photonic research. However, the bulky, passive phase modulators currently available remain a hurdle for photonic integration. Here we demonstrate full-range active phase modulations in the Tera-Hertz (THz) regime, realized by gate-tuned ultra-thin reflective metasurfaces based on graphene. A one-port resonator model, backed by our full-wave simulations, reveals the underlying mechanism of our extreme phase modulations, and points to general strategies for the design of tunable photonic devices. As a particular example, we demonstrate a gate-tunable THz polarization modulator based on our graphene metasurface. Our findings pave the road towards exciting photonic applications based on active phase manipulations

Man-in-the-Middle Attack Resistant Secret Key Generation via Channel Randomization

Physical-layer based key generation schemes exploit the channel reciprocity for secret key extraction, which can achieve information-theoretic secrecy against eavesdroppers. Such methods, although practical, have been shown to be vulnerable against man-in-the-middle (MitM) attacks, where an active adversary, Mallory, can influence and infer part of the secret key generated between Alice and Bob by injecting her own packet upon observing highly correlated channel/RSS measurements from Alice and Bob. As all the channels remain stable within the channel coherence time, Mallory's injected packets cause Alice and Bob to measure similar RSS, which allows Mallory to successfully predict the derived key bits. To defend against such a MitM attack, we propose to utilize a reconfigurable antenna at one of the legitimate transceivers to proactively randomize the channel state across different channel probing rounds. The randomization of the antenna mode at every probing round breaks the temporal correlation of the channels from the adversary to the legitimate devices, while preserving the reciprocity of the channel between the latter. This prevents key injection from the adversary without affecting Alice and Bob's ability to measure common randomness. We theoretically analyze the security of the protocol and conduct extensive simulations and real-world experiments to evaluate its performance. Our results show that our approach eliminates the advantage of an active MitM attack by driving down the probability of successfully guessing bits of the secret key to a random guess.Comment: 13 pages, 8 figures, 4 table