138 research outputs found
Inflating hollow nanocrystals through a repeated Kirkendall cavitation process.
The Kirkendall effect has been recently used to produce hollow nanostructures by taking advantage of the different diffusion rates of species involved in the chemical transformations of nanoscale objects. Here we demonstrate a nanoscale Kirkendall cavitation process that can transform solid palladium nanocrystals into hollow palladium nanocrystals through insertion and extraction of phosphorus. The key to success in producing monometallic hollow nanocrystals is the effective extraction of phosphorus through an oxidation reaction, which promotes the outward diffusion of phosphorus from the compound nanocrystals of palladium phosphide and consequently the inward diffusion of vacancies and their coalescence into larger voids. We further demonstrate that this Kirkendall cavitation process can be repeated a number of times to gradually inflate the hollow metal nanocrystals, producing nanoshells of increased diameters and decreased thicknesses. The resulting thin palladium nanoshells exhibit enhanced catalytic activity and high durability toward formic acid oxidation
PDNPulse: Sensing PCB Anomaly with the Intrinsic Power Delivery Network
The ubiquitous presence of printed circuit boards (PCBs) in modern electronic
systems and embedded devices makes their integrity a top security concern. To
take advantage of the economies of scale, today's PCB design and manufacturing
are often performed by suppliers around the globe, exposing them to many
security vulnerabilities along the segmented PCB supply chain. Moreover, the
increasing complexity of the PCB designs also leaves ample room for numerous
sneaky board-level attacks to be implemented throughout each stage of a PCB's
lifetime, threatening many electronic devices. In this paper, we propose
PDNPulse, a power delivery network (PDN) based PCB anomaly detection framework
that can identify a wide spectrum of board-level malicious modifications.
PDNPulse leverages the fact that the PDN's characteristics are inevitably
affected by modifications to the PCB, no matter how minuscule. By detecting
changes to the PDN impedance profile and using the Frechet distance-based
anomaly detection algorithms, PDNPulse can robustly and successfully discern
malicious modifications across the system. Using PDNPulse, we conduct extensive
experiments on seven commercial-off-the-shelf PCBs, covering different design
scales, different threat models, and seven different anomaly types. The results
confirm that PDNPulse creates an effective security asymmetry between attack
and defense
A collaborative and dynamic multi-source single-destination navigation algorithm for smart cities
A knowledge distillation-based multi-scale relation-prototypical network for cross-domain few-shot defect classification
Surface defect classification plays a very important role in industrial production and mechanical manufacturing. However, there are currently some challenges hindering its use. The first is the similarity of different defect samples makes classification a difficult task. Second, the lack of defect samples leads to poor accuracies when using deep learning methods. In this paper, we first design a novel backbone network, ResMSNet, which draws on the idea of multi-scale feature extraction for small discriminative regions in defect samples. Then, we introduce few-shot learning for defect classification and propose a Relation-Prototypical network (RPNet), which combines the characteristics of ProtoNet and RelationNet and provides classification by linking the prototypes distances and the nonlinear relation scores. Next, we consider a more realistic scenario where the base dataset for training the model and target defect dataset for applying the model are usually obtained from domains with large differences, called cross-domain few-shot learning. Hence, we further improve RPNet to KD-RPNet inspired by knowledge distillation methods. Through extensive comparative experiments and ablation experiments, we demonstrate that either our ResMSNet or RPNet proves its effectiveness and KD-RPNet outperforms other state-of-the-art approaches for few-shot defect classification.</p
An Efficient Dynamic Multi-Sources To Single-Destination (DMS-SD) Algorithm In Smart City Navigation Using Adjacent Matrix
Dijkstra's algorithm is one of the most popular classic path planning
algorithms, achieving optimal solutions across a wide range of challenging
tasks. However, it only calculates the shortest distance from one vertex to
another, which is hard to directly apply to the Dynamic Multi-Sources to
Single-Destination (DMS-SD) problem. This paper proposes a modified Dijkstra
algorithm to address the DMS-SD problem, where the destination can be
dynamically changed. Our method deploys the concept of Adjacent Matrix from
Floyd's algorithm and achieves the goal with mathematical calculations. We
formally show that all-pairs shortest distance information in Floyd's algorithm
is not required in our algorithm. Extensive experiments verify the scalability
and optimality of the proposed method.Comment: International Conference On Human-Centered Cognitive Systems (HCCS)
202
Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder
Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., sigma) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.China Postdoctoral Science Foundation, Grant/Award Number: 2019M660236; National Natural Science Foundation of China, Grant/Award Numbers: 61901129, 62036003, 81871432, U1808204; The Basque Foundation for Science and from Ministerio de Economia, Industria y Competitividad (Spain) and FEDER, Grant/Award Number: DPI2016-79874-R; the Fundamental Research Funds for the Central Universities, Grant/Award Numbers: 2672018ZYGX2018J079, ZYGX2019Z017; the Sichuan Science and Technology Program, Grant/Award Number: 2019YJ018
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications,
including image classification, game playing, protein structure analysis,
language translation, and content generation. Despite these powerful
applications, there are still many tasks in our daily life that are rather
simple to humans but pose great challenges to AI. These include image and
language understanding, few-shot learning, abstract concepts, and low-energy
cost computing. Thus, learning from the brain is still a promising way that can
shed light on the development of next-generation AI. The brain is arguably the
only known intelligent machine in the universe, which is the product of
evolution for animals surviving in the natural environment. At the behavior
level, psychology and cognitive sciences have demonstrated that human and
animal brains can execute very intelligent high-level cognitive functions. At
the structure level, cognitive and computational neurosciences have unveiled
that the brain has extremely complicated but elegant network forms to support
its functions. Over years, people are gathering knowledge about the structure
and functions of the brain, and this process is accelerating recently along
with the initiation of giant brain projects worldwide. Here, we argue that the
general principles of brain functions are the most valuable things to inspire
the development of AI. These general principles are the standard rules of the
brain extracting, representing, manipulating, and retrieving information, and
here we call them the first principles of the brain. This paper collects six
such first principles. They are attractor network, criticality, random network,
sparse coding, relational memory, and perceptual learning. On each topic, we
review its biological background, fundamental property, potential application
to AI, and future development.Comment: 59 pages, 5 figures, review articl
Whole exome sequencing of insulinoma reveals recurrent T372R mutations in YY1
Functional pancreatic neuroendocrine tumours (PNETs) are mainly represented by insulinoma, which secrete insulin independent of glucose and cause hypoglycaemia. The major genetic alterations in sporadic insulinomas are still unknown. Here we identify recurrent somatic T372R mutations in YY1 by whole exome sequencing of 10 sporadic insulinomas. Further screening in 103 additional insulinomas reveals this hotspot mutation in 30% (34/113) of all tumours. T372R mutation alters the expression of YY1 target genes in insulinomas. Clinically, the T372R mutation is associated with the later onset of tumours. Genotyping of YY1, a target of mTOR inhibitors, may contribute to medical treatment of insulinomas. Our findings highlight the importance of YY1 in pancreatic β-cells and may provide therapeutic targets for PNETs
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