DBSCAN Revisited, Revisited

Schubert, Erich; Sander, Jöerg; Ester, Martin; Kriegel, Hans Peter; Xu, Xiaowei

doi:10.1145/3068335

Cited by 1,810 publications

(545 citation statements)

References 34 publications

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“…2 Aii), additional QC steps were required. The traces were first 159 clustered using DBSCAN [16,17], and each cluster median was compared against white 160 noise of the same mean and standard deviation (KS-test), and rejected as artifact if it 161 was not significantly p < 0.05 different from Gaussian white noise of matched mean and 162 variance, e.g. Fig.…”

Section: /32mentioning

confidence: 99%

On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging

Ledochowitsch

Huang

Knoblich

et al. 2019

Preprint

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Multiphoton calcium imaging is commonly used to monitor the spiking of large populations of neurons. Recovering action potentials from fluorescence necessitates calibration experiments, often with simultaneous imaging and cell-attached recording.Here we performed calibration for imaging conditions matching those of the Allen Brain Observatory. We developed a novel crowd-sourced, algorithmic approach to quality control. Our final data set was 50 recordings from 35 neurons in 3 mouse lines. Our calibration indicated that 3 or more spikes were required to produce consistent changes in fluorescence. Moreover, neither a simple linear model nor a more complex biophysical model accurately predicted fluorescence for small numbers of spikes (1-3). We observed increases in fluorescence corresponding to prolonged depolarizations, particularly in Emx1-IRES-Cre mouse line crosses. Our results indicate that deriving spike times from fluorescence measurements may be an intractable problem in some mouse lines.Introduction 1 Systems neuroscience demands a steady increase in the number of simultaneously 2 recorded neurons. Over the last five decades, the number of recorded neurons has 3 doubled approximately every 7 years, mimicking Moore's Law [1]. Electrophysiology has 4 been the de facto gold standard for measuring neural activity for more than 80 years, 5 starting with pioneering work on the squid giant axon by Young, Curtis, Cole, Hodgkin, 6 and Huxley [2][3][4]. However, when the express goal is to push the envelope on the 7 number of simultaneously recorded neurons in vivo, intracellular electrophysiology is 8 impractical, and extracellular electrophysiology has other limitations, e.g. that the 9 precise origin of the signal (positional, genetic, morphological, and physiological details 10 of the generating neuron) are typically unknown [5]. Simultaneous recordings from large 11 numbers of genetically-identified neurons or experiments that necessitate recording from 12 the same neurons repeatedly over many days are difficult to achieve with 13 electrophysiology and are more tractable with imaging techniques. 14 One commonly used imaging technique is 2-photon calcium imaging. Anticipated15 from first principles by Maria Göppert-Mayer [6, 7], and experimentally pioneered by 16 Denk, Strickler, and Webb [8], 2-photon imaging emerged as a complementary technique 17 for the acquisition of neural activity with single-cell resolution from hundreds of neurons 18 in the living brain. Modern technology has enabled the development of genetically 19 1/32 encoded calcium indicators (GECIs), which can be introduced via viral transfection or 20transgenic strategy, and are expressed in a promoter-specific manner. The development 21 of GCaMP6 in particular, with its promise of elusive single spike sensitivity [9], heralded 22 a new era of massive optophysiological surveys [10], recording large numbers of neurons 23 from specific, genetically distinct populations. 24Unfortunately, the fluorescence of a calcium indicator is only indirectly li...

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Section: /32mentioning

confidence: 99%

On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging

Ledochowitsch

Huang

Knoblich

et al. 2019

Preprint

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“…In cases where such noisy data exists, it is necessary to use more robust methods such as Gaussian mixture models (GMMs) 48 or hierarchical density-based spatial clustering of applications with noise (DBSCAN). 49 Some machine learning models do not perform well with noisy data compared with others such as ensemble methods. 50 Ensemble methods aggregate prediction results through multiple models (eg, decision trees) and then average the differences among these multiple models.…”

Section: Missing Data Tolerancementioning

confidence: 99%

Securing Internet of Things (IoT) with machine learning

Zeadally

Tsikerdekis

2019

Int J Communication

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Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the emergence of the Internet of Things (IoT) paradigm. Today, several billions of Internet-connected devices are part of the IoT ecosystem.IoT devices have become an integral part of the information and communication technology (ICT) infrastructure that supports many of our daily activities. The security of these IoT devices has been receiving a lot of attention in recent years. Another major recent trend is the amount of data that is being produced every day which has reignited interest in technologies such as machine learning and artificial intelligence. We investigate the potential of machine learning techniques in enhancing the security of IoT devices. We focus on the deployment of supervised, unsupervised learning techniques, and reinforcement learning for both host-based and network-based security solutions in the IoT environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them so that they can better protect IoT devices. KEYWORDSattack, internet of things, machine learning, security

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“…The implementation of the final clustering is omitted at this point and widely described in literature [36]. With the original algorithm implemented, adaptions only have to be made in the core point definition and neighborhood metrics.…”

Section: Modified Core Point Definition For Dbstacmentioning

confidence: 99%

Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities

Lücken

Voss

Schreier

et al. 2018

Journal of Advanced Transportation

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Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion, and air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding management systems. However, the lack of real-time traffic flow information with a city-wide coverage is a major limiting factor for an optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications, distributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic sensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light system, are used for multilane traffic participant detection and classification. Application of these sensors in time-varying reflective environments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the dissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization with clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and decentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements and several European real-world installations.

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DBSCAN Revisited, Revisited

Cited by 1,810 publications

References 34 publications

On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging

On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging

Securing Internet of Things (IoT) with machine learning

Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities

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