Enabling Surveillance Cameras to Navigate

Smartphone localization is essential to a wide spectrum of applications in the era of mobile computing. The ubiquity of smartphone mobile cameras and surveillance ambient cameras holds promise for offering sub-meter accuracy localization services thanks to the maturity of computer vision techniques. In general, ambient-camera-based solutions are able to localize pedestrians in video frames at fine-grained, but the tracking performance under dynamic environments remains unreliable. On the contrary, mobile-camera-based solutions are capable of continuously tracking pedestrians; however, they usually involve constructing a large volume of image database, a labor-intensive overhead for practical deployment. We observe an opportunity of integrating these two most promising approaches to overcome above limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing mobile-camera-based and ambient-camera-based systems is non-trivial due to disparity of camera in terms of perspectives, parameters and incorrespondence of localization results. In this article, we propose iMAC, an integrated mobile cameras and ambient cameras based localization system that achieves sub-meter accuracy and enhanced robustness with zero-human start-up effort. The key innovation of iMAC is a well-designed fusing frame to eliminate disparity of cameras including a construction of projection map function to automatically calibrate ambient cameras, an instant crowd fingerprints model to describe user motion patterns, and a confidence-aware matching algorithm to associate results from two sub-systems. We fully implement iMAC on commodity smartphones and validate its performance in five different scenarios. The results show that iMAC achieves a remarkable localization accuracy of 0.68 m, outperforming the state-of-the-art systems by >75%.

Structural basis for small molecule targeting of Doublecortin Like Kinase 1 with DCLK1-IN-1

Doublecortin-like kinase 1 (DCLK1) is an understudied bi-functional kinase with a proven role in tumour growth and development. However, the presence of tissue-specific spliced DCLK1 isoforms with distinct biological functions have challenged the development of effective strategies to understand the role of DCLK1 in oncogenesis. Recently, DCLK1-IN-1 was reported as a highly selective DCLK1 inhibitor, a powerful tool to dissect DCLK1 biological functions. Here, we report the crystal structures of DCLK1 kinase domain in complex with DCLK1-IN-1 and its precursors. Combined, our data rationalises the structure-activity relationship that informed the development of DCLK1-IN-1 and provides the basis for the high selectivity of DCLK1-IN-1, with DCLK1-IN-1 inducing a drastic conformational change of the ATP binding site. We demonstrate that DCLK1-IN-1 binds DCLK1 long isoforms but does not prevent DCLK1’s Microtubule-Associated Protein (MAP) function. Together, our work provides an invaluable structural platform to further the design of isoform-specific DCLK1 modulators for therapeutic intervention.

A New Optimization Algorithm Based on the Fungi Kingdom Expansion Behavior for Antenna Applications

This paper presents a new optimization algorithm based on the behavior of the fungi kingdom expansion (FKE) to optimize the radiation pattern of the array antenna. The immobile mass expansion of the fungi is mimicked in this work as a chaotic behavior with a sinusoidal map function, while the mobile mass expansion is realized by a linear function. In addition, the random germination of the spores is utilized for randomly distributing the variables that are far away from the best solution. The proposed FKE algorithm is applied to optimize the radiation pattern of the antenna array, and then its performance is compared with that of some well-known algorithms. The MATLAB simulation results verify the superiority of the proposed algorithm in solving 20-element antenna array problems such as sidelobe reduction with sidelobe ratio (SLR = 25.6 dB), flat-top pattern with SLR = 23.5 dB, rectangular pattern with SLR = 19 dB, and anti-jamming systems. The algorithm also results in a 100% success rate for all of the mentioned antenna array problems.

Analisa Performa Klastering Data Besar pada Hadoop

Big Data is a collection of data with a large and complex size, consisting of various data types and obtained from various sources, overgrowing quickly. Some of the problems that will arise when processing big data, among others, are related to the storage and access of big data, which consists of various types of data with high complexity that are not able to be handled by the relational model. One technology that can solve the problem of storing and accessing big data is Hadoop. Hadoop is a technology that can store and process big data by distributing big data into several data partitions (data blocks). Problems arise when an analysis process requires all data spread out into one data entity, for example, in the data clustering process. One alternative solution is to do a parallel and scattered analysis, then perform a centralized analysis of the results of the scattered analysis. This study examines and analyzes two methods, namely K-Medoids Mapreduce and K-Modes without Mapreduce. The dataset used is a dataset about cars consisting of 3.5 million rows of data with 400MB distributed in a Hadoop Cluster (consisting of more than one engine). Hadoop has a MapReduce feature, consisting of 2 functions, namely map and reduce. The map function performs a selection to retrieve a key, value pairs, and returns a value in the form of a collection of key value pairs, and then the reduce function combines all key value pairs from several map functions. The results of the cluster quality evaluation are tested using the Silhouette Coefficient testing metric. The K-Medoids MapReduce algorithm for the car dataset gives a silhouette value of 0.99 with a total of 2 clusters.

The organizational principles of de-differentiated topographic maps in somatosensory cortex

Topographic maps are a fundamental feature of cortex architecture in the mammalian brain. One common theory is that the de-differentiation of topographic maps links to impairments in everyday behavior due to less precise functional map readouts. Here, we tested this theory by characterizing de-differentiated topographic maps in primary somatosensory cortex (SI) of younger and older adults by means of ultra-high resolution functional magnetic resonance imaging together with perceptual finger individuation and hand motor performance. Older adults' SI maps showed similar amplitude and size to younger adults' maps, but presented with less representational similarity between distant fingers. Larger population receptive field sizes in older adults' maps did not correlate with behavior, whereas reduced cortical distances between D2 and D3 related to worse finger individuation but better motor performance. Our data uncover the drawbacks of a simple de-differentiation model of topographic map function, and motivate the introduction of feature-based models of cortical reorganization.

The organizational principles of de-differentiated topographic maps

Topographic maps are a fundamental feature of cortex architecture in the mammalian brain. One common theory is that the de-differentiation of topographic maps links to impairments in everyday behavior due to less precise functional map readouts. Here, we tested this theory by characterizing de-differentiated topographic maps in primary somatosensory cortex (SI) of younger and older adults by means of ultra-high resolution functional magnetic resonance imaging together with perceptual finger individuation and hand dexterity. Older adults’ SI maps showed similar amplitude, size, and levels of stimulus-related noise than younger adults’ SI maps, but presented with less representational similarity between distant fingers. Larger population receptive field sizes in older adults’ maps did not correlate with behavior, whereas reduced cortical distances related to better hand dexterity. Our data uncover the drawbacks of a simple de-differentiation model of topographic map function, and motivate the introduction of a feature-based model of cortical reorganization.

A nonlinear dynamical approach to analysis of emotions using EEG signals based on the Poincaré map function and recurrence plots

Dynamic variations of electroencephalogram (EEG) contain significant information in the study of human emotional states. Transient time methods are well suited to evaluate short-term dynamic changes in brain activity. Human affective states, however, can be more appropriately analyzed using chaotic dynamical techniques, in which temporal variations are considered over longer durations. In this study, we have applied two different recurrence-based chaotic schemes, namely the Poincaré map function and recurrence plots (RPs), to analyze the long-term dynamics of EEG signals associated with state space (SS) trajectory of the time series. Both approaches determine the system dynamics based on the Poincaré recurrence theorem as well as the trajectory divergence producing two-dimensional (2D) characteristic plots. The performance of the methods is compared with regard to their ability to distinguish between levels of valence, arousal, dominance and liking using EEG data from the “dataset for emotion analysis using physiological” database. The differences between the levels of emotional feelings were investigated based on the analysis of variance (ANOVA) test and Spearman’s statistics. The results obtained from the RP features distinguish between the emotional ratings with a higher level of statistical significance as compared with those produced by the Poincaré map function. The scheme based on RPs was particularly advantageous in identifying the levels of dominance. Out of the 32 EEG electrodes examined, the RP-based approach distinguished the dominance levels in 23 electrodes, while the approach based on the Poincaré map function was only able to discriminate dominance levels in five electrodes. Furthermore, based on nonlinear analysis, significant correlations were observed over a wider area of the cortex for all affective states as compared with that reported based on the analysis of EEG power bands.

MAPping Kinetochore MAP Function Required for Stabilizing Microtubule Attachments to Chromosomes during Metaphase

In mitosis, faithful chromosome segregation is orchestrated by the dynamic interactions between the spindle microtubules (MTs) emanating from the opposite poles and the kinetochores of chromosomes. However, the precise mechanism that coordinates the coupling of kinetochore components to dynamic MTs has been a long-standing question. Microtubule (MT) associated proteins (MAPs) regulate MT nucleation, dynamics, MT-mediated transport and MT cross-linking in cells. Especially during mitosis, MAPs play an essential role not only in determining the spindle length, position and orientation but also in facilitating robust kinetochore-microtubule (kMT) attachments by linking the kinetochores to spindle MTs efficiently. MT-stability imparted by the MAPs is critical to ensure accurate chromosome segregation. This review primarily focuses on the specific function of non-motor kinetochore MAPs, their recruitment to kinetochores and their MT-binding properties. We also attempt to synthesize and strengthen our understanding of how these MAPs work in coordination with the kinetochore-bound Ndc80 complex (the key component of the MT-binding interface in metaphase and anaphase) to establish stable kMT attachments and control accurate chromosome segregation during mitosis.