A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses

Fish-eye lenses are convenient in such applications where a very wide angle of view is needed, but their use for measurement purposes has been limited by the lack of an accurate, generic, and easy-to-use calibration procedure. We hence propose a generic camera model, which is suitable for fish-eye lens cameras as well as for conventional and wide-angle lens cameras, and a calibration method for estimating the parameters of the model. The achieved level of calibration accuracy is comparable to the previously reported state-of-the-art.     

A picture is worth a thousand words: an empirical study on the influence of content visibility on diffusion processes within a virtual world

ABSTRACT

Studying information diffusion and the spread of goods in the real world and in many digital services can be extremely difficult since information about the information flows is challenging to accurately track. How information spreads has commonly been analysed from the perspective of homophily, social influence, and initial seed selection. However, in virtual worlds and virtual economies, the movements of information and goods can be precisely tracked. Therefore, these environments create laboratories for the accurate study of information diffusion characteristics that have been difficult to study in prior research. In this paper, we study how content visibility as well as sender and receiver characteristics, the relationship between them, and the types of multilayer social network layers affect content absorption and diffusion in virtual world. The results show that prior visibility of distributed content is the strongest predictor of content adoption and its further spread across networks. Among other analysed factors, the mechanics of diffusion, content quality, and content adoption by users’ neighbours on the social activity layer had very strong influences on the adoption of new content.     

SERA: a secure energy reliability aware data gathering for sensor networks

Wireless sensor networks are used in many applications in military, ecology, health, and other areas. These applications often include the monitoring of sensitive information making the security issue one of the most important aspects to consider in this field. However, most of protocols optimize for the limited capabilities of sensor nodes and the application specific nature of the networks, but they are vulnerable to serious attacks. In this paper, a Secure Energy and Reliability Aware data gathering protocol (SERA) is proposed, which provides energy efficiency and data delivery reliability as well as security. The proposed protocol’s security was confirmed by a formal verification carried out using the AVISPA tool and analysis of the most common network layer attacks such as selective forwarding, sinkhole, Sybil, wormhole, HELLO flood, and acknowledgment spoofing attacks. Additionally, a visual simulation environment was developed to evaluate the performance of the proposed protocol.     

Averaging in vitro cardiac field potential recordings obtained with microelectrode arrays

Extracellular field potential (FP) recordings with microelectrode arrays (MEAs) from cardiomyocyte cultures offer a non-invasive way of studying the electrophysiological properties of these cells at the population level. Several studies have examined the FP properties of cardiomyocytes of various origins, including stem cell-derived cardiomyocytes. This focus reflects growing importance and interest in the field of MEA. High-quality cardiac FP signals are often difficult to obtain, especially from stem cell-derived cardiomyocyte cultures, which represent an important new field in cardiac electrophysiology. One way to improve the quality of these recordings is to average the cardiac FP signals. To date, however, no studies have examined the effect of averaging on cardiac FP signals. We report here that cardiac FP averaging can yield higher-quality signals than original individual FPs, and therefore promise more accurate detection of different phases and analysis of the cardiac FP signal. Averaged signals improved the signal-to-noise ratio (SNR), and obtaining reliable averages required approximately 50 cardiac cycles. We therefore propose that routine cardiac FP averaging can serve as a tool to compare the effects of different experimental conditions or stimuli on the properties of cardiac FPs.     

Efficient Multisplitting Revisited: Optima-Preserving Elimination of Partition Candidates

We consider multisplitting of numerical value ranges, a task that is encountered as a discretization step preceding induction and also embedded into learning algorithms. We are interested in finding the partition that optimizes the value of a given attribute evaluation function. For most commonly used evaluation functions this task takes quadratic time in the number of potential cut points in the numerical range. Hence, it is a potential bottleneck in data mining algorithms.We present two techniques that speed up the optimal multisplitting task. The first one aims at discarding cut point candidates in a quick linear-time preprocessing scan before embarking on the actual search. We generalize the definition of boundary points by Fayyad and Irani to allow us to merge adjacent example blocks that have the same relative class distribution. We prove for several commonly used evaluation functions that this processing removes only suboptimal cut points. Hence, the algorithm does not lose optimality.Our second technique tackles the quadratic-time dynamic programming algorithm, which is the best schema for optimizing many well-known evaluation functions. We present a technique that dynamically—i.e., during the search—prunes partitions of prefixes of the sorted data from the search space of the algorithm. The method works for all convex and cumulative evaluation functions.Together the use of these two techniques speeds up the multisplitting process considerably. Compared to the baseline dynamic programming algorithm the speed-up is around 50 percent on the average and up to 90 percent in some cases. We conclude that optimal multisplitting is fully feasible on all benchmark data sets we have encountered.     

Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization

Deep Learning provided powerful tools for forecasting financial time series data. However, despite the success of these approaches on many challenging financial forecasting tasks, it is not always straightforward to employ DL-based approaches for highly volatile and non-stationary time financial series. To this end, in this paper, an adaptive input normalization layer that can learn to identify the distribution from which the input data were generated and then apply the most appropriate normalization scheme is proposed. This allows for promptly adapting the input to the subsequent DL model, which can be especially important, given recent findings that hint at the existence of critical learning periods in neural networks. Furthermore, the proposed method operates on a sliding window over the time series allowing for overcoming non-stationary issues that often arise. It is worth noting that the main difference with existing approaches is that the proposed method does not just learn to perform static normalization, e.g., using a fixed set of parameters, but instead it adaptively calculates the most appropriate normalization parameters, significantly improving the robustness of the proposed approach when distribution shifts occur. The effectiveness of the proposed formulation is verified using extensive experiments on three challenging financial time-series datasets.

     

A decision-theoretic approach for model interpretability in Bayesian framework

Machine Learning - A salient approach to interpretable machine learning is to restrict modeling to simple models. In the Bayesian framework, this can be pursued by restricting the model structure...     

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the structures in the data. Most of the current clustering algorithms produce a clustering structure even on data that do not possess such structure. In these cases, the algorithms force a structure in the data instead of discovering one. To avoid false structures in the relations of data, a novel clusterability assessment method called density-based clusterability measure is proposed in this paper. It measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningful insight to the relationships in the data. This is especially useful in time-series data since visualizing the structure in time-series data is hard. The performance of the clusterability measure is evaluated against several synthetic data sets and time-series data sets, which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.      

Effect of precursor particle size and morphology on lithiation of Ni0.6Mn0.2Co0.2(OH)2

In this paper, Ni_0.6Mn_0.2Co_0.2(OH)₂ precursors with several different morphologies and particle sizes are mixed with Li₂CO₃ and heat treated for 5, 7.5 and 10 h. The effects of the precursor properties on the degree of lithiation, electrochemical properties and volumetric capacities of lithiated product are compared. Based on the characterization results, a small (3 μm), narrow span precursor can be lithiated in a short period of time (5 h) and has good initial discharge capacity (185 mA h g^??1) and capacity retention (93% for 55 cycles). In contrast, a large wide-span precursor requires over 10 h for full lithiation. A highly porous precursor can be lithiated faster than traditional large wide-span materials, and has low cation mixing and good crystallinity. However, the volumetric energy density of porous material is low after lithiation compared to the other tested materials. Capacity retention after washing correlated with crystallographic properties of the sample.

Graphic abstract