Context-Aware Single-Shot Detector

SSD is one of the state-of-the-art object detection algorithms, and it combines high detection accuracy with real-time speed. However, it is widely recognized that SSD is less accurate in detecting small objects compared to large objects, because it ignores the context from outside the proposal boxes. In this paper, we present CSSD--a shorthand for context-aware single-shot multibox object detector. CSSD is built on top of SSD, with additional layers modeling multi-scale contexts. We describe two variants of CSSD, which differ in their context layers, using dilated convolution layers (DiCSSD) and deconvolution layers (DeCSSD) respectively. The experimental results show that the multi-scale context modeling significantly improves the detection accuracy. In addition, we study the relationship between effective receptive fields (ERFs) and the theoretical receptive fields (TRFs), particularly on a VGGNet. The empirical results further strengthen our conclusion that SSD coupled with context layers achieves better detection results especially for small objects ($+3.2\% {\rm AP}_{@0.5}$ on MS-COCO compared to the newest SSD), while maintaining comparable runtime performance

Herd behaviour in extreme market conditions: The case of the Athens stock exchange

This paper examines herd behaviour in extreme market conditions using data from the Athens Stock Exchange. We test for the presence of herding as suggested by Christie and Huang (1995) and Chang, Cheng, and Khorana (2000). Results based on daily, weekly and monthly data indicate the existence of herd behaviour for the years 1998-2007. Evidence of herd behaviour over daily time intervals is much stronger, revealing the short-term nature of the phenomenon. When the testing period is broken into semi-annual sub-periods, herding is found during the stock market crisis of 1999. Investor behaviour seems to have become more rational since 2002, owing to the regulatory and institutional reforms of the Greek equity market and the intense presence of foreign institutional investors

Voice of the Diaspora: An Analysis of Migrant Voting Behavior

This paper utilizes a unique dataset on votes cast by Czech and Polish migrants in their recent national elections to investigate the impact of institutional, political and economic characteristics on migrants’ voting behavior. The political preferences of migrants are strikingly different from those of their domestic counterparts. In addition, there are also important differences among migrants living in different countries. This paper examines three alternative hypotheses to explain migrant voting behavior: adaptive learning; economic self-selection and political selfselection. The results of the analysis suggest that migrant voting behavior is affected by the institutional environment of the host countries, in particular the tradition of democracy and the extent of economic freedom. In contrast, there is little evidence that differences in migrants’ political attitudes are caused by self-selection based either on economic motives or political attitudes prior to migrating. These results are interpreted as indicating that migrants’ political preferences change in the wake of migration as they adapt to the norms and values prevailing in their surroundings.http://deepblue.lib.umich.edu/bitstream/2027.42/40100/3/wp714.pd

Herding behaviour in extreme market conditions: the case of the Athens Stock Exchange

This paper examines herd behaviour in extreme market conditions using data from the Athens Stock Exchange. We test for the presence of herding as suggested by Christie and Huang (1995) and Chang, Cheng, and Khorana (2000). Results based on daily, weekly and monthly data indicate the existence of herd behaviour for the years 1998-2007. Evidence of herd behaviour over daily time intervals is much stronger, revealing the short-term nature of the phenomenon. When the testing period is broken into semi-annual sub-periods, herding is found during the stock market crisis of 1999. Investor behaviour seems to have become more rational since 2002, owing to the regulatory and institutional reforms of the Greek equity market and the intense presence of foreign institutional investors.

Comparative performance of selected variability detection techniques in photometric time series

Photometric measurements are prone to systematic errors presenting a challenge to low-amplitude variability detection. In search for a general-purpose variability detection technique able to recover a broad range of variability types including currently unknown ones, we test 18 statistical characteristics quantifying scatter and/or correlation between brightness measurements. We compare their performance in identifying variable objects in seven time series data sets obtained with telescopes ranging in size from a telephoto lens to 1m-class and probing variability on time-scales from minutes to decades. The test data sets together include lightcurves of 127539 objects, among them 1251 variable stars of various types and represent a range of observing conditions often found in ground-based variability surveys. The real data are complemented by simulations. We propose a combination of two indices that together recover a broad range of variability types from photometric data characterized by a wide variety of sampling patterns, photometric accuracies, and percentages of outlier measurements. The first index is the interquartile range (IQR) of magnitude measurements, sensitive to variability irrespective of a time-scale and resistant to outliers. It can be complemented by the ratio of the lightcurve variance to the mean square successive difference, 1/h, which is efficient in detecting variability on time-scales longer than the typical time interval between observations. Variable objects have larger 1/h and/or IQR values than non-variable objects of similar brightness. Another approach to variability detection is to combine many variability indices using principal component analysis. We present 124 previously unknown variable stars found in the test data.Comment: 29 pages, 8 figures, 7 tables; accepted to MNRAS; for additional plots, see http://scan.sai.msu.ru/~kirx/var_idx_paper