We propose a new high-quality and efficient single-image upscaling technique that extends existing example-based super-resolution frameworks. In our approach we do not rely on an external example database or use the whole input image as a source for example patches. Instead, we follow a local self-similarity assumption on natural images and extract patches from extremely localized regions in the input image. This allows us to reduce considerably the nearest-patch search time without compromising quality in most images. Tests, that we perform and report, show that the local self-similarity assumption holds better for small scaling factors where there are more example patches of greater relevance. We implement these small scalings using dedicated novel nondyadic filter banks, that we derive based on principles that model the upscaling process. Moreover, the new filters are nearly biorthogonal and hence produce high-resolution images that are highly consistent with the input image without solving implicit back-projection equations. The local and explicit nature of our algorithm makes it simple, efficient, and allows a trivial parallel implementation on a GPU. We demonstrate the new method ability to produce high-quality resolution enhancement, its application to video sequences with no algorithmic modification, and its efficiency to perform real-time enhancement of low-resolution video standard into recent high-definition formats.

/pdf/image-and-video-upscaling-from-local-self-examples-4invcledki.pdf

Image and video upscaling from local self-examples

Measuring Business Cycles

This article presents a review of some modern approaches to trend extraction for one-dimensional time series, which is one of the major tasks of time series analysis. The trend of a time series is usually defined as a smooth additive component which contains information about the time series global change, and we discuss this and other definitions of the trend. We do not aim to review all the novel approaches, but rather to observe the problem from different viewpoints and from different areas of expertise. The article contributes to understanding the concept of a trend and the problem of its extraction. We present an overview of advantages and disadvantages of the approaches under consideration, which are: the model-based approach (MBA), nonparametric linear filtering, singular spectrum analysis (SSA), and wavelets. The MBA assumes the specification of a stochastic time series model, which is usually either an autoregressive integrated moving average (ARIMA) model or a state space model. The nonparametri...

https://www.census.gov/srd/papers/pdf/rrs2008-03.pdf

A Review of Some Modern Approaches to the Problem of Trend Extraction

According to rational expectation models, uninformed or liquidity trading make market price volatility rise. This paper sets out to analyse the impact of herding, which may be interpreted as one of the components of uninformed trading, on the volatility of the Spanish stock market. Herding is examined at the intraday level, considered the most reliable sampling frequency for detecting this type of investor behavior, and measured using the Patterson and Sharma (Working Paper, University of Michigan–Dearborn, 2006) herding intensity measure. Different volatility measures (historical, realized and implied) are employed. The results confirm that herding has a direct linear impact on volatility for all of the volatility measures considered, although the corresponding intensity is not always the same. In fact, herding variables seem to be useful in volatility forecasting and therefore in decision making when volatility is considered a key factor.

/pdf/does-herding-affect-volatility-implications-for-the-spanish-1kt3unafyh.pdf

Does herding affect volatility? Implications for the Spanish stock market

Forecasting global equity market volatilities

The consequences of the crisis have been mainly analyzed at national/international levels, neglecting its differential effects on local areas. Notwithstanding the international character of the Great Recession, the different local structural features might have influenced the economic and social impact of the crisis, determining effects on the resilience and recovery chance. In this paper, we focus on the role of different territorial indicators by looking at how their relevance has changed during the recent crisis at provincial level. Our aim is threefold. First, we identify the strategic territorial elements which might be particularly relevant in ensuring a greater local absorption capacity. Second, we test whether the crisis has increased the role of specific local factors. In doing that, we use different measures of performance (GDP, exports and employment). Third, we control for macro-regional characteristics (i.e., North versus South) and local specific effects through a panel analysis of the dynamics of the performance variables in three sub-periods over the years 2000–2014. For that purpose, we use a multidimensional dataset for Italian provinces aiming at estimating the empirical relations between the set of territorial elements (i.e., the components of the so-called territorial capital) and the change in local performance. The results stress the role of some localized variables (i.e., human and cultural/natural/capital, agglomeration economies) over the whole period and the changing role of other territorial dimensions (material and non-material) according to the performance variable considered and during the crisis.

Territorial capital and growth over the Great Recession: a local analysis for Italy

The conventional dyadic multiresolution analysis constructs a succession of frequency intervals in the form of (π/2 j , π/2j-1);j = 1, 2, …, j of which the bandwidths are halved repeatedly in the descent from high frequencies to low frequencies. Whereas this scheme provides an excellent framework for encoding and transmitting signals with a high degree of data compression, it is less appropriate to statistical data analysis. A non-dyadic mixed-radix wavelet analysis which allows the wave bands to be defined more flexibly than in the case of a conventional dyadic analysis is described. The wavelets that form the basis vectors for the wave bands are derived from the Fourier transforms of a variety of functions that specify the frequency responses of the filters corresponding to the sequences of wavelet coefficients

/pdf/non-dyadic-wavelet-analysis-cjewvyh064.pdf

Non-Dyadic Wavelet Analysis

Volatility co-movements: a time scale decomposition analysis

A wavelet analysis of US fiscal sustainability

This paper analyses the differential impact of several territorial determinants of the economic performance of Italian provinces (NUTS 3 level). as measured by per capita GDP, export and employment growth from 1999 to 2014. It covers both the pre‐crisis and the crisis period and stresses the role of geographical proximity in shaping local performance over a wide set of explanatory variables. In order to do so, we employ, firstly, a spatial Durbin model which enables us to discriminate between direct and indirect effects and to highlight the possible contagion or crowding‐out spatial effects for each territorial dimension affecting growth. Then, we extend the analysis by allowing for the possibility of two regimes (pre‐crisis and post‐crisis). The performance of the provinces before and during the crisis relates to specific territorial components and geographic proximity appears to influence differently the results and their interpretation. Este articulo analiza el impacto diferencial de varios determinantes territoriales del desempeno economico de las provincias italianas (nivel NUTS 3), medidos por el PIB per capita y el crecimiento de las exportaciones y del empleo entre 1999 y 2014. El estudio abarca tanto el periodo anterior a la crisis como el periodo de crisis y hace hincapie en el papel de la proximidad geografica en la configuracion del desempeno local mediante un amplio conjunto de variables explicativas. Para ello se emplea en primer lugar un modelo espacial de Durbin, que permite discriminar entre efectos directos e indirectos y poner de relieve los posibles efectos de contagio o de desplazamiento espacial para cada dimension territorial que afecta al crecimiento. A continuacion se amplia el analisis, permitiendo la posibilidad de dos regimenes (pre‐crisis y post‐crisis). El desempeno de las provincias antes y durante la crisis se relaciona con componentes territoriales especificos y la proximidad geografica parece influir de manera diferente en los resultados y en su interpretacion. 本稿では、1999年~2014年の一人当たりGDP、輸出、雇用の増加で測定したイタリアの (NUT3レベルの)県 (province)の経済動向の、地域的な決定因子の変動的なインパクトを分析する。経済危機前と経済危機の期間の両方を分析し、地域の動向を広範な説明変数のセットで形成する際の地理的近接性の役割を強調する。そのため、はじめに直接的な効果と間接的な効果を区別し、コンテイジョンの可能性、または成長に影響するそれぞれの地域ディメンションに対する空間的なクラウディングアウト効果を強調させることができる、空間ダービンモデルを使用する。次に、2つのレジーム(危機前と危機後)の可能性を考慮に入れて分析を拡大する。危機の前と危機の間の県の経済動向は地域の特定の構成要素に関連し、地理的近接性は結果とその解釈に様々な影響を与えると考えられる。

Iolanda Lo Cascio

Papers

Territorial capital and growth over the Great Recession: a local analysis for Italy

Non-Dyadic Wavelet Analysis

Volatility co-movements: a time scale decomposition analysis

A wavelet analysis of US fiscal sustainability

Territorial determinants and NUTS 3 regional performance: A spatial analysis for Italy across the crisis