Konsumsi dan Inflasi Indonesia

This study aims to analyze and observes (1) the effect of inflation, disposable income, interest rates and the previous period consumption to inflation in Indonesia. (2) the effect of consumption, interest rate and excange rates and the money supply to Indonesia Inflation. The type of research is descriptive and associative studies. The type of data that used is documentary data, the source of data is secondary data sources. data is in the form of time series from first quarter of 2000 – to fourth quarter of 2010. This study utilize a simultaneous equation model analysis by means of two stages Least Squared method (TSLS). Endogenous variable in this study is the consumption and inflation. While the eksogen variable is the excange rate,money supply,interest rates disposable income, and previous period consumption. The study yields conclusion that (1)inflation,disposable income, interest rates and the previous period consumption have a significant effect on consumtion in Indonesia. In a way that. If there is a decrease of inflation, disposable income and previous consumption have increased the consumption in Indonesia will increase. Conversely, if there is an increasing in consumtion, excange rate (depreciation) and the money supply while the interest rates go down then it will impact an increase in inflation in Indonesia. Vice versa if there is a decrease of consumption, exchange rate (appreciation) and the money supply, while the interest rates rise it will have an impact on reducing Indonesia inflation

Density of States for a Specified Correlation Function and the Energy Landscape

The degeneracy of two-phase disordered microstructures consistent with a specified correlation function is analyzed by mapping it to a ground-state degeneracy. We determine for the first time the associated density of states via a Monte Carlo algorithm. Our results are described in terms of the roughness of the energy landscape, defined on a hypercubic configuration space. The use of a Hamming distance in this space enables us to define a roughness metric, which is calculated from the correlation function alone and related quantitatively to the structural degeneracy. This relation is validated for a wide variety of disordered systems.Comment: Accepted for publication in Physical Review Letter

Flexible constrained sampling with guarantees for pattern mining

Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.Comment: Accepted for publication in Data Mining & Knowledge Discovery journal (ECML/PKDD 2017 journal track

Neural Network Compression for Noisy Storage Devices

Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices. Although NN model compression has made significant progress, there has been considerably less investigation in the actual physical storage of NN parameters. Conventionally, model compression and physical storage are decoupled, as digital storage media with error correcting codes (ECCs) provide robust error-free storage. This decoupled approach is inefficient, as it forces the storage to treat each bit of the compressed model equally, and to dedicate the same amount of resources to each bit. We propose a radically different approach that: (i) employs analog memories to maximize the capacity of each memory cell, and (ii) jointly optimizes model compression and physical storage to maximize memory utility. We investigate the challenges of analog storage by studying model storage on phase change memory (PCM) arrays and develop a variety of robust coding strategies for NN model storage. We demonstrate the efficacy of our approach on MNIST, CIFAR-10 and ImageNet datasets for both existing and novel compression methods. Compared to conventional error-free digital storage, our method has the potential to reduce the memory size by one order of magnitude, without significantly compromising the stored model's accuracy.Comment: 19 pages, 9 figure