Abstract
Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail supply chains. For profitable retail businesses, accurate sales forecasting is crucial in organizing and planning purchasing, production, transportation and labor force. Retail sales series belong to a special type of time series that typically contain strong trend and seasonal patterns, presenting challenges in developing effective forecasting models. This paper compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. An approach based on cross-validation is used to identify automatically appropriate state space and ARIMA models. The forecasting performance of these models is also compared by examining the out-of-sample forecasts. The results indicate that the overall out-of-sample forecasting performance of ARIMA models evaluated via RMSE, MAE and MAPE is better than state space models. The performance of both forecasting methodologies in producing forecast intervals was also evaluated and the results indicate that ARIMA produces slightly better coverage probabilities than state space models for the nominal 95% forecast intervals. For the nominal 80% forecast intervals the performance of state space models is slightly better.

Abstract
Aggressive marketing causes rapid changes in consumer behavior and some significant impact in the retail business. In this context, the sales forecasting at the SKU level can help retailers to become more competitive by reducing inventory investment and distribution costs. Sales forecasts are often obtained combining basic univariate forecasting models with empirical judgment. However, more effective forecasting methods can be obtained by incorporating promotional information, including price, percentage of discount (direct discount or loyalty card discount), calendar events and weekend indicators not only from the focal product but also from its competitors. To deal with the high dimensionality of the variable space, we propose a two-stage LASSO regression to select optimal predictors and estimate the model parameters. At the first stage, only focal SKUs promotional explanatory variables are included in the Autoregressive Distributed Lag model. At the second stage, the in-sample forecast errors from the first stage are regressed on the explanatory variables from the other SKUs in the same category with the focal SKU, and to use that information more effectively three different approaches were considered: select the five top sales SKUs, include all raw promotional information, and preprocess raw information using Principal Component Analysis. The empirical results obtained using daily data from a Portuguese retailer show that the inclusion of promotional information from SKUs in the same category may improve the forecast accuracy and that better overall forecasting results may be obtained if the best model for each SKU is selected.

Abstract
Sales forecasts gained more importance in the retail industry with the increasing of promotional activity, not only because of the considerable portion of products under promotion but also due to the existence of promotional activities, which boost product sales and make forecasts more difficult to obtain. This study is performed with real data from a Portuguese consumer goods retail company, from January 2012 until April 2015. To achieve the purpose of the study, dynamic regression is used based on information of the focal product and its competitors, with seasonality modelled using Fourier terms. The selection of variables to be included in the model is done based on the lowest value of AIC in the train period. The forecasts are obtained for a test period of 30 weeks. The forecasting models overall performance is analyzed for the full period and for the periods with and without promotions. The results show that our proposed dynamic regression models with price and promotional information of the focal product generate substantially more accurate forecasts than pure time series models for all periods studied.

Abstract
This letter presents a novel high-gain four-quadrant analog multiplier using only n-type enhancement indium-gallium-zinc-oxide thin-film-transistors. The proposed circuit improves the gain by using an active load with positive feedback. A Gilbert cell with a diode-connected load is also presented for comparison purposes. Both circuits were fabricated on glass at low temperature (200 degrees C) and were successfully characterized at room temperature under normal ambient conditions, with a power supply of 15 V and 4-pF capacitive load. The novel circuit has shown a gain improvement of 7.2 dB over the Gilbert cell with the diode-connected load. Static linearity response, total harmonic distortion, frequency response, and power consumption are reported. This circuit is an important signal processing building block in large-area sensing and readout systems, specially if data communication is involved.

Abstract
Security is becoming an essential problem for integrated circuits (ICs). Various attacks, such as reverse engineering and dumping on-chip data, have been reported to undermine IC security. IEEE 1149.1, also known as JTAG, is primarily used for IC manufacturing test but inevitably provides a "backdoor" that can be exploited to attack ICs. Encryption has been used extensively as an effective mean to protect ICs through authentication, but a few weaknesses subsist, such as key leakage. Signature-based techniques ensure security using a database that includes known attacks, but fail to detect attacks that are not contained by the database. To overcome these drawbacks, a two-layer learning-based protection scheme is proposed. Specifically, the scheme monitors the execution of JTAG instructions and uses support vector machines (SVM) to identify abnormal instruction sequences. The use of machine learning enables the detection of unseen attacks without the need for key-based authentication. The experiments based on the OpenSPARC T2 platform demonstrate that the proposed scheme improves the accuracy of detecting unseen attacks by 50% on average when compared to previous work.

Abstract
This paper presents a study concerning the role of channel length scaling on IGZO TFT technology benchmark parameters, which are fabricated at temperatures not exceeding 180 degrees C. The parameters under investigation are unity current-gain cutoff frequency, intrinsic voltage-gain, and on-resistance of the bottom-gate IGZOTFTs. As the channel length varies from 160 to 3 mu m, the measured cutoff frequency increases from 163 kHz to 111.5 MHz, which is a superior value compared to the other competing low-temperature thin-film technologies, such as organic TFTs. On the other hand, for the same transistor dimensions, the measured intrinsic voltage-gain is changing from 165 to 5.3 and the on-resistance is decreasing from 1135.6 to 26.1 k Omega. TFTs with smaller channel length (3 mu m) have shown a highly negative turn-on voltage and hump in the subthreshold region, which can be attributed to short channel effects. The results obtained here, together with their interpretation based on device physics, provide crucial information for accurate IC design, enabling an adequate selection of device dimensions to maximize the performance of different circuit building blocks assuring the multifunctionality demanded by system-on-panel concepts.