Nominal Rigidities and The Real Effects of Monetary Policy in a Structural VAR Model

The paper proposes an empirical VAR for the UK open economy in order to measure the effects of monetary policy shocks from 1981 to 2003. The identification of the VAR structure is based on short-run restrictions that are consistent with the general implications of a New Keynesian model. The identification scheme used in the paper is successful in identifying monetary policy shocks and solving the puzzles and anomalies regarding the effects of monetary policy shocks. The estimated dynamic impulse responses and the forecast error variance decompositions show a consistency with the New Keynesian approach and other available theories.Structural VAR; Nominal Rigidities; Monetary Policy Shocks; New Keynesian Theory

Combining Language and Vision with a Multimodal Skip-gram Model

We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning.Comment: accepted at NAACL 2015, camera ready version, 11 page

Algorithms Related to Triangle Groups

Given a finite index subgroup of \PSL_2(\Z), one can talk about the different properties of this subgroup. These properties have been studied extensively in an attempt to classify these subgroups. Tim Hsu created an algorithm to determine whether a subgroup is a congruence subgroup by using permutations \cite{hsu}. Lang, Lim, and Tan also created an algorithm to determine if a subgroup is a congruence subgroup by using Farey Symbols \cite{llt}. Sebbar classified torsion-free congruence subgroups of genus 0 \cite{sebbar}. Pauli and Cummins computed and tabulated all congruence subgroups of genus less than 24 \cite{ps}. However, there are still some problems left to be solved. In the first part of this thesis, we will use the concept of Farey Symbols and bipartite cuboid graphs to determine when two subgroups of \PSL_2(\Z) are in the same conjugacy class in \PSL_2(\Z). We implemented this algorithm, and other related algorithms, with SageMath \cite{baowebsite}. In the second part of the thesis, we will extend these ideas to general triangle groups. Specifically, we will classify some small index conjugacy classes of subgroups of the triangle group $\overline{\triangle}(2,4,6)$

Design of a Configurable 4-Channel Analog Front-End for EEG Signal Acquisition on 180nm CMOS Process

In this work, a 4-channel Analog Front-End (AFE) circuit has been proposed for EEG signal recording. For EEG recording systems, the AFE may handle a wide range of sensor inputs with high input impedance, adjustable gain, low noise, and wide bandwidth. The buffer or current-to-voltage converter block (BCV), which can be set to operate as a buffer or a current-to-voltage converter circuit, is positioned between the electrode and the main amplifier stages of the AFE to achieve high input impedance and work with sensor signal types. A chopper capacitively-coupled instrumentation amplifier (CCIA) is positioned after the BCV as the main amplifier stage of the AFE to reduce input-referred noise and balance the impedance of the overall AFE system. A programmable gain amplifier (PGA) is the third stage of the AFE that allows the overall gain of the AFE to be adjusted. The suggested AFE operates in a wide frequency range of 0.5 Hz to 2 kHz with a high input impedance bigger than 2TΩ, and it is constructed and simulated using a 180nm CMOS process. With the lowest 100-dB CMRR and low input-referred noise of 1.8 µVrms, the AFE can achieve low noise efficiency. EEG signals can be acquired with this AFE system, which is very useful for detecting epilepsy and seizures