Jealousy is not only a way of experiencing love but also a stabilizer of romantic relationships, although morbid romantic jealousy is maladaptive. Being engaged in a formal romantic relationship can tune one's romantic jealousy towards a specific target. Little is known about how the human brain processes romantic jealousy by now. Here, by combining scenario-based imagination and functional MRI, we investigated the behavioral and neural correlates of romantic jealousy and their development across stages (before vs. after being in a formal relationship). Romantic jealousy scenarios elicited activations primarily in the basal ganglia (BG) across stages, and were significantly higher after the relationship was established in both the behavioral rating and BG activation. The intensity of romantic jealousy was related to the intensity of romantic happiness, which mainly correlated with ventral medial prefrontal cortex activation. The increase in jealousy across stages was associated with the tendency for interpersonal aggression. These results bridge the gap between the theoretical conceptualization of romantic jealousy and its neural correlates and shed light on the dynamic changes in jealousy.

Inference of transcriptional regulation, which includes discovering binding sites of a transcription factor (TF), identifying its direct target genes and detecting its dynamical activity, is an important step towards reconstructing transcription network. In this dissertation, I have developed three novel computational methods to tackle this task by integrating large-scale genomic data. All three methods train a probabilistic model using sequence motif, gene expression, TF binding and conservation data. This probabilistic model provides an elegant way to reduce noise in individual data by integrating multiple sources of data. Mathematically, they maximize the joint likelihood of the observable data using expectation-maximization method. The hidden variables in the models represent the identity of a gene (target or not in TRANSMODIS and CompMODEM) and the activity of a TF (in ActivMiner). The EM algorithm iteratively determines these hidden variables and the parameters in the models. The three methods have different purposes. The first two methods called TRANSMODIS and CompMODEM aim to identify binding sites and direct target genes of TFs. TRANMODIS takes into account that target genes of a TF normally share similar sequence motifs in the TF binding regions and gene expression patterns under different conditions. If only a single gene expression or TF binding experiment is available, in addition to the sequence and expression information, CompMODEM considers conservation of TF binding sites in the model because functional regulatory sites tend to be evolutionarily constrained. Both TRANSMODIS and CompMODEM assume the TF of interest is active. When such information is not available, ActivMiner aims to simultaneously infer the dynamic activity of TFs and their regulatory targets. These methods have been successfully applied to multiple species including human, worm and yeast. The studies presented in this dissertation lay the foundation of inferring gene regulatory network, which is a great challenge in the post-genome era. With the fast accumulation of genomic data, these methods will provide a set of useful tools to understand transcriptional mechanisms

Estimation of models for both plant and disturbance dynamics is important in controller design applications which especially focus on the disturbance and vibration rejection. Several methods for low order model estimation on the basis of the closed-loop data exist in the literature, but fail to address the simultaneous estimation of low order models of both plant and disturbance dynamics. This dissertation contributes to the development of a new methodology to extend the results to low order disturbance model estimation, and apply these techniques to the control problems for disturbance rejection. In addition to the control relevant estimation problem, this dissertation also provides new tools for feedforward based disturbance rejection found in Active Noise Control (ANC) systems. We focus on the feedforward control algorithms that are one of the most popular methods to cancel low frequency sound where passive methods are ineffective. This dissertation shows that the feedforward filter design can also be seen as a model matching problem with the system model approximated on the basis of the expansion of orthonormal basis functions. Therefore, existing results on generalized FIR filters are exploited to provide feedforward compensation with the advantage of including the prior information of the system dynamics in the tapped delay line of the filter. It has the same linear parameter structure as FIR filter which is favorable for adaptation process. In the case that the acoustic coupling can not be neglected in the process of designing the feedforward filter, a dual-Youla parametrization is introduced and applied to estimate the possible perturbation of the feedforward filter and the robust stability of the closed loop system is enforced during the design of feedforward filter for active noise cancellation

The carrier emission efficiency of light emitting diodes is of fundamental importance for many technological applications, including the performance of GaN and other semiconductor photocathodes. We have measured the evolution of the emitted carriers and the associated transient electric field after femtosecond laser excitation of n-type GaN single crystals. These processes were studied using sub-picosecond, ultrashort, electron pulses and explained by means of a "three-layer" analytical model. We find that for pump laser intensities on the order of 1011 W/cm2, the electrons that escaped from the crystal surface have a charge of ∼2.7 pC and a velocity of ∼1.8 μm/ps. The associated transient electrical field evolves at intervals ranging from picoseconds to nanoseconds. These results provide a dynamic perspective on the photoemission properties of semiconductor photocathodes.