Dynamic Stability and Thermodynamic Characterization in an Enzymatic Reaction at the Single Molecule Level

In this work we study, at the single molecular level, the thermodynamic and dynamic characteristics of an enzymatic reaction comprising a rate limiting step. We investigate how the stability of the enzyme-state stationary probability distribution, the reaction velocity, and its efficiency of energy conversion depend on the system parameters. We employ in this study a recently introduced formalism for performing a multiscale thermodynamic analysis in continuous-time discrete-state stochastic systems.Comment: In Press in Physica

Learning Personalized End-to-End Goal-Oriented Dialog

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a Profile Model which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a Preference Model captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the Personalized MemN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.Comment: Accepted by AAAI 201

Beampattern-Based Tracking for Millimeter Wave Communication Systems

We present a tracking algorithm to maintain the communication link between a base station (BS) and a mobile station (MS) in a millimeter wave (mmWave) communication system, where antenna arrays are used for beamforming in both the BS and MS. Downlink transmission is considered, and the tracking is performed at the MS as it moves relative to the BS. Specifically, we consider the case that the MS rotates quickly due to hand movement. The algorithm estimates the angle of arrival (AoA) by using variations in the radiation pattern of the beam as a function of this angle. Numerical results show that the algorithm achieves accurate beam alignment when the MS rotates in a wide range of angular speeds. For example, the algorithm can support angular speeds up to 800 degrees per second when tracking updates are available every 10 ms.Comment: 6 pages, to be published in Proc. IEEE GLOBECOM 2016, Washington, D.C., US

Quantum state engineering with flux-biased Josephson phase qubits by Stark-chirped rapid adiabatic passages

In this paper, the scheme of quantum computing based on Stark chirped rapid adiabatic passage (SCRAP) technique [L. F. Wei et al., Phys. Rev. Lett. 100, 113601 (2008)] is extensively applied to implement the quantum-state manipulations in the flux-biased Josephson phase qubits. The broken-parity symmetries of bound states in flux-biased Josephson junctions are utilized to conveniently generate the desirable Stark-shifts. Then, assisted by various transition pulses universal quantum logic gates as well as arbitrary quantum-state preparations could be implemented. Compared with the usual PI-pulses operations widely used in the experiments, the adiabatic population passage proposed here is insensitive the details of the applied pulses and thus the desirable population transfers could be satisfyingly implemented. The experimental feasibility of the proposal is also discussed.Comment: 9 pages, 4 figure

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca