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A micro-ring-resonator (MRR) optical biosensor based on the evanescent field sensing mechanism has been extensively studied due to its high sensitivity and compact device size. However, a suitable on-chip integrated spectrometer device has to be demonstrated for the lab-on-a-chip applications, which can read the resonance wavelength shift from MRR biosensors based on minuscule changes in refractive index. In this paper, we demonstrated the design and experimental results of the near-infrared lab-on-a-chip optical biosensor platform that monolithically integrates the MRR and the on-chip spectrometer on the silicon-on-insulator (SOI) wafer, which can eliminate the external optical spectrum analyzer for scanning the wavelength spectrum. The symmetric add-drop MRR biosensor is designed to have a free spectral range (FSR) of ~19 nm, and a bulk sensitivity of ~73 nm/RIU; then the drop-port output resonance peaks are reconstructed from the integrated spatial-heterodyne Fourier transform spectrometer (SHFTS) with the spectral resolution of ~3.1 nm and bandwidth of ~50 nm, which results in the limit of detection of 0.042 RIU. The MRR output spectrum with air- and water-claddings are measured and reconstructed from the MRR-SHFTS integrated device experimentally to validate the wavelength shifting measurement., Comment: 23 pages, 9 figures including supplementary

Optical I/O plays a crucial role in the lifespan of lab-on-a-chip systems, from preliminary testing to operation in the target environment. However, due to the precise alignments required, efficient and reliable fiber-to-chip connections remain challenging, yielding inconsistent test results and unstable packaged performance. To overcome this issue, for use in single mode on-chip systems, we propose the incorporation of area-enlarged grating couplers working in conjunction with multimode fibers. This combination enables simpler, faster, and more reliable connections than the traditional small area grating coupler with single-mode fiber. In this work, we experimentally demonstrate a 3dB in-plane (X, Y) spatial tolerance of (10.2 {\mu}m, 17.3 {\mu}m) for the large area configuration, being at least (2.49, 3.33) times that of the small area one, and agreeing well with theoretical calculations. The simple concept is readily applicable to a range of photonic systems where cheaper more robust optical I/O is desired., Comment: 11 pages, 5 figures

In this paper, we propose to use pre-trained features from end-to-end ASR models to solve speech sentiment analysis as a down-stream task. We show that end-to-end ASR features, which integrate both acoustic and text information from speech, achieve promising results. We use RNN with self-attention as the sentiment classifier, which also provides an easy visualization through attention weights to help interpret model predictions. We use well benchmarked IEMOCAP dataset and a new large-scale speech sentiment dataset SWBD-sentiment for evaluation. Our approach improves the-state-of-the-art accuracy on IEMOCAP from 66.6% to 71.7%, and achieves an accuracy of 70.10% on SWBD-sentiment with more than 49,500 utterances.

We study an optimal control problem arising from a generalization of rock-paper-scissors in which the number of strategies may be selected from any positive odd number greater than 1 and in which the payoff to the winner is controlled by a control variable $\gamma$. Using the replicator dynamics as the equations of motion, we show that a quasi-linearization of the problem admits a special optimal control form in which explicit dynamics for the controller can be identified. We show that all optimal controls must satisfy a specific second order differential equation parameterized by the number of strategies in the game. We show that as the number of strategies increases, a limiting case admits a closed form for the open-loop optimal control. In performing our analysis we show necessary conditions on an optimal control problem that allow this analytic approach to function., Comment: 23 pages, 6 figures

Poker is a family of card games that includes many variations. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representation. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold'em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competitive player against human experts. The contributions of this paper include: (1) a novel representation for poker games, extendable to different poker variations, (2) a CNN based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that significantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players., Comment: 8 pages

We use a control framework to analyze the digital vendor's profit maximization problem. The vendor captures market share by focusing costly effort on post-launch product maintenance, which influences user perception of the product and drives a revenue stream associated with product use. Our theoretical results show necessary and sufficient conditions for product maintenance to decline over a product's life-cycle, thus showing conditions when Lehman's 7th law of software evolution holds. We also numerically illustrate control paths under different market conditions., Comment: Updated version with improved proofs and sufficiency condition discussion

Major advances in Question Answering technology were needed for IBM Watson to play Jeopardy! at championship level -- the show requires rapid-fire answers to challenging natural language questions, broad general knowledge, high precision, and accurate confidence estimates. In addition, Jeopardy! features four types of decision making carrying great strategic importance: (1) Daily Double wagering; (2) Final Jeopardy wagering; (3) selecting the next square when in control of the board; (4) deciding whether to attempt to answer, i.e., "buzz in." Using sophisticated strategies for these decisions, that properly account for the game state and future event probabilities, can significantly boost a players overall chances to win, when compared with simple "rule of thumb" strategies. This article presents our approach to developing Watsons game-playing strategies, comprising development of a faithful simulation model, and then using learning and Monte-Carlo methods within the simulator to optimize Watsons strategic decision-making. After giving a detailed description of each of our game-strategy algorithms, we then focus in particular on validating the accuracy of the simulators predictions, and documenting performance improvements using our methods. Quantitative performance benefits are shown with respect to both simple heuristic strategies, and actual human contestant performance in historical episodes. We further extend our analysis of human play to derive a number of valuable and counterintuitive examples illustrating how human contestants may improve their performance on the show.

We construct a two species evolutionary game model of an online society consisting of ordinary users and behavior enforcers (moderators). Among themselves, moderators play a coordination game choosing between being "positive" or "negative" (or harsh) while ordinary users play prisoner's dilemma. When interacting, moderators motivate good behavior (cooperation) among the users through punitive actions while the moderators themselves are encouraged or discouraged in their strategic choice by these interactions. We show the following results: (i) We show that the $\omega$-limit set of the proposed system is sensitive both to the degree of punishment and the proportion of moderators in closed form. (ii) We demonstrate that the basin of attraction for the Pareto optimal strategy $(\text{Cooperate},\text{Positive})$ can be computed exactly. (iii) We demonstrate that for certain initial conditions the system is self-regulating. These results partially explain the stability of many online users communities such as Reddit. We illustrate our results with examples from this online system., Comment: 8 pages, 4 figures, submitted to 2012 ASE Conference on Social Informatics