Agent Behavior Prediction and Its Generalization Analysis

Machine learning algorithms have been applied to predict agent behaviors in real-world dynamic systems, such as advertiser behaviors in sponsored search and worker behaviors in crowdsourcing. The behavior data in these systems are generated by live agents: once the systems change due to the adoption of the prediction models learnt from the behavior data, agents will observe and respond to these changes by changing their own behaviors accordingly. As a result, the behavior data will evolve and will not be identically and independently distributed, posing great challenges to the theoretical analysis on the machine learning algorithms for behavior prediction. To tackle this challenge, in this paper, we propose to use Markov Chain in Random Environments (MCRE) to describe the behavior data, and perform generalization analysis of the machine learning algorithms on its basis. Since the one-step transition probability matrix of MCRE depends on both previous states and the random environment, conventional techniques for generalization analysis cannot be directly applied. To address this issue, we propose a novel technique that transforms the original MCRE into a higher-dimensional time-homogeneous Markov chain. The new Markov chain involves more variables but is more regular, and thus easier to deal with. We prove the convergence of the new Markov chain when time approaches infinity. Then we prove a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain, which depends on both the Markovian parameters and the covering number of the function class compounded by the loss function for behavior prediction and the behavior prediction model. To the best of our knowledge, this is the first work that performs the generalization analysis on data generated by complex processes in real-world dynamic systems

Energy Consumption and Quality of Life: Energy Efficiency Index

AbstractThis paper shows a micro-economic based quantitative analysis scheme to evaluate the energy efficiency of cities based on quality of life and energy consumption. By representing the quality of life by utility, this study developed a CES-based model to estimate the individual demand of non-mobility goods, car trips, and public transport trips at the maximum utility level. Energy consumption is estimated by the demand of goods. An energy efficiency index is developed to show the relative energy consumption on the certain quality of life. We applied this model to Nagasaki region. Higher energy efficiency zones were found in city center and along the mass transit lines. Such findings suggest that a compact urban structure and higher public transport accessibility could increase energy efficiency

A Theoretical Analysis of NDCG Type Ranking Measures

A central problem in ranking is to design a ranking measure for evaluation of ranking functions. In this paper we study, from a theoretical perspective, the widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures. Although there are extensive empirical studies of NDCG, little is known about its theoretical properties. We first show that, whatever the ranking function is, the standard NDCG which adopts a logarithmic discount, converges to 1 as the number of items to rank goes to infinity. On the first sight, this result is very surprising. It seems to imply that NDCG cannot differentiate good and bad ranking functions, contradicting to the empirical success of NDCG in many applications. In order to have a deeper understanding of ranking measures in general, we propose a notion referred to as consistent distinguishability. This notion captures the intuition that a ranking measure should have such a property: For every pair of substantially different ranking functions, the ranking measure can decide which one is better in a consistent manner on almost all datasets. We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions. We next characterize the set of all feasible discount functions for NDCG according to the concept of consistent distinguishability. Specifically we show that whether NDCG has consistent distinguishability depends on how fast the discount decays, and 1/r is a critical point. We then turn to the cut-off version of NDCG, i.e., NDCG@k. We analyze the distinguishability of NDCG@k for various choices of k and the discount functions. Experimental results on real Web search datasets agree well with the theory.Comment: COLT 201

The Kohn-Laplacian and Cauchy-Szeg\"{o} projection on Model Domains

We study the Kohn-Laplacian and its fundamental solution on some model domains in $\mathbb C^{n+1}$, and further discuss the explicit kernel of the Cauchy-Szeg\"o projections on these model domains using the real analysis method. We further show that these Cauchy-Szeg\"o kernels are Calder\'on-Zygmund kernels under the suitable quasi-metric

4,6,7,9,10,12,13,15-Octa­hydro-2H-1,3-dithiolo[4,5-i][1,4,7,12]dioxadithia­cyclo­tetra­decine-2-thione

In the title mol­ecule, C11H16O2S5, the two S atoms from the macrocycle are situated on opposite sides of the mean plane of the five-membered ring, deviating from it by 1.288 (3) and 1.728 (3) Å. In the crystal, weak inter­molecular C—H⋯S and C—H⋯O hydrogen bonds link the mol­ecules into layers parallel to (100). The crystal studied was a racemic twin

2,3-[(3,6-Dioxaoctane-1,8-diyl)bis(sul­fanediylmethylene)]-6,7-bis(methylsulfanyl)-1,4,5,8-tetra­thia­fulvalene

In the title mol­ecule, C16H22S8O2, two S atoms, two O atoms and ten C atoms form a 14-membered ring with a boat conformation. In the crystal, C—H⋯O hydrogen bonds link the mol­ecules into dimers which are further connected into a chain along the a axis by C—H⋯S hydrogen bonds

Tetrabenazine is neuroprotective in Huntington's disease mice

<p>Abstract</p> <p>Background</p> <p>Huntington's disease (HD) is a neurodegenerative disorder caused by a polyglutamine (polyQ) expansion in Huntingtin protein (Htt). PolyQ expansion in Httexp causes selective degeneration of striatal medium spiny neurons (MSN) in HD patients. A number of previous studies suggested that dopamine signaling plays an important role in HD pathogenesis. A specific inhibitor of vesicular monoamine transporter (VMAT2) tetrabenazine (TBZ) has been recently approved by Food and Drug Administration for treatment of HD patients in the USA. TBZ acts by reducing dopaminergic input to the striatum.</p> <p>Results</p> <p>In previous studies we demonstrated that long-term feeding with TBZ (combined with L-Dopa) alleviated the motor deficits and reduced the striatal neuronal loss in the yeast artificial chromosome transgenic mouse model of HD (YAC128 mice). To further investigate a potential beneficial effects of TBZ for HD treatment, we here repeated TBZ evaluation in YAC128 mice starting TBZ treatment at 2 months of age ("early" TBZ group) and at 6 months of age ("late" TBZ group). In agreement with our previous studies, we found that both "early" and "late" TBZ treatments alleviated motor deficits and reduced striatal cell loss in YAC128 mice. In addition, we have been able to recapitulate and quantify depression-like symptoms in TBZ-treated mice, reminiscent of common side effects observed in HD patients taking TBZ.</p> <p>Conclusions</p> <p>Our results further support therapeutic value of TBZ for treatment of HD but also highlight the need to develop more specific dopamine antagonists which are less prone to side-effects.</p