Behavioral modeling in weight loss interventions

Designing systems with human agents is difficult because it often requires models that characterize agents’ responses to changes in the system’s states and inputs. An example of this scenario occurs when designing treatments for obesity. While weight loss interventions through increasing physical activity and modifying diet have found success in reducing individuals’ weight, such programs are difficult to maintain over long periods of time due to lack of patient adherence. A promising approach to increase adherence is through the personalization of treatments to each patient. In this paper, we make a contribution toward treatment personalization by developing a framework for predictive modeling using utility functions that depend upon both time-varying system states and motivational states evolving according to some modeled process corresponding to qualitative social science models of behavior change. Computing the predictive model requires solving a bilevel program, which we reformulate as a mixed-integer linear program (MILP). This reformulation provides the first (to our knowledge) formulation for Bayesian inference that uses empirical histograms as prior distributions. We study the predictive ability of our framework using a data set from a weight loss intervention, and our predictive model is validated by comparison to standard machine learning approaches. We conclude by describing how our predictive model could be used for optimization, unlike standard machine learning approaches that cannot.     

Two-weight and three-weight linear codes based on Weil sums

Linear codes with few weights have applications in secret sharing, authentication codes, association schemes and strongly regular graphs. In this paper, several classes of two-weight and three-weight linear codes are presented and their weight distributions are determined using Weil sums. Some of the linear codes obtained are optimal or almost optimal with respect to the Griesmer bound.     

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm “Baseline Correction Combined Partial Least Squares (BCC-PLS)”, which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7–19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33–68%).     

Weights of boundaries of compact convex sets

Let B be a boundary of a compact convex set X. We prove that the weight of X equals the weight of B provided B is Lindelöf or X is a standard compact convex set and B is the set of extreme points of X.     

Integral closures and weight functions over finite fields

Curves and surfaces of type I are generalized to integral towers of rank r. Weight functions with values in N^r and the corresponding weighted total-degree monomial orderings lift naturally from one domain R_j−1 in the tower to the next, R_j, the integral closure of R_j−1[x_j]/φ(x_j). The qth power algorithm is reworked in this more general setting to produce this integral closure over finite fields, though the application is primarily that of calculating the normalizations of curves related to one-point AG codes arising from towers of function fields. Every attempt has been made to couch all the theory in terms of multivariate polynomial rings and ideals instead of the terminology from algebraic geometry or function field theory, and to avoid the use of any type of series expansion.     

独立随机变量序列加权和的相合性

设t0∈(0，1)，Wni(t0)是关于实变量t1，t2，…，tn的权函数；随机变量序列Y1，Y2，…，Yn，iid．本研究了随机变量序列加权和∑(i=1,n)Wni(t0)Yi的相合性．     

Weight distribution of the bases of a binary matroid

Let M be a weighted binary matroid and w₁ < … < w_m be the increasing sequence of all possible distinct weights of bases of M. We give a sufficient condition for the property that w₁, …, w_m is an arithmetical progression of common difference d. We also give conditions which guarantee that w_i+1 − w_i ≤ d, 1 ≤ i ≤ m −1. Dual forms for these results are given also.     

专家问卷调查的非参数统计分析

王兆军．专家问卷调查的非参数统计分析．数理统计与管理，１９９８，１７（３），２６～３０．在经济领域，当研究某些基本指标在一个指标体系中的权重问题时，专家问卷调查是经常采用的一种方法。本文从非参数统计的角度出发，给出如何选取这些权重的方法，并利用Ｋｅｎｄａｌ协合系数对专家问卷的一致性进行检验     

A metric measure for weight matrices of variable lengths—with applications to clustering and classification of hidden Markov models

We construct a metric measure among weight matrices that are commonly used in non-interacting statistical physics systems, computational biology problems, as well as in general applications such as hidden Markov models. The metric distance between two weight matrices is obtained via aligning the matrices and thus can be evaluated by dynamic programming. Capable of allowing reverse complements in distance evaluation, this metric accommodates both gapless and gapped alignments between two weight matrices. The distance statistics among random motifs is also studied. We find that the average square distance and its standard error grow with different powers of motif length, and the normalized square distance follows a Gaussian distribution for large motif lengths.