Sparse Stochastic Inference for Latent Dirichlet allocation

We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

Modern Blasphemy Laws in Pakistan and the Rimsha Masih Case: What Effect—if Any—the Case Will Have on Their Future Reform

This Note will examine the current blasphemy laws in Pakistan, with a particular focus on the case of Rimsha Masih, who was charged with blasphemy in 2012 and ultimately acquitted in 2013. Although Rimsha’s case is unprecedented, it is far from a turning point for blasphemy laws in Pakistan for two reasons. First, Rimsha’s case was an outlier, as evidenced by the unique circumstances of her case, as well as subsequent events that reinforce that proposition. Second, the nature of the political-religious-legal system in Pakistan makes the blasphemy laws and their enforcement a far more complicated issue than what occurs in a courtroom can explain. The very nature of Pakistan’s governmental, constitutional, and legal structure has created an extra-legal system of blasphemy law enforcement grounded in traditional notions of Islam and enforced in large part through vigilantism. If reform is to be achieved, it is in that arena where it must take place

Stochastic Optimization of Power System Dynamics for Grid Resilience

When faced with uncertainty regarding potential failure contingencies, prioritizing system resiliency through optimal control of exciter reference voltage and mechanical torque can be arduous due to the scope of potential failure contingencies. Optimal control schemes can be generated through a two-stage stochastic optimization model by anticipating a set of contingencies with associated probabilities of occurrence, followed by the optimal recourse action once the contingency has been realized. The first stage, common across all contingency scenarios, co-optimally positions the grid for the set of possible contingencies. The second stage dynamically assesses the impact of each contingency and allows for emergency control response. By unifying the optimal control scheme prior and post the failure contingency, a singular policy can be constructed to maximize system resiliency

Nonparametric variational inference

Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation. The locations of these kernels and their bandwidth are treated as variational parameters and optimized to improve an approximate lower bound on the marginal likelihood of the data. Using multiple kernels allows the approximation to capture multiple modes of the posterior, unlike most other variational approximations. We demonstrate the efficacy of the nonparametric approximation with a hierarchical logistic regression model and a nonlinear matrix factorization model. We obtain predictive performance as good as or better than more specialized variational methods and sample-based approximations. The method is easy to apply to more general graphical models for which standard variational methods are difficult to derive.Comment: ICML201

Bayesian Networks for Interpretable Cyberattack Detection

The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CKTM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs) – probabilistic graphical models consisting of a set of variables and their conditional dependencies – for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof

Investigating the Relationships Among Peer Athlete Mentor Leadership Behaviours, Mentoring Functions, and Perceptions of Satisfaction

The purpose of the present study was twofold. The first purpose was to examine the relationship between peer athlete mentor transformational and transactional leadership behaviours and protégé receipt of Vocational and Psychosocial mentoring functions. The second purpose was to examine the association between peer athlete mentoring functions and protégé satisfaction. The sample comprised 272 varsity athletes. Results of structural equation modeling showed that the transformational leadership behaviours of Inspirational Motivation, Democratic Behaviour, and Social Support were positively related to Psychosocial mentoring. Further, the transformational leadership behaviours of Intellectual Stimulation and Social Support were positively associated to Vocational mentoring. In terms of transactional leadership behaviours, Positive Feedback was positively related to Psychosocial mentoring, while Contingent Reward was positively associated to Vocational mentoring. Additionally, the leadership behaviour of Training and Instruction was positively related to Vocational mentoring. Finally, the results showed that Psychosocial mentoring was positively related to protégé satisfaction

Preterm birth : can we do better?

Preterm birth (PTB) remains the most serious complication in obstetrics and a substantial excess burden in US healthcare economics. The etiology of PTB is complex and likely has multiple physiological pathways. Unfortunately, current antenatal care screening methods have not been successful in predicting and, eventually, preventing PTB. Although treatments such as progesterone, cerclage and pessary are available for patients with historical risk factors and shortened cervix, these treatments are not universally efficacious. Antenatal care is in great need of new prediction and prevention strategies. The role of more global methods of screening and treatment is still undefined. Most women with clinical risk factors will not deliver early, and aggressive interventions in large segments of the population may not be warranted or cost effective. Furthermore, over half of women who experience PTB have no historical risk factors. Even second-trimester cervical length (CL) has only modest ability to predict which women will experience PTB. There is thus a clear need to identify biomarkers that provide quantitative, individualized assessment of risk early in pregnancy that is specific for each individual woman. The ideal biomarkers would be indicative of the pathway leading to PTB, require no special testing equipment, have a low false positive and negative rate, and offer early identification, allowing adequate time to intervene. We need an aggressive and comprehensive approach to see a dramatic reduction in rates of preterm delivery in the U.

Non-Violent Drug Offences: Are There Alternatives to Imprisonment?

No one wants to be sent to jail but laws and punishment for violation of laws are part of every society. Unfortunately, USA is the leader in percent of population imprisoned. Based on current projections, by 2011 the U.S. prison population will increase by 13 percent, which is triple the growth of the entire population as a whole, to more than 1.7 million. Supporting that increase in incarcerated people will cost American taxpayers and local/state budgets an estimated $27.5 billion. This results in an enormous burden on society. Our research attempts to evaluate what effects this prison overcrowding has on Idaho\u27s economy through cost analysis and comparison along with a look at true effectiveness of punishment methods based on recidivism rates. We focus on the non-violent drug offender population and offer an alternative to imprisonment

Application of an Extremal Result of Erdős and Gallai to the (n,k,t) Problem

An extremal result about vertex covers, attributed by Hajnal to Erdős and Gallai, is applied to prove the following: If n, k, and t are integers satisfying n ≥ k ≥ t ≥ 3 and k ≤ 2t - 2, and G is a graph with the minimum number of edges among graphs on n vertices with the property that every induced subgraph on k vertices contains a complete subgraph on t vertices, then every component of G is complete