Reducing the Effects of Detrimental Instances

Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental or not. In this paper, we 1) extend this paradigm by weighting the instances on a continuous scale and 2) present a methodology for measuring how detrimental an instance may be for inducing a model of the data. We call our method of identifying and weighting detrimental instances reduced detrimental instance learning (RDIL). We examine RIDL on a set of 54 data sets and 5 learning algorithms and compare RIDL with other weighting and filtering approaches. RDIL is especially useful for learning algorithms where every instance can affect the classification boundary and the training instances are considered individually, such as multilayer perceptrons trained with backpropagation (MLPs). Our results also suggest that a more accurate estimate of which instances are detrimental can have a significant positive impact for handling them.Comment: 6 pages, 5 tables, 2 figures. arXiv admin note: substantial text overlap with arXiv:1403.189

Causal Induction from Continuous Event Streams: Evidence for Delay-Induced Attribution Shifts

Contemporary theories of Human Causal Induction assume that causal knowledge is inferred from observable contingencies. While this assumption is well supported by empirical results, it fails to consider an important problem-solving aspect of causal induction in real time: In the absence of well structured learning trials, it is not clear whether the effect of interest occurred because of the cause under investigation, or on its own accord. Attributing the effect to either the cause of interest or alternative background causes is an important precursor to induction. We present a new paradigm based on the presentation of continuous event streams, and use it to test the Attribution-Shift Hypothesis (Shanks & Dickinson, 1987), according to which temporal delays sever the attributional link between cause and effect. Delays generally impaired attribution to the candidate, and increased attribution to the constant background of alternative causes. In line with earlier research (Buehner & May, 2002, 2003, 2004) prior knowledge and experience mediated this effect. Pre-exposure to a causally ineffective background context was found to facilitate the discovery of delayed causal relationships by reducing the tendency for attributional shifts to occur. However, longer exposure to a delayed causal relationship did not improve discovery. This complex pattern of results is problematic for associative learning theories, but supports the Attribution-Shift Hypothesi

The Psychological Impact of Long-Term Solitary Confinement on Inmates in the United States

Psychological distress among inmates is prevalent in correctional facilities throughout the United States. Although, according to Haney (2003), severe isolation of incarcerates has been commonplace in prisons since their inception, the use of secure housing units (SHU) and the development of ‘supermax’ prisons are becoming increasingly utilized within the last several decades. Legislators have expressed the need to increase punitive measures against delinquents in response to the rising prison population (Arrigo and Bullock 2008). Thus harsher crime control policies, such as administrative and disciplinary segregation, have been established in order to limit the personal freedoms of prisoners (Arrigo and Bullock 2008). Within these institutions, inmates are increasingly subjected to solitary confinement, a method of incarceration characterized by “the confinement of a prisoner in isolation with limited chance for social interaction or environmental stimulus” (The Psychology of Cruelty 2015). Theories surrounding the use of solitary confinement emphasize its potential to deter future misconduct among inmates (Morris 2015); however, little attention has been given to the potential psychological effects of long-term segregation. In response, this paper seeks to examine the exacerbating and detrimental psychological effects experienced by inmates subjected to solitary confinement in the United States

Quantum and Classical in Adiabatic Computation

Adiabatic transport provides a powerful way to manipulate quantum states. By preparing a system in a readily initialised state and then slowly changing its Hamiltonian, one may achieve quantum states that would otherwise be inaccessible. Moreover, a judicious choice of final Hamiltonian whose groundstate encodes the solution to a problem allows adiabatic transport to be used for universal quantum computation. However, the dephasing effects of the environment limit the quantum correlations that an open system can support and degrade the power of such adiabatic computation. We quantify this effect by allowing the system to evolve over a restricted set of quantum states, providing a link between physically inspired classical optimisation algorithms and quantum adiabatic optimisation. This new perspective allows us to develop benchmarks to bound the quantum correlations harnessed by an adiabatic computation. We apply these to the D-Wave Vesuvius machine with revealing - though inconclusive - results

Identifying the determinants of chronic absenteeism: A bioecological systems approach

Background/Context: Chronic school absenteeism is a pervasive problem across the US; in early education, it is most rampant in kindergarten and its consequences are particularly detrimental, often leading to poorer academic, behavioral and developmental outcomes later in life. Though prior empirical research has identified a broad range of determinants of chronic absenteeism, there lacks a single, unified theoretically driven investigation examining how such factors concurrently explain the incidence of chronic absenteeism among our nation 's youngest schoolchildren. Thus, it is difficult to determine the relative importance of one factor over another, hence making it challenging to develop appropriate supports and services to reduce school absences. Purpose/Research Questions: Our study filled this critical void-we investigated multiple determinants of chronic absenteeism that were grounded, theoretically and empirically, in Bronfenbrenner's bioecological model of development. Specifically, using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 and the method of hierarchical generalized linear modeling, we analyzed how the co-occurrence of key (a) process, (b) person, and (c) context (micro-, meso-, exo- and macrosystem) factors was associated with kindergarteners' probability of being chronically absent. Findings/Results: Children who have poorer health, higher internalizing behaviors, and more frequent engagement in learning activities at home had higher odds of chronic absenteeism. Also, children from larger families and of lower socioeconomic status faced increased odds of chronic absenteeism. Conversely, children holding positive attitudes towards school had lowered odds of chronic absenteeism, a finding that remained robust across socioeconomic status groups. Finally, parent-school connections were associated with lowered odds of absenteeism. Conclusions/Recommendations: Overall, our findings strongly suggested that addressing chronic absenteeism will require comprehensive and multifaceted approaches that recognize these multiple factors. With this theoretically grounded, more descriptive approach, it is more feasible to identify key factors and subsequently design policies and practices to prevent absence behavior

Interventions for reducing sedentary behaviour in community-dwelling older adults

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To identify the effects and assess the effectiveness of interventions to reduce sedentary behaviour (total sedentary time and the pattern of accumulation of sedentary time) in older adults. To summarise the effects of interventions to reduce sedentary behaviour on quality of life, depression, and health status in older adults. To summarise any evidence on the cost-effectiveness of interventions that reduce sedentary behaviour in older adults