228 research outputs found
Education, lifetime labor supply, and longevity improvements
This paper presents an analysis of the differential role of mortality for the optimal schooling and retirement age when the accumulation of human capital follows the so-called “Ben-Porath mechanism”. We set up a life-cycle model of consumption and labor supply at the extensive margin that allows for endogenous human capital formation. This paper makes two important contributions. First, we provide the conditions under which a decrease in mortality leads to a longer education period and an earlier retirement age. Second, those conditions are decomposed into a Ben-Porath mechanism and a lifetime-human wealth effect vs. the years-to-consume effect. Finally, using US and Swedish data for cohorts born between 1890 and 2000, we show that our model can match the empirical evidence
Migration on request, a practical technique for preservation
Maintaining a digital object in a usable state over time is a crucial aspect of digital preservation. Existing methods of preserving have many drawbacks. This paper describes advanced techniques of data migration which can be used to support preservation more accurately and cost effectively.
To ensure that preserved works can be rendered on current computer systems over time, “traditional migration” has been used to convert data into current formats. As the new format becomes obsolete another conversion is performed, etcetera. Traditional migration has many inherent problems as errors during transformation propagate throughout future transformations.
CAMiLEON’s software longevity principles can be applied to a migration strategy, offering improvements over traditional migration. This new approach is named “Migration on Request.” Migration on Request shifts the burden of preservation onto a single tool, which is maintained over time. Always returning to the original format enables potential errors to be significantly reduced
Factorizing LambdaMART for cold start recommendations
Recommendation systems often rely on point-wise loss metrics such as the mean
squared error. However, in real recommendation settings only few items are
presented to a user. This observation has recently encouraged the use of
rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to
rank which relies on such a metric. Despite its success it does not have a
principled regularization mechanism relying in empirical approaches to control
model complexity leaving it thus prone to overfitting.
Motivated by the fact that very often the users' and items' descriptions as
well as the preference behavior can be well summarized by a small number of
hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization
(LambdaMART-MF), that learns a low rank latent representation of users and
items using gradient boosted trees. The algorithm factorizes lambdaMART by
defining relevance scores as the inner product of the learned representations
of the users and items. The low rank is essentially a model complexity
controller; on top of it we propose additional regularizers to constraint the
learned latent representations that reflect the user and item manifolds as
these are defined by their original feature based descriptors and the
preference behavior. Finally we also propose to use a weighted variant of NDCG
to reduce the penalty for similar items with large rating discrepancy.
We experiment on two very different recommendation datasets, meta-mining and
movies-users, and evaluate the performance of LambdaMART-MF, with and without
regularization, in the cold start setting as well as in the simpler matrix
completion setting. In both cases it outperforms in a significant manner
current state of the art algorithms
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation
Biomedical text tagging systems are plagued by the dearth of labeled training
data. There have been recent attempts at using pre-trained encoders to deal
with this issue. Pre-trained encoder provides representation of the input text
which is then fed to task-specific layers for classification. The entire
network is fine-tuned on the labeled data from the target task. Unfortunately,
a low-resource biomedical task often has too few labeled instances for
satisfactory fine-tuning. Also, if the label space is large, it contains few or
no labeled instances for majority of the labels. Most biomedical tagging
systems treat labels as indexes, ignoring the fact that these labels are often
concepts expressed in natural language e.g. `Appearance of lesion on brain
imaging'. To address these issues, we propose constructing extra labeled
instances using label-text (i.e. label's name) as input for the corresponding
label-index (i.e. label's index). In fact, we propose a number of strategies
for manufacturing multiple artificial labeled instances from a single label.
The network is then fine-tuned on a combination of real and these newly
constructed artificial labeled instances. We evaluate the proposed approach on
an important low-resource biomedical task called \textit{PICO annotation},
which requires tagging raw text describing clinical trials with labels
corresponding to different aspects of the trial i.e. PICO (Population,
Intervention/Control, Outcome) characteristics of the trial. Our empirical
results show that the proposed method achieves a new state-of-the-art
performance for PICO annotation with very significant improvements over
competitive baselines.Comment: International Workshop on Health Intelligence (W3PHIAI-20); AAAI-2
Optimal time allocation in active retirement. Working Paper 02/2019
We set up a lifecycle model of a retired scholar who chooses opti-mally the time devoted to different activities including physical activity,continued work and social engagement. While time spent in physicalactivity increases life expectancy, continued scientific publications in-creases the knowledge stock. We show the optimal trade off betweenthese activities in retirement and its sensitivity with respect to alterna-tive settings of the preference parameters
Should I stay or should I go: Modelling disaster risk behaviour using a dynamic household level approach
In the last decades, many parts of the world faced an increase in the number of extreme weather events and worsening climate conditions endangering the livelihood of households in developing countries that rely on their local environment. While various empirical studies have identified key factors of exposure and vulnerability to disaster risk, we still lack a conceptual understanding of how these forces interact and how they impact household decision making. To gain insight into these mechanisms we set up a dynamic household model where households face environmental hazards. To respond to the risk, households can either relocate to a safer area or undertake preventive measures. Both actions require material and immaterial resources, which constrain the household's decision. Households are assumed to be heterogeneous with respect to key empirically identified factors for individual disaster risk: education, income, risk awareness, time preference and their access to preventive measures. This paper provides analytical insights into the short-run decision making of households derived from the theoretical framework as well as an extensive numerical investigation. To parameterize and calibrate the model we use data from Thailand and Vietnam. The roles of household characteristics on the short-term decision-making and long-run outcomes of households' well-being and disaster risk is discussed. We conclude the paper with an extensive evaluation of different policy interventions including housing and prevention cost subsidies as well as income transfers with respect to their heterogeneous effects on different sub-populations
Multi-score Learning for Affect Recognition: the Case of Body Postures
An important challenge in building automatic affective state
recognition systems is establishing the ground truth. When the groundtruth
is not available, observers are often used to label training and testing
sets. Unfortunately, inter-rater reliability between observers tends to
vary from fair to moderate when dealing with naturalistic expressions.
Nevertheless, the most common approach used is to label each expression
with the most frequent label assigned by the observers to that expression.
In this paper, we propose a general pattern recognition framework
that takes into account the variability between observers for automatic
affect recognition. This leads to what we term a multi-score learning
problem in which a single expression is associated with multiple values
representing the scores of each available emotion label. We also propose
several performance measurements and pattern recognition methods for
this framework, and report the experimental results obtained when testing
and comparing these methods on two affective posture datasets
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Post-hoc model-agnostic interpretation methods such as partial dependence
plots can be employed to interpret complex machine learning models. While these
interpretation methods can be applied regardless of model complexity, they can
produce misleading and verbose results if the model is too complex, especially
w.r.t. feature interactions. To quantify the complexity of arbitrary machine
learning models, we propose model-agnostic complexity measures based on
functional decomposition: number of features used, interaction strength and
main effect complexity. We show that post-hoc interpretation of models that
minimize the three measures is more reliable and compact. Furthermore, we
demonstrate the application of these measures in a multi-objective optimization
approach which simultaneously minimizes loss and complexity
Redistributive effects of different pension systems when longevity varies by socioeconomic status
We propose a general analytical framework to model the redistributive features of alternative pension systems when individuals face ex ante differences in mortality. Differences in life expectancy between high and low socioeconomic groups are often large and have widened recently in many countries. Such longevity gaps affect the actuarial fairness and progressivity of public pension systems. However, behavioral responses to longevity and policy complicate analysis of possible reforms. Here we consider how various pension systems would perform in an OLG setting with heterogeneous longevity and ability. We evaluate redistributive effects of three Notional Defined Contribution plans and three Defined Benefit plans, calibrated on the US case. Compared to a benchmark non-redistributive plan that accounts for differences in mortality, US Social Security reduces regressivity from longevity differences, but would require group-specific life tables to achieve progressivity. Moreover, without separate life tables, despite apparent accounting gains, lower income groups would suffer welfare losses and higher income groups would enjoy welfare gains through indirect effects of pension systems on labor supply
Redistributive effects of pension reforms: who are the winners and losers?
As the heterogeneity in life expectancy by socioeconomic status increases, many pension systems imply a wealth transfer from short- to long-lived individuals. Various pension reforms aim to reduce inequalities that are caused by ex-ante differences in life expectancy. However, these pension reforms may induce redistribution effects. We introduce a dynamic general equilibrium-overlapping generations model with heterogeneous individuals that differ in their education, labor supply, lifetime income, and life expectancy. Within this framework we study six different pension reforms that foster the sustainability of the pension system and aim to account for heterogeneous life expectancy. Our results highlight that pension reforms have to be evaluated at various dimensions. Reforms that may increase the sustainability of the pension system are not necessarily conducive to reduce the redistributive wealth transfers from short- to long-lived individuals. Our paper emphasizes the need for studying pension reforms in models with behavioral feedback and heterogeneous socioeconomic groups
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