Self-monitoring and self-reinforcement applied to junior college study behavior

Although previous research has shown the efficacy of self-control techniques such as self-monitoring and self-reinforcement in changing study behavior, the adequacy of the experimental procedures utilized in many studies may be questioned. This experiment re-examined the effects these operant conditioning procedures had on the study behavior of junior college evening students by using three groups of subjects exposed to different combinations of self-control techniques. Control Group subjects recorded their new thoughts (ideas relating the course material to their everyday lives) in an attempt to control for the effects of novel procedures, motivation and other non-specific variables. Self-Monitoring Group subjects complied with the control procedure, monitored their study time {by recording study start and stop times) and monitored the number of facts learned (by self-administering quizzes). The Self-Reinforcement Group complied with the self-monitoring procedures and self-reinforced their study behavior (by listing their reinforcers, the cost per reinforcer in minutes of study time and recording the number of reinforcements received). Additional data collected consisted of concurrent weekly quiz scores, final examination scores and the extent to which subjects complied with the treatment procedures. Results showed no significant differences among the three groups on either concurrent quizzes or on the final examination. The Self-

An efficient algorithm for learning with semi-bandit feedback

We consider the problem of online combinatorial optimization under semi-bandit feedback. The goal of the learner is to sequentially select its actions from a combinatorial decision set so as to minimize its cumulative loss. We propose a learning algorithm for this problem based on combining the Follow-the-Perturbed-Leader (FPL) prediction method with a novel loss estimation procedure called Geometric Resampling (GR). Contrary to previous solutions, the resulting algorithm can be efficiently implemented for any decision set where efficient offline combinatorial optimization is possible at all. Assuming that the elements of the decision set can be described with d-dimensional binary vectors with at most m non-zero entries, we show that the expected regret of our algorithm after T rounds is O(m sqrt(dT log d)). As a side result, we also improve the best known regret bounds for FPL in the full information setting to O(m^(3/2) sqrt(T log d)), gaining a factor of sqrt(d/m) over previous bounds for this algorithm.Comment: submitted to ALT 201

Uncertainty and Economic Activity: A Global Perspective

The 2007-2008 global _financial crisis and the subsequent anemic recovery have rekindled academic interest in quantifying the impact of uncertainty on further assume that these common factors affect volatility and economic activity with a time lag of at least a quarter. Under these assumptions, we show analytically that volatility is forward looking and that the output equation of a typical VAR estimated in the literature is mis-specified as least squares estimates of this equation are inconsistent. Empirically, we document a statistically significant and economically sizable impact of future output growth on current volatility, and no effect of volatility shocks on business cycles, over and above those driven by the common factors. We interpret this evidence as suggesting that volatility is a symptom rather than a cause of economic instability

Multitask Protein Function Prediction Through Task Dissimilarity

Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions. We propose a multitask learning algorithm addressing both issues. Unlike standard multitask algorithms, which use task (protein functions) similarity information as a bias to speed up learning, we show that dissimilarity information enforces separation of rare class labels from frequent class labels, and for this reason is better suited for solving unbalanced protein function prediction problems. We support our claim by showing that a multitask extension of the label propagation algorithm empirically works best when the task relatedness information is represented using a dissimilarity matrix as opposed to a similarity matrix. Moreover, the experimental comparison carried out on three model organism shows that our method has a more stable performance in both "protein-centric" and "function-centric" evaluation settings

Revisiting the Core Ontology and Problem in Requirements Engineering

In their seminal paper in the ACM Transactions on Software Engineering and Methodology, Zave and Jackson established a core ontology for Requirements Engineering (RE) and used it to formulate the "requirements problem", thereby defining what it means to successfully complete RE. Given that stakeholders of the system-to-be communicate the information needed to perform RE, we show that Zave and Jackson's ontology is incomplete. It does not cover all types of basic concerns that the stakeholders communicate. These include beliefs, desires, intentions, and attitudes. In response, we propose a core ontology that covers these concerns and is grounded in sound conceptual foundations resting on a foundational ontology. The new core ontology for RE leads to a new formulation of the requirements problem that extends Zave and Jackson's formulation. We thereby establish new standards for what minimum information should be represented in RE languages and new criteria for determining whether RE has been successfully completed.Comment: Appears in the proceedings of the 16th IEEE International Requirements Engineering Conference, 2008 (RE'08). Best paper awar

China’s Emergence in the World Economy and Business Cycles in Latin America

The international business cycle is very important for Latin America's economic performance as the recent global crisis vividly illustrated. This paper investigates how changes in trade linkages between China, Latin America, and the rest of the world have altered the transmission mechanism of international business cycles to Latin America. Evidence based on a Global Vector Autoregressive (GVAR) model for 5 large Latin American economies and all major advanced and emerging economies of the world shows that the long-term impact of a China GDP shock on the typical Latin American economy has increased by three times since mid-1990s. At the same time, the long-term impact of a US GDP shock has halved, while the transmission of shocks to Latin America and the rest of emerging Asia (excluding China and India) GDP has not undergone any significant change. Contrary to common wisdom, we find that these changes owe more to the changed impact of China on Latin America's traditional and largest trading partners than to increased direct bilateral trade linkages boosted by the decade-long commodity price boom. These findings help to explain why Latin America did so well during the global crisis, but point to the risks associated with a deceleration in China's economic growth in the future for both Latin America and the rest of the world economy. The evidence reported also suggests that the emergence of China as an important source of world growth might be the driver of the so called "decoupling" of emerging markets business cycle from that of advanced economies reported in the existing literature.Latin Americ

Pain-motor integration in the primary motor cortex in Parkinson's disease

In Parkinson's disease (PD), the influence of chronic pain on motor features has never been investigated. We have recently designed a technique that combines nociceptive system activation by laser stimuli and primary motor cortex (M1) activation through transcranial magnetic stimulation (TMS), in a laser-paired associative stimulation design (Laser-PAS). In controls, Laser-PAS induces long-term changes in motor evoked potentials reflecting M1 long-term potentiation-like plasticity, arising from pain-motor integration

Competing with stationary prediction strategies

In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of stationarity is made about the environment, and the stationarity of the considered strategies only means that they do not depend explicitly on time; we argue that it is natural to consider only stationary strategies even for highly non-stationary environments.Comment: 20 page

Correlation Clustering with Adaptive Similarity Queries

In correlation clustering, we are givennobjects together with a binary similarityscore between each pair of them. The goal is to partition the objects into clustersso to minimise the disagreements with the scores. In this work we investigatecorrelation clustering as an active learning problem: each similarity score can belearned by making a query, and the goal is to minimise both the disagreementsand the total number of queries. On the one hand, we describe simple activelearning algorithms, which provably achieve an almost optimal trade-off whilegiving cluster recovery guarantees, and we test them on different datasets. On theother hand, we prove information-theoretical bounds on the number of queriesnecessary to guarantee a prescribed disagreement bound. These results give a richcharacterization of the trade-off between queries and clustering error