Facilitating a benign attentional bias reduces negative thought intrusions.

The causal role of biased attention in worry was investigated in an experiment in which high worriers were assigned either to a condition requiring attention to nonthreatening words and text while ignoring worry-related material or to a mixed-attention control condition. The former procedure led to fewer negative thought intrusions in a worry test (as rated by both participants and an assessor) than did the control condition. These findings suggest that attentional bias plays a causal role in worry and that its modification can reduce excessive worry. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Data Replication in Cooperative Mobile Ad-Hoc Networks

The mobile computing environment provides many benefits such as ubiquitous access to computing but includes constraints on resources such as available bandwidth and battery life. Replication is a widely recognized method for balancing the demands of storage space with bandwidth and battery life. We propose a novel scheme that seeks to strategically balance these constrained resources through a cooperative game-theory approach for replication in a mobile environment. Our replication strategy relies on the cooperation of the nodes within the network to make replica caching decisions which are spatiotemporally local-optimal for the network from an energy and bandwidth conservation standpoint. In cooperative altruistic data replication, each node calculates the net global benefit, for caching a replica of the requested data, as the result data is returned from the responding node to the requesting node, where it determines the spatiotemporally local-optimal node for replicating the data item. Performance results from our research indicate that our scheme, CADR, improves the query response time by 25 and 45 %, mean hop count is improved by 26 and 46 %, query error is reduced by 30 and 48 %, while energy utilization is reduced 30 and 57 % when compared with both another game theoretic replication approach and standard cooperative caching respectively.     

动态不确定环境下的决策：一种分层决策模型

本文提出一种智能体分层决策结构模型，试图通过分层决策技术有效地解决动态、不确定环境中的智能体的实时决策问题。本模型的高层采用BDI结构，以便为较长期任务的规划和推理提供充分的支持；模型的底层采用反应式结构，以保证对短期实时任务的及时响应。实验结果表明了这种分层模型在某些复杂任务领域中的有效性。     

UnitWalk: A new SAT solver that uses local search guided by unit clause elimination

In this paper we present a new randomized algorithm for SAT, i.e., the satisfiability problem for Boolean formulas in conjunctive normal form. Despite its simplicity, this algorithm performs well on many common benchmarks ranging from graph coloring problems to microprocessor verification. Our algorithm is inspired by two randomized algorithms having the best current worst-case upper bounds ([27,28] and [30,31]). We combine the main ideas of these algorithms in one algorithm. The two approaches we use are local search (which is used in many SAT algorithms, e.g., in GSAT [34] and WalkSAT [33]) and unit clause elimination (which is rarely used in local search algorithms). In this paper we do not prove any theoretical bounds. However, we present encouraging results of computational experiments comparing several implementations of our algorithm with other SAT solvers. We also prove that our algorithm is probabilistically approximately complete (PAC).     

UnitWalk: A new SAT solver that uses local search guided by unit clause elimination

In this paper we present a new randomized algorithm for SAT, i.e., the satisfiability problem for Boolean formulas in conjunctive normal form. Despite its simplicity, this algorithm performs well on many common benchmarks ranging from graph coloring problems to microprocessor verification. Our algorithm is inspired by two randomized algorithms having the best current worst-case upper bounds ([27,28] and [30,31]). We combine the main ideas of these algorithms in one algorithm. The two approaches we use are local search (which is used in many SAT algorithms, e.g., in GSAT [34] and WalkSAT [33]) and unit clause elimination (which is rarely used in local search algorithms). In this paper we do not prove any theoretical bounds. However, we present encouraging results of computational experiments comparing several implementations of our algorithm with other SAT solvers. We also prove that our algorithm is probabilistically approximately complete (PAC).     

SAT Local Search Algorithms: Worst-Case Study

Recent experiments demonstrated that local search algorithms (e.g. GSAT) are able to find satisfying assignments for many hard Boolean formulas. A wide experimental study of these algorithms demonstrated their good performance on some inportant classes of formulas as well as poor performance on some other ones. In contrast, theoretical knowledge of their worst-case behavior is very limited. However, many worst-case upper and lower bounds of the form 2 ⁿ (<1 is a constant) are known for other SAT algorithms, for example, resolution-like algorithms. In the present paper we prove both upper and lower bounds of this form for local search algorithms. The class of linear-size formulas we consider for the upper bound covers most of the DIMACS benchmarks; the satisfiability problem for this class of formulas is NP-complete.     

High-dose thiotepa and etoposide-based regimens with autologous hematopoietic support for high-risk or recurrent CNS tumors in children and adults

The prognosis in patients with primary brain tumors treated with surgery, radiotherapy and conventional chemotherapy remains poor. To improve outcome, combination high-dose chemotherapy (HDC) has been explored in children, but rarely in adults. This study was performed to determine the tolerability of three-drug combination high-dose thiotepa (T) and etoposide (E)-based regimens in pediatric and adult patients with high-risk or recurrent primary brain tumors. Thirty-one patients (13 children and 18 adults) with brain tumors were treated with high-dose chemotherapy: 19 with BCNU (B) and TE (BTE regimen), and 12 with carboplatin (C) and TE (CTE regimen). Patients received growth factors and hematopoietic support with marrow (n = 15), peripheral blood progenitor cells (PBPC) (n = 11) or both (n = 5). The 100 day toxic mortality rate was 3% (1/31). Grade III/IV toxicities included mucositis (58%), hepatitis (39%) and diarrhea (42%). Five patients had seizures and two had transient encephalopathy (23%). All patients had neutropenic fever and all pediatric patients required hyperalimentation. Median time to engraftment with absolute neutrophil count (ANC) >0.5 x 10(9)/l was 11 days (range 8-37 days). Time to ANC engraftment was significantly longer (P = 0.0001) in patients receiving marrow (median 14 days, range 10-37) than for PBPC (median 9.5 days, range 8-10). Platelet engraftment >50 x 10(9)/l was 24 days (range 14-53 days) in children. In adults, platelet engraftment >20 x 10(9)/l was 12 days (range 9-65 days). In 11 patients supported with PBPC, there was a significant inverse correlation between CD34+ dose and days to ANC (rho = -0.87, P = 0.009) and platelet engraftment (rho = -0.85, P = 0.005), with CD34+ dose predicting time to engraftment following HDC. Overall, 30% of evaluable patients (7/24) had a complete response (CR) (n = 3) or partial response (PR) (n = 4). Median time to tumor progression (TTP) was 7 months, with an overall median survival of 12 months. These TE-based BCNU or carboplatin three-drug combination HDC regimens are safe and tolerable with promising response rates in both children and older adults.     

Meta-analysis of multiple outcomes by regression with random effects

Earlier work showed how to perform fixed-effects meta-analysis of studies or trials when each provides results on more than one outcome per patient and these multiple outcomes are correlated. That fixed-effects generalized-least-squares approach analyzes the multiple outcomes jointly within a single model, and it can include covariates, such as duration of therapy or quality of trial, that may explain observed heterogeneity of results among the trials. Sometimes the covariates explain all the heterogeneity, and the fixed-effects regression model is appropriate. However, unexplained heterogeneity may often remain, even after taking into account known or suspected covariates. Because fixed-effects models do not make allowance for this remaining unexplained heterogeneity, the potential exists for bias in estimated coefficients, standard errors and p-values. We propose two random-effects approaches for the regression meta-analysis of multiple correlated outcomes. We compare their use with fixed-effects models and with separate-outcomes models in a meta-analysis of periodontal clinical trials. A simulation study shows the advantages of the random-effects approach. These methods also facilitate meta-analysis of trials that compare more than two treatments.