Using Research Metrics to Improve Timelines: Proceedings from the 2nd Annual CTSA Clinical Research Management Workshop

The Clinical and Translational Science Award (CTSA) Consortium Workshop was conceived as a venue to foster communication among Academic Medical Centers (AMCs) in the development of methods to improve clinical research management. The consortium, comprised of 46 awardee sites as of 2009, many with multiple AMCs, is expected to expand to 60 sites when fully implemented. At the 2nd Annual CTSA Clinical Research Management Workshop held on June 22 nd and 23 rd , 2009, on the National Institutes of Health (NIH) campus, consortium members and potential CTSA sites gathered with stakeholders from private industry, the NIH, the Food and Drug Administration, and private research organizations, to formulate a plan to address challenges in clinical research management. Specific aims included improving protocol processing and sharing process improvement initiatives in the expectation that best practices will be implemented and improvements will be measured and reported. The findings presented at this workshop indicated significant variance in Institutional Review Board approval of protocols and contract execution by AMC and CTSA sites. Most represented marked delays compared to non-AMC sites and that, as a likely consequence, AMCs were later to enroll patients and/or meet enrollment targets compared to dedicated or professional sites. Clin Trans Sci 2010; Volume 3: 305–308Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79218/1/j.1752-8062.2010.00246.x.pd

ROC curves for regression

“NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Volume 46, Issue 12, December 2013, Pages 3395–3411 DOI: 10.1016/j.patcog.2013.06.014Receiver Operating Characteristic (ROC) analysis is one of the most popular tools for the visual assessment and understanding of classifier performance. In this paper we present a new representation of regression models in the so-called regression ROC (RROC) space. The basic idea is to represent over-estimation against under-estimation. The curves are just drawn by adjusting a shift, a constant that is added (or subtracted) to the predictions, and plays a similar role as a threshold in classification. From here, we develop the notions of optimal operating condition, convexity, dominance, and explore several evaluation metrics that can be shown graphically, such as the area over the RROC curve (AOC). In particular, we show a novel and significant result: the AOC is equivalent to the error variance. We illustrate the application of RROC curves to resource estimation, namely the estimation of software project effort.I would like to thank Peter Flach and Nicolas Lachiche for some very useful comments and corrections on earlier versions of this paper, especially the suggestion of drawing normalised curves (dividing x-axis and y-axis by n). This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project Prometeo/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the respective national research councils and ministries.Hernández-Orallo, J. (2013). ROC curves for regression. Pattern Recognition. 46(12):3395-3411. https://doi.org/10.1016/j.patcog.2013.06.014S33953411461

Pre-therapy process and outcome: A review of research instruments and findings

Pre-Therapy aims at stimulating psychological contact in persons suffering psychosis. We offer a review of Pre-Therapy research instruments and findings. The Pre-Therapy Rating Scale (PTRS, Prouty, 1994) and the Evaluation Criterion for the Pre-Therapy Interview (ECPI, Dinacci, 1997) have been the two most frequently used instruments for the assessment of psychological contact. PTRS scores seem more reliable than ECPI scores, but all manuals need revision. Particular attention is needed for the rating of nonverbal behavior. A preliminary evaluation of the structure of the PTRS indicates that it is two-dimensional rather than three-dimensional. The PTRS and the ECPI can be regarded as measures of communicative contact but also as measures of the meaningfulness of communication. Preliminary outcome studies suggest that pre-post and comparative effect sizes of Pre-Therapy are large for communicative contact, but the number of participants in these studies is generally low, as is the number of systematic case studies

The role of sex differences in detecting deception in computer-mediated communication in English

[EN] While deception seems to be a common approach in interpersonal communication, most examination on interpersonal deception sees the sex of the interlocutor as unconnected with the capability to notice deceptive messages. This research studies the truth and deception detection capability of both male and female receivers when replying to both true and deceptive messages from both male and female speakers. The outcomes indicate that sex may be a significant variable in comprehending the interpersonal detection probabilities of truth and of lies. An interaction of variables including the speakers’ sex, receivers’ sex, and whether the message appears to be truthful or deceptive is created to relate to detection capability.Kuzio, A. (2018). The role of sex differences in detecting deception in computer-mediated communication in English. Journal of Computer-Assisted Linguistic Research. 2(1):39-53. doi:10.4995/jclr.2018.10521SWORD395321Aamodt, M. G., & Custer, H. (2006). Who can best catch a liar? A meta-analysis of individual differences in detecting deception. The Forensic Examiner, 15(1), 6-11.Blalock, H. M. (1972). Social Statistics. New York: McGraw Hill.Bond, C. F., & DePaulo, B. M. (2006). Accuracy of deception judgments. Personality and Social Psychology Review, 10(3), 214-234. https://doi.org/10.1207/s15327957pspr1003_2Boush, D. M., Friestad, M., & Wright, P. (2009). Deception in the marketplace : The psychology of deceptive persuasion and consumer self-protection. New York: Routledge.Camden, C., Motley, M. T., & Wilson, A. (1984). White lies in interpersonal communication: A taxonomy and preliminary investigation of social motivations. Western Journal of Speech Communication, 48(4), 309-325. https://doi.org/10.1080/10570318409374167Carlson, J., George, J., Burgoon, J., Adkins, M., & White, C. (2004). Deception in computer mediated communication. Group Decision and Negotiation, 13, 5-28. https://doi.org/10.1023/B:GRUP.0000011942.31158.d8Daft, R.L. & Lengel, R.H. (1986). Information richness: A new approach to managerial behavior and organizational design. In Cummings, L. L. & Staw, B.M. (Eds.), Research in organizational behavior 6 (pp. 191-233). Homewood, IL: JAI Press.DePaulo, B. M., Epstein, J. A., & Wyer, M. M. (1993). Sex differences in lying: How women and men deal with the dilemma of deceit. In M. Lewis, & C. Saarni (Eds.), Lying and deception in everyday life (pp. 126-147). New York: Guilford Press.DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996). Lying in everyday life. Journal of Personality and Social Psychology, 70(5), 979- 995. https://doi.org/10.1037/0022-3514.70.5.979DePaulo, B. M., Kirkendol, S. E., Tang, J., & O'Brien, T. P. (1988). The motivational impairment effect in the communication of deception: Replications and extensions. Journal of Nonverbal Behavior, 12(3), 177-202. https://doi.org/10.1007/BF00987487DePaulo, B. M., Lassiter, G. D., & Stone, J. L. (1982). Attention all determinants of success at detecting deception and truth. Personality and Social Psychology Bulletin, 8(2), 273-279. https://doi.org/10.1177/0146167282082014DePaulo, B. M., & Rosenthal, R. (1981). Telling lies. Journal of Personality and Social Psychology, 37(10), 1713-1722. https://doi.org/10.1037/0022-3514.37.10.1713Dreber, A., & Johannesson, M. (2008). Gender differences in deception. Economics Letters, 99(1), 197-199. https://doi.org/10.1016/j.econlet.2007.06.027Ekman, P., & O'Sullivan, M. (1991). Who can catch a liar? American Psychologist, 46(9), 913-920. https://doi.org/10.1037/0003-066X.46.9.913Ekman, P., O'Sullivan, M., & Frank, M. G. (1999). A few can catch a liar. Psychological Science, 10(3), 263-266. https://doi.org/10.1111/1467-9280.00147Feldman, R. S., Forrest, J. A., & Happ, B. R. (2002). Self-presentation and verbal deception: Do self-presenters lie more? Basic and Applied Social Psychology, 24(2), 163-170. https://doi.org/10.1207/153248302753674848George, J. F., & Robb, A. (2008). Deception and computer-mediated communication in daily life. Communication Reports, 21(2), 92-103. https://doi.org/10.1080/08934210802298108Hample, D. (1980). Purposes and effects of lying. Southern Speech Communication Journal, 46(1), 33-47. https://doi.org/10.1080/10417948009372474Hancock, J., Thom-Santelli, J., & Ritchie, T. (2004). Deception and design: The impact of communication technology on lying behavior. In E. Dykstra-Erickson, & M. Tscheligi (Eds.), Proceedings of the 2004 conference on human factors in computing systems (pp. 129-134). New York: Association for Computing Machinery.https://doi.org/10.1145/985692.985709Haselton, M. G., Buss, D. M., Oubaid, V., & Angleitner, A. (2005). Sex, lies, and strategic interference: The psychology of deception between the sexes. Personality and Social Psychology Bulletin, 31(1), 3-23. https://doi.org/10.1177/0146167204271303Inglehart, R., Basa-ez, M., & Moreno, A. (1998). Human values and beliefs: A crosscultural sourcebook. Ann Arbor, MI: University of Michigan Press. https://doi.org/10.3998/mpub.14858Knapp, L. M., Hart, R. P., & Dennis, H. S. (1974). An exploration of deception as a communication construct. Human Communication Research, 1(1), 15-29. https://doi.org/10.1111/j.1468-2958.1974.tb00250.xKraut, R. E. (1980). Behavioral roots of person perception: The deception judgments of customs inspectors and laymen. Journal of Personality and Social Psychology, 39(5), 784-798. https://doi.org/10.1037/0022-3514.39.5.784Kuzio, A. (2018). Cross-cultural Deception in Polish and American English in Computer-Mediated Communication. New Castle upon Tyne: Cambridge Scholars Publishing.Levine, T. R., & Kim, R. K. (2010). Some considerations for a new theory of deceptive communication. In M. S. McGlone, & M. L. Knapp (Eds.), The interplay of truth and deception: New agendas in theory and research (pp. 16-34). New York: Routledge.Levine, T. R., Park, H. S., & McCornack, S. A. (2006). Accuracy in detecting truths and lies: Documenting the "Veracity Effect". Communication Monographs, 66(2), 125- 144. https://doi.org/10.1080/03637759909376468Manstead, A., Wagner, H. L., & McDonald, C. J. (1986). Deceptive and non-deceptive communications: Sending experience, modality, and individual abilities. Journal of Nonverbal Behavior, 10(3), 147-167. https://doi.org/10.1007/BF00987612McCornack, S. A., & Parks, M. R. (1990). What women know that men don't: Sex differences in determining the truth behind deceptive messages. Journal of Social and Personal Relationships, 7(1), 107-118. https://doi.org/10.1177/0265407590071006Park, H. S., Levine, T. R., McCornack, S. A., Morrison, K., & Ferrara, M. (2002). How people really detect lies. Communication Monographs, 69(2), 144-157. https://doi.org/10.1080/714041710Prater, T., & Kiser, S. B. (2002). Lies, lies, and more lies. SAM Advanced Management Journal,67(2), 9-36.Sanchez-Pages, S., & Vorsatz, M. (2008). Enjoy the silence: An experiment on truthtelling. Experimental Economics, 12(2), 220-241. https://doi.org/10.1007/s10683-008-9211-7Seiter, J. S., Bruschke, J., & Bai, C. (2002). The acceptability of deception as a function of perceivers' culture, deceiver's intention, and deceiver-deceived relationship. Western Journal of Communication, 66(2), 158-180. https://doi.org/10.1080/10570310209374731Serota, K. B., Levine, T. R., & Boster, F. J. (2010). The prevalence of lying in America: Three studies of self-reported lies. Human Communication Research, 36(1), 2-25. https://doi.org/10.1111/j.1468-2958.2009.01366.xTurner, R. E., Edgley, C., & Olmstead, G. (1975). Information control in conversations: Honesty is not always the best policy. Kansas Journal of Sociology, 11(1), 69-89.https://doi.org/10.17161/STR.1808.6098Zuckerman, M., DePaulo, B. M., & Rosenthal, R. (1981). Verbal and nonverbal communication of deception. In L. Berkowitz (Ed.), Advances in experimental social psychology (volume 11, pp. 1-59). New York: Academic Press.https://doi.org/10.1016/S0065-2601(08)60369-

A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information

Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German

Local SGD Converges Fast and Communicates Little

Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training. The scheme can reach a linear speedup with respect to the number of workers, but this is rarely seen in practice as the scheme often suffers from large network delays and bandwidth limits. To overcome this communication bottleneck recent works propose to reduce the communication frequency. An algorithm of this type is local SGD that runs SGD independently in parallel on different workers and averages the sequences only once in a while. This scheme shows promising results in practice, but eluded thorough theoretical analysis. We prove concise convergence rates for local SGD on convex problems and show that it converges at the same rate as mini-batch SGD in terms of number of evaluated gradients, that is, the scheme achieves linear speedup in the number of workers and mini-batch size. The number of communication rounds can be reduced up to a factor of T^{1/2}---where T denotes the number of total steps---compared to mini-batch SGD. This also holds for asynchronous implementations. Local SGD can also be used for large scale training of deep learning models. The results shown here aim serving as a guideline to further explore the theoretical and practical aspects of local SGD in these applications.Comment: to appear at ICLR 2019, 19 page

Do companies reduce CSR disclosures during recessions?

Purpose: We investigate trends in prevalence and volume of CSR disclosure by the top 50 New Zealand listed companies from 2005 to 2010, i.e. from before until after the initial impact of the global financial crisis (GFC). Design/methodology/approach: We examine the annual reports of each of the companies between the years 2005 and 2010, as well as company websites for standalone CSR reports. We count the number of pages of any social and environmental disclosures in annual reports and in standalone reports for each year and use this data to assess whether overall trends can be discerned. We compare CSR disclosure trends with changes in business confidence. Findings: Our results reveal a general upward trend in CSR disclosures over the six-year period. The number of companies disclosing in their annual reports and standalone reports increased from 2005 to 2007. However, during the initial drop in business confidence in 2008 (brought on by the GFC), CSR disclosures in annual reports and standalone reports remained consistent overall with 2007. Companies operating within industries more prone to public scrutiny or those industries more sensitive to the social and environmental impacts of corporate operations actually increased their CSR disclosures, whereas other companies decreased their disclosure for an overall constant level. The upward trend resumed in 2009, but when business confidence again suffered in 2010, overall annual report CSR disclosures decreased, whereas overall standalone report disclosure continued the upward trend. In sum, during times of reduced business confidence, companies in non-environmentally-sensitive and non-socially-sensitive industries appear to buck the overall trend towards increased CSR disclosures. Originality: Many studies conclude that there is an upward trend in CSR disclosures over time. Other studies examine the impact of particular events on disclosure. However, we are not aware of any study that examines the impact of the initial phase of the GFC on the overall upward trend in CSR disclosures, i.e. whether companies subjugate CSR in favour of more pressing business priorities during times of reduced business confidence

Investigating the Evidence of Behavioral, Cognitive, and Psychiatric Endophenotypes in Autism: A Systematic Review

Substantial evidence indicates that parents of autistic individuals often display milder forms of autistic traits referred to as the broader autism phenotype (BAP). To determine if discrete endophenotypes of autism can be identified, we reviewed the literature to assess the evidence of behavioral, cognitive, and psychiatric profiles of the BAP. A systematic review was conducted using EMBASE, MEDLINE, PsycINFO, PsycEXTRA, and Global Health. Sixty papers met our inclusion criteria and results are discussed according to the proportion of studies that yield significant deficits per domain. The behavioral, cognitive, and psychiatric endophenotypes in parents of autistic probands are still not clarified; however, evidence suggests mild social/communication deficits, rigid/aloof personality traits, and pragmatic language difficulties as the most useful sociobehavioral candidate endophenotype traits. The existence of deficits in the cognitive domain does suggest familial vulnerability for autism. Furthermore, increased depressed mood and anxiety can also be useful markers; however, findings should be interpreted with caution because of the small number of studies in such heterogeneously broad domains and several methodological limitations

Family Dynamics and Personal Strengths among Dementia Caregivers in Argentina

This study examined whether healthier family dynamics were associated with higher personal strengths of resilience, sense of coherence, and optimism among dementia caregivers in Argentina. Caregivers are usually required to assist individuals with dementia, and family members have typically fulfilled that role. Personal strengths such as resilience, sense of coherence, and optimism have been shown to protect caregivers from some of the negative experiences of providing care, though the family-related variables associated with these personal strengths are largely unknown. Hierarchical multiple regressions investigated the extent to which family dynamics variables are associated with each of the caregiver personal strengths after controlling for demographic and caregiver characteristics. A sample of 105 caregivers from Argentina completed a set of questionnaires during a neurologist visit. Family dynamics explained 32% of the variance in resilience and 39% of the variance in sense of coherence. Greater family empathy and decreased family problems were uniquely associated with higher resilience. Greater communication and decreased family problems were uniquely associated with higher sense of coherence. Optimism was not found to be significantly associated with family dynamics. These results suggest that caregiver intervention research focused on the family may help improve caregiver personal strengths in Argentina and other Latin American countries