Affect and Inference in Bayesian Knowledge Tracing with a Robot Tutor

In this paper, we present work to construct a robotic tutoring system that can assess student knowledge in real time during an educational interaction. Like a good human teacher, the robot draws on multimodal data sources to infer whether students have mastered language skills. Specifically, the model extends the standard Bayesian Knowledge Tracing algorithm to incorporate an estimate of the student's affective state (whether he/she is confused, bored, engaged, smiling, etc.) in order to predict future educational performance. We propose research to answer two questions: First, does augmenting the model with affective information improve the computational quality of inference? Second, do humans display more prominent affective signals in an interaction with a robot, compared to a screen-based agent? By answering these questions, this work has the potential to provide both algorithmic and human-centered motivations for further development of robotic systems that tightly integrate affect understanding and complex models of inference with interactive, educational robots.National Science Foundation (U.S.) (Grant CCF-1138986)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant No. 1122374

Verkostojohtamisen opas

Perinteisiä hierarkkisia organisaatioita vaivaa pahimmillaan jäykkyys ja kykenemättömyys ratkaista komplekseja ongelmia ja siksi onkin ennustettu, että organisaatioiden aika olisi murroksessa ja sitä seuraisi nk. verkostojen aika. Verkosto on useamman toimijan välinen vastavuoroinen ja keskinäiseen tiedon sekä resurssien jakamiseen perustuva yhteistyösuhde ja arvontuottamismalli, jonka avulla pyritään yhdistämään toimijoiden osaamista ja voimavaroja synergiaetujen saamiseksi. Verkostojen vahvuus tulee erityisesti esille muuttuvassa ja ennakoimattomassa toimintaympäristössä, jossa tarvitaan innovatiivisuutta, ketteryyttä, joustavuutta ja dynaamista organisoitumista. Verkostot täydentävät julkishallinnon perinteisten organisoitumistapojen keinovalikoimaa ja tarjoavat ketterän tavan ratkaista kompleksisia ja hallinnonalat ylittäviä haasteita, jotka edellyttävät laajaa yhteistyötä ja moninäkökulmaisuutta. Verkostotoiminnan ilmeisistä hyödyistä huolimatta, voivat ne jäädä myös saavuttamatta ja käytettävä aika sekä resurssit valua hukkaan. Verkostoa ja verkostoitumista aktiivisesti johtamalla voidaan kuitenkin ratkaisevasti vaikuttaa siihen, millaista lisäarvoa verkosto jäsenilleen ja sidosryhmilleen tuottaa. Tämän oppaan idea syntyi Sitran Siilonmurtaja-hankkeen yhteydessä. Hankkeessa tarkasteltiin valtionhallinnon sisällä tapahtuvaa osaamisen liikkuvuutta sekä ilmiölähtöistä toimintaa. Ohjelman aikana tunnistettiin sekä verkostotoiminnan lisääntyminen valtiohallinnon sisällä, mutta myös ilmeinen tarve verkostotoiminnan laadun kehittämiselle. Tämä opas on eräänlainen Siilonmurtajat-hankkeen yhteenveto, mutta samalla sen jatkumo. Oppaan ensisijainen tehtävä on tukea verkostovastuussa olevia henkilöitä tehtävässään, mutta samalla se voi toimia myös verkostolaisten oppaana ja on soveltuvin osin hyödynnettävissä myös perinteisen työryhmätyöskentelyn tukena

Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset

Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing. Traditional approaches frequently require hand-crafted speech features and segmentation of speaker turns. In this work, we design end-to-end deep learning methods to recognize each person's affective expression in an audio stream with two speakers, automatically discovering features and time regions relevant to the target speaker's affect. We integrate a local attention mechanism into the end-to-end architecture and compare the performance of three attention implementations -- one mean pooling and two weighted pooling methods. Our results show that the proposed weighted-pooling attention solutions are able to learn to focus on the regions containing target speaker's affective information and successfully extract the individual's valence and arousal intensity. Here we introduce and use a "dyadic affect in multimodal interaction - parent to child" (DAMI-P2C) dataset collected in a study of 34 families, where a parent and a child (3-7 years old) engage in reading storybooks together. In contrast to existing public datasets for affect recognition, each instance for both speakers in the DAMI-P2C dataset is annotated for the perceived affect by three labelers. To encourage more research on the challenging task of multi-speaker affect sensing, we make the annotated DAMI-P2C dataset publicly available, including acoustic features of the dyads' raw audios, affect annotations, and a diverse set of developmental, social, and demographic profiles of each dyad.Comment: Accepted by the 2020 International Conference on Multimodal Interaction (ICMI'20

Tega: A social robot

Tega is a new expressive “squash and stretch”, Android-based social robot platform, designed to enable long-term interactions with children

Allele-specific RNA interference prevents neuropathy in Charcot-Marie-Tooth disease type 2D mouse models.

Gene therapy approaches are being deployed to treat recessive genetic disorders by restoring the expression of mutated genes. However, the feasibility of these approaches for dominantly inherited diseases - where treatment may require reduction in the expression of a toxic mutant protein resulting from a gain-of-function allele - is unclear. Here we show the efficacy of allele-specific RNAi as a potential therapy for Charcot-Marie-Tooth disease type 2D (CMT2D), caused by dominant mutations in glycyl-tRNA synthetase (GARS). A de novo mutation in GARS was identified in a patient with a severe peripheral neuropathy, and a mouse model precisely recreating the mutation was produced. These mice developed a neuropathy by 3-4 weeks of age, validating the pathogenicity of the mutation. RNAi sequences targeting mutant GARS mRNA, but not wild-type, were optimized and then packaged into AAV9 for in vivo delivery. This almost completely prevented the neuropathy in mice treated at birth. Delaying treatment until after disease onset showed modest benefit, though this effect decreased the longer treatment was delayed. These outcomes were reproduced in a second mouse model of CMT2D using a vector specifically targeting that allele. The effects were dose dependent, and persisted for at least 1 year. Our findings demonstrate the feasibility of AAV9-mediated allele-specific knockdown and provide proof of concept for gene therapy approaches for dominant neuromuscular diseases

Expanding the diversity of mycobacteriophages: insights into genome architecture and evolution.

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists

Association of the PHACTR1/EDN1 genetic locus with spontaneous coronary artery dissection

Background: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndromes (ACS) afflicting predominantly younger to middle-aged women. Observational studies have reported a high prevalence of extracoronary vascular anomalies, especially fibromuscular dysplasia (FMD) and a low prevalence of coincidental cases of atherosclerosis. PHACTR1/EDN1 is a genetic risk locus for several vascular diseases, including FMD and coronary artery disease, with the putative causal noncoding variant at the rs9349379 locus acting as a potential enhancer for the endothelin-1 (EDN1) gene. Objectives: This study sought to test the association between the rs9349379 genotype and SCAD. Methods: Results from case control studies from France, United Kingdom, United States, and Australia were analyzed to test the association with SCAD risk, including age at first event, pregnancy-associated SCAD (P-SCAD), and recurrent SCAD. Results: The previously reported risk allele for FMD (rs9349379-A) was associated with a higher risk of SCAD in all studies. In a meta-analysis of 1,055 SCAD patients and 7,190 controls, the odds ratio (OR) was 1.67 (95% confidence interval [CI]: 1.50 to 1.86) per copy of rs9349379-A. In a subset of 491 SCAD patients, the OR estimate was found to be higher for the association with SCAD in patients without FMD (OR: 1.89; 95% CI: 1.53 to 2.33) than in SCAD cases with FMD (OR: 1.60; 95% CI: 1.28 to 1.99). There was no effect of genotype on age at first event, P-SCAD, or recurrence. Conclusions: The first genetic risk factor for SCAD was identified in the largest study conducted to date for this condition. This genetic link may contribute to the clinical overlap between SCAD and FMD

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Land Grant Application- Spaulding, Samuel (Searsmont)

Land grant application submitted to the Maine Land Office for Samuel Spaulding for service in the Revolutionary War.https://digitalmaine.com/revolutionary_war_me_land_office/1838/thumbnail.jp