The item versus the object in memory: on the implausibility of overwriting as a mechanism for forgetting in short-term memory

The nature of forgetting in short-term memory remains a disputed topic, with much debate focussed upon whether decay plays a fundamental role (Berman et al., 2009; Altmann and Schunn, 2012; Barrouillet et al., 2012; Neath and Brown, 2012; Oberauer and Lewandowsky, 2013; Ricker et al., 2014) but much less focus on other plausible mechanisms. One such mechanism of long-standing in auditory memory is overwriting (e.g., Crowder and Morton, 1969) in which some aspects of a representation are “overwritten” and rendered inaccessible by the subsequent presentation of a further item. Here, we review the evidence for different forms of overwriting (at the feature and item levels) and examine the plausibility of this mechanism both as a form of auditory memory and when viewed in the context of a larger hearing, speech and language understanding system

Application of Gray's theory of personality to the DSM-III-R personality disorders : multivariate and behavioral findings

Recent years have witnessed a rapid growth of published reports on the descriptive features associated with the personality disorders. Despite growing recognition of the existence and clinical relevance of these disorders, there has been relatively little systematic experimental research performed, perhaps because of an absence of a testable, guiding theoretical framework. In the recognition that descriptive studies without the benefit of a guiding theoretical framework can only provide limited understanding, this study examined the applicability of Jeffrey Gray's structural and behavioral theory of personality to a subset of the DSM-HI-R personality disorders. Two independent samples, a normative and a research sample, were employed in this study to test some of the basic assumptions of Gray's theory. The normative sample consisted of 477 college undergraduates. This sample's primary roles in this study included the evaluation of some of the structural assumptions of Gray's model as well as the provision of a context for understanding the smaller research sample. The research sample, self-selected based on individual perceptions of oneself as being anxious or impulsive, was composed of 77 persons who responded to advertisements in local periodicals. This sample's principle roles in this research included: (a) the further evaluation of some of the structural assumptions of Gray's theory, (b) the evaluation of Gray's behavioral predictions arising from his structural model, and (c) the evaluation of the applicability of a subset of the DSM-III-R personality disorders, specifically the "anxious-fearful" and "erratic-dramatic" disorders, to Gray's structural and behavioral theory

Multi-omics bioactivity profile-based chemical grouping and read-across:a case study with Daphnia magna and azo dyes

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this ‘omics approach are identified, the study illustrates an effective statistical approach for building chemical groups

Economic Analysis of the Investments in Battery Energy Storage Systems: Review and Current Perspectives

energy storage systems; economic feasibility; techno-economic analysis; power quality; renewable energ

THE WESTERN TENNESSEE SHELL MOUND ARCHAIC: PREHISTORIC OCCUPATION IN THE LOWER TENNESSEE RIVER VALLEY BETWEEN 9000 AND 2500 CAL YR BP

Data from seven Middle and Late Archaic sites in western Tennessee dating to ca. 8900 – 3200 cal BP are used explore how shell middens and mounds were created and used. The study sites – Eva (40BN12), Big Sandy (40HY18), Kays Landing (40HY13), Cherry (40BN74), Ledbetter Landing (40BN25), McDaniel (40BN77), and Oak View (40DR1) – were excavated during the Great Depression prior to the construction of the Kentucky Dam by the Tennessee Valley Authority. A high-resolution chronology of site use was developed, based on existing older radiocarbon assays and 50 new AMS determinations. These chronological data were used in conjunction with analyses of curated collections at the Frank H. McClung Museum to produce a synthesis of human occupation, including shell fish use, in this part of the Tennessee River Valley. The temporal data also formed the basis for in-depth examination of the composition of, and variation in, artifact assemblages, cultural features, and burial populations through time to assess changes in the intensity and manner of site use. Results indicate that shellfishing appeared in western Tennessee by the mid-9th millennium cal BP, and continued sporadically throughout the Middle and Late Archaic periods until at least the mid-3rd millennium cal BP. Shell-bearing sites accumulated over many centuries. Although raw numbers of artifacts and human burials recovered from them are impressive, when contextualized within a temporal span of many centuries, they suggest periodic, or even sporadic, occupation rather than continuous use. It has been suggested, based on burial numbers, that freshwater shell-bearing sites resulted from feasting and other activities associated with funerary rituals. However, average annual burial rates for the study sites, when compared with modern and historic ethnographic data on hunter-gatherer mortality rates, suggest that these burial populations represent only a tiny fraction of the total number of deaths that would have occurred during the time the sites formed, and may be better interpreted as the long-term aggregated result of occasional deaths among groups who periodically occupied these sites

Exploratory Browsing

In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing. We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives. We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities. The main studied media types are photographs and music. The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections

Exploring the topical structure of short text through probability models : from tasks to fundamentals

Recent technological advances have radically changed the way we communicate. Today’s communication has become ubiquitous and it has fostered the need for information that is easier to create, spread and consume. As a consequence, we have experienced the shortening of text messages in mediums ranging from electronic mailing, instant messaging to microblogging. Moreover, the ubiquity and fast-paced nature of these mediums have promoted their use for unthinkable tasks. For instance, reporting real-world events was classically carried out by news reporters, but, nowadays, most interesting events are first disclosed on social networks like Twitter by eyewitness through short text messages. As a result, the exploitation of the thematic content in short text has captured the interest of both research and industry. Topic models are a type of probability models that have traditionally been used to explore this thematic content, a.k.a. topics, in regular text. Most popular topic models fall into the sub-class of LVMs (Latent Variable Models), which include several latent variables at the corpus, document and word levels to summarise the topics at each level. However, classical LVM-based topic models struggle to learn semantically meaningful topics in short text because the lack of co-occurring words within a document hampers the estimation of the local latent variables at the document level. To overcome this limitation, pooling and hierarchical Bayesian strategies that leverage on contextual information have been essential to improve the quality of topics in short text. In this thesis, we study the problem of learning semantically meaningful and predictive representations of text in two distinct phases: • In the first phase, Part I, we investigate the use of LVM-based topic models for the specific task of event detection in Twitter. In this situation, the use of contextual information to pool tweets together comes naturally. Thus, we first extend an existing clustering algorithm for event detection to use the topics learned from pooled tweets. Then, we propose a probability model that integrates topic modelling and clustering to enable the flow of information between both components. • In the second phase, Part II and Part III, we challenge the use of local latent variables in LVMs, specially when the context of short messages is not available. First of all, we study the evaluation of the generalization capabilities of LVMs like PFA (Poisson Factor Analysis) and propose unbiased estimation methods to approximate it. With the most accurate method, we compare the generalization of chordal models without latent variables to that of PFA topic models in short and regular text collections. In summary, we demonstrate that by integrating clustering and topic modelling, the performance of event detection techniques in Twitter is improved due to the interaction between both components. Moreover, we develop several unbiased likelihood estimation methods for assessing the generalization of PFA and we empirically validate their accuracy in different document collections. Finally, we show that we can learn chordal models without latent variables in text through Chordalysis, and that they can be a competitive alternative to classical topic models, specially in short text.Els avenços tecnològics han canviat radicalment la forma que ens comuniquem. Avui en dia, la comunicació és ubiqua, la qual cosa fomenta l’ús de informació fàcil de crear, difondre i consumir. Com a resultat, hem experimentat l’escurçament dels missatges de text en diferents medis de comunicació, des del correu electrònic, a la missatgeria instantània, al microblogging. A més de la ubiqüitat, la naturalesa accelerada d’aquests medis ha promogut el seu ús per tasques fins ara inimaginables. Per exemple, el relat d’esdeveniments era clàssicament dut a terme per periodistes a peu de carrer, però, en l’actualitat, el successos més interessants es publiquen directament en xarxes socials com Twitter a través de missatges curts. Conseqüentment, l’explotació de la informació temàtica del text curt ha atret l'interès tant de la recerca com de la indústria. Els models temàtics (o topic models) són un tipus de models de probabilitat que tradicionalment s’han utilitzat per explotar la informació temàtica en documents de text. Els models més populars pertanyen al subgrup de models amb variables latents, els quals incorporen varies variables a nivell de corpus, document i paraula amb la finalitat de descriure el contingut temàtic a cada nivell. Tanmateix, aquests models tenen dificultats per aprendre la semàntica en documents curts degut a la manca de coocurrència en les paraules d’un mateix document, la qual cosa impedeix una correcta estimació de les variables locals. Per tal de solucionar aquesta limitació, l’agregació de missatges segons el context i l’ús d’estratègies jeràrquiques Bayesianes són essencials per millorar la qualitat dels temes apresos. En aquesta tesi, estudiem en dos fases el problema d’aprenentatge d’estructures semàntiques i predictives en documents de text: En la primera fase, Part I, investiguem l’ús de models temàtics amb variables latents per la detecció d’esdeveniments a Twitter. En aquest escenari, l’ús del context per agregar tweets sorgeix de forma natural. Per això, primer estenem un algorisme de clustering per detectar esdeveniments a partir dels temes apresos en els tweets agregats. I seguidament, proposem un nou model de probabilitat que integra el model temàtic i el de clustering per tal que la informació flueixi entre ambdós components. En la segona fase, Part II i Part III, qüestionem l’ús de variables latents locals en models per a text curt sense context. Primer de tot, estudiem com avaluar la capacitat de generalització d’un model amb variables latents com el PFA (Poisson Factor Analysis) a través del càlcul de la likelihood. Atès que aquest càlcul és computacionalment intractable, proposem diferents mètodes d estimació. Amb el mètode més acurat, comparem la generalització de models chordals sense variables latents amb la del models PFA, tant en text curt com estàndard. En resum, demostrem que integrant clustering i models temàtics, el rendiment de les tècniques de detecció d’esdeveniments a Twitter millora degut a la interacció entre ambdós components. A més a més, desenvolupem diferents mètodes d’estimació per avaluar la capacitat generalizadora dels models PFA i validem empíricament la seva exactitud en diverses col·leccions de text. Finalment, mostrem que podem aprendre models chordals sense variables latents en text a través de Chordalysis i que aquests models poden ser una bona alternativa als models temàtics clàssics, especialment en text curt.Postprint (published version

The Relationship between Post-Stroke Depression and Post-Stroke Dysphagia

Background: Post-stroke dysphagia and post-stroke depression (PSD) can have devastating effects on stroke survivors and substantial financial impacts on the healthcare system; however, there is a dearth of research examining this patient population. Thus, we studied the incidence, risk, and cost of PSD in patients with post-stroke dysphagia. Methods: We conducted a retrospective, cross-sectional study of individuals with a primary diagnosis of acute ischemic stroke and secondary diagnoses of dysphagia and/or depression using administrative claims data from the 2017 Medicare 5% Limited Data Set. Additionally, we developed a novel dysphagia severity index for use with administrative data and applied it to our data sets. Results: Persons with post-stroke dysphagia were as, or slightly more, likely to have PSD compared to the general stroke population. Those with dysphagia (irrespective of severity) had greater odds and hazard of diagnosis of PSD in the 90 days after discharge, and those with dysphagia and PSD incurred higher healthcare costs. Conclusion: Our results supported an association between post-stroke dysphagia and PSD and that the presence of PSD in patients with post-stroke dysphagia increased post-discharge cost. Thus, future research is warranted to further explore the effects of PSD on post-stroke dysphagia