Background
Post Traumatic Stress Disorder (PTSD) is a disorder characterized by exposure to some traumatic event leaving the individual with a variety of symptoms that can result in debilitating disability. Treatment has historically been centered around Cognitive Processing Therapy (CPT) and literature has shown a significant risk of suicide in those left untreated. Thus, the importance of appropriate healthcare utilization is important for addressing morbidity and mortality of PTSD. Determining appropriate utilization is not well recorded in the literature though some studies show increased utilization with increased symptom severity. The aim of
this study is to replicate these findings and determine if there exists any range of symptom severity that over or under-utilizes the healthcare system.

Methods
Data was collected by Rosen et al. 2012 through Project VALOR, a longitudinal registry of PTSD in combat exposed veterans of Afghanistan and Iraqi military theaters. PTSD checklist (PCL) scores and data regarding number of healthcare utilizations for PTSD treatment year to year were recorded over a five-year period. Utilization of healthcare services for PTSD were stratified by this study into categories and compared against PCL scores using an ordinal logistical regression model to predict probability of being placed in a higher utilization category. Quadratic analysis was conducted on the PCL scores to determine linear relationship of findings.

Results
Ordinal logistical regression of mean centered PCL value was 1.042 (p=0.000). Quadratic effect of ordinal logistical regression of mean centered PCL2 was determined to be 0.99 (p=0.061).

Conclusion
Self-reported PCL scores are positively predictive of healthcare utilization. For every 1 increase in PCL score, there is a predicted 4.29% increased chance of being placed into the next highest utilization category (p=0.000). In addition, the relationship between PCL score and healthcare utilization was determined to be log-linear due to quadratic effect being found not significant 0.99 (p=0.061). This indicates no PCL score is correlated with increased probability of increased or decreased healthcare utilization. This likely represents healthcare services are appropriately being administered based on symptom severity and no group is over or under-utilizing resources. Ultimately, this study replicates previous literature that increased severity is associated with increased healthcare utilization while also quantifying the predictive value of increasing PCL scores. Currently the healthcare system is correctly identifying and capturing individuals who would in theory benefit from increased healthcare utilization. Additional research into capture strength would be beneficial in determining impact of hospital system capture rate of at risk patients.

Increasing PTSD Symptom Severity Predicts Increasing Healthcare Utilization Among OEF/OIF Veterans.

Background: Social media (SoMe) has revolutionized the way people communicate and transformed various aspects of modern life. The COVID-19 pandemic necessitated reduced in person interaction and the ACGME recommended a shift to virtual residency interviews. This shift increased the reliance of residency programs and medical school graduates on SoMe for recruitment and information. This study aims to determine the impact of social media during the anesthesiology residency application cycle and characterize the content that anesthesiology residency programs can use to most effectively optimize programs’ ability to recruit applicants.
Methods: This project was approved by the Augusta University Institutional Review Board. An anonymous online survey was distributed to residency program directors and coordinators at all US ACGME-accredited anesthesiology residency programs for dissemination to their residents. This 17-question survey was created on Qualtrics and included questions regarding demographics, current PGY year, type of medical degree, interview type, social media use, and social media impact. Microsoft Excel was used to perform descriptive statistics and t-tests where applicable. Responses were summarized using frequencies and percentages for categorical data. The chi-square test (or Fisher exact test, as appropriate) was used to examine the association between categorical variables. Theme analysis was used for the open-ended questions.

Results: A total of 109 respondents completed the survey. Respondents were similarly distributed among the four PGY years. Participants under 30 years older were significantly more likely to look at SoMe while applying for residency compared to individual older than 35 years old (p<0.01). Female respondents used Instagram significantly more often than males, and males used YouTube and Reddit significantly more often than females (p < 0.05). Participants primarily sought information regarding program culture (58%) and lifestyle (50%) from SoMe. Regarding improving efficacy of SoMe outreach, common themes identified include resident-made content, using different elements of SoMe posts to increase engagement, and increasing accessibility to SoMe via increasing number of platforms.

Conclusion: This study presents a national analysis of SoMe impact on the anesthesiology residency application process. To cultivate a more purposeful approach to SoMe usage among anesthesiology residency programs, a meticulous examination of the content that is most beneficial to residency applicants is paramount. This study underscores the importance of using social media’s original "social" function by crafting posts that spotlight program culture and residency lifestyle in a clear and welcoming manner, all with the goal of engaging and appealing to anesthesiology residency applicants.

Making Social Media “Social” May Improve Anesthesiology Residency Program Outreach

When assessing children in laboratory experiments, the measured responses also contain task-irrelevant participant-level variability (“noise”) and measurement noise. Since experimental data are used to make inferences of development of cognitive capabilities with age, it is important to know if reliability of the used measurements depends on child age. Any systematic age-dependent changes in reliability could result in misleading developmental trajectories, as lower reliability will necessarily result in smaller effect sizes. This paper examines age-dependency of task-independent measurement variability in early childhood (3–40 months) by analyzing two large-scale datasets of participant-level experimental responses: the ManyBabies infant-directed speech preference (MB-IDS) dataset and a saccadic reaction time (SRT) dataset collected from rural South Africa. Analysis of participant- and study-level data reveals that MB-IDS shows comparable reliability across the included age range. In contrast, SRTs reflect systematically increasing measurement consistency with increasing age. Potential reasons and implications of this divergence are briefly discussed.

Is Reliability of Cognitive Measures in Children Dependent on Participant Age? A Case Study with Two Large-Scale Datasets

Previous research suggests that a verb’s meaning is learned partly through the aggregated profile of syntactic frames associated with it. For example, “turn” occurs with transitive and intransitive frames in causative alternation (“He turned the car”/“The car turned”), indicating it is a causal verb. Some evidence demonstrates that young children combine multiple frames to map verbs to appropriate events. However, previous work always presented these frames together, in a single dialogue. What remains unknown is how verb learning occurs when the frames are separated, uttered in different referential contexts, as is likely in children’s everyday life. In a series of cross-situational word-learning experiments, we show that both adults and three-year-olds generalize verb meanings across different syntactic frames in a cross-situational learning task. These results shed light on the cross-situational mechanisms of syntactic bootstrapping.

Evidence for Cross-situational Syntactic Bootstrapping: Three-year olds Generalize Verb Meaning across Different Syntactic Frames

Word associations are among the most common paradigms for studying the human mental lexicon. While their structure and underlying relation types have been widely studied, surprisingly little attention has been given to the question of why participants produce the observed associations. Answering this question would not only advance understanding of human cognition, but could also aid machines in learning and representing basic commonsense knowledge. Here, we introduce WAX: a large, crowd-sourced data set of 19K English word associations paired with human-generated free-text explanations and relation type labels. We present an efficient framework for eliciting associations together with explanations, and a comprehensive analysis of the emerging types of explanations and relations. We test language models in their ability to predict associations and generate explanations, demonstrating that models struggle to capture the diversity of human-produced associations, and suggesting WAX as a fertile resource for future research.

WAX: A Dataset for Word Association eXplanations

Developmental dyscalculia (DD) is a neurodevelopmental disorder characterized by persistent poor math performance despite normal intelligence and education opportunities. Existing behavioral and neuroimaging studies have demonstrated that quantity processing deficits in DD are accompanied by aberrant brain functions and neurobiological alterations. Although theories have argued that the behavioral impairments observed in DD result from neurobiological deficiency and imbalance of excitatory and inhibitory signals in the brain, these hypotheses are difficult to test in human subjects. Therefore, in the current study, we implemented convolutional neural network models and tested the causal influence of neural capacity (i.e., number of units) and system excitability (i.e., the slope of activation) during the learning of quantity information. For both symbolic and non-symbolic processing, we observed that reducing the number of units did not lead to changes in learning performance. In contrast, increased excitability largely impaired the accuracy of learning, especially for the non-symbolic representations. Therefore, our model simulations provided direct evidence that increased excitability in the brain could result in behavioral impairments in learning quantity information, potentially suggesting a neurobiological basis for DD.

Limited Neural Capacity and Hyper-Excitability Affect Quantity Processing: A Computational Account

Learning verbs is critical to learning one’s native language. Two new studies with comparison trials and additional test trials reveal better verb learning by both Japanese- and English-speaking children than prior studies (Imai et al., 2005; 2008). Specifically, 2½-, 3½- and 4½-year-old Japanese-speaking children (total n=170; n= 43 2.5s; n= 48 3.5s, n= 48 4.5s) in Japan and English-speaking children in the US (total n=107 to date; n= 28 2.5s; n= 36 3.5s; n= 43 4.5s) were shown 3 events linked to each of 4 novel verbs, either in a Progressive Alignment (similar before varied events) or All Varied condition; children pointed to new events at test. Multivariate ANOVAs in both languages show children benefit from seeing high than low similarity events during verb learning, particularly at younger ages. Links to key theories in verb learning, including structure mapping and statistical learning, will be discussed.

High Similarity Comparison Experience Helps Japanese- and English-speaking Children Learn New Verbs

On-line decision problems – in which a decision is made based on a sequence of past events without knowledge of the future – have been extensively studied in theoretical computer science. A famous example is the Prediction from Expert Advice problem,
in which an agent has to make a decision informed by the predictions of a set of experts. An optimal solution to this problem is the Multiplicative Weights Update Method (MWUM).
In this paper, we investigate how humans behave in a Prediction from Expert Advice task. We compare MWUM and several other algorithms proposed in the computer science literature against human behavior. We find that MWUM provides the best fit to people’s choices.

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms

Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly available datasets which have only the question field and significantly differ from real-life datasets. This paper proposes methods to improve the intent discovery pipeline deployed in a large e-commerce platform. We show the benefit of pre-training language models on in-domain data: both self-supervised and with weak supervision. We also devise the best method to utilize the conversational structure (i.e., question and answer) of real-life datasets during fine-tuning for clustering tasks, which we call Conv. All our methods combined to fully utilize real-life datasets give up to 33pp performance boost over state-of-the-art Constrained Deep Adaptive Clustering (CDAC) (Lin et al., 2020) model for question only. By comparison CDAC model for the question data only gives only up to 13pp performance boost over the naive baseline.

Going beyond research datasets: Novel intent discovery in the industry setting

Helpful reviews have been essential for the success of e-commerce services, as they help customers make quick purchase decisions and benefit the merchants in their sales. While many reviews are informative, others provide little value and may contain spam, excessive appraisal, or unexpected biases. With the large volume of reviews and their uneven quality, the problem of detecting helpful reviews has drawn much attention lately. Existing methods for identifying helpful reviews primarily focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. Moreover, the helpfulness votes suffer from scarcity for less popular products and recently submitted (a.k.a., cold-start) reviews. To address these challenges, we introduce a dataset and develop a model that integrates the reviewer's expertise, derived from the past review history of the reviewers, and the temporal dynamics of the reviews to automatically assess review helpfulness. We conduct experiments on our dataset to demonstrate the effectiveness of incorporating these factors and report improved results compared to several well-established baselines.

On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction

Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly available datasets which have only the question field and significantly differ from real-life datasets. This paper proposes methods to improve the intent discovery pipeline deployed in a large e-commerce platform. We show the benefit of pre-training language models on in-domain data: both self-supervised and with weak supervision. We also devise the best method to utilize the conversational structure (i.e., question and answer) of real-life datasets during fine-tuning for clustering tasks, which we call Conv. All our methods combined to fully utilize real-life datasets give up to 33pp performance boost over state-of-the-art Constrained Deep Adaptive Clustering (CDAC) model for question only. By comparison CDAC model for the question data only gives only up to 13pp performance boost over the naive baseline.

According to the main international reports, more pervasive industrial and business-process automation, thanks to machine learning and advanced analytic tools, will unlock more than 14 trillion USD worldwide annually by 2030. In the specific case of pricing problems-which constitute the class of problems we investigate in this paper-, the estimated unlocked value will be about 0.5 trillion USD per year. In particular, this paper focuses on pricing in e-commerce when the objective function is profit maximization and only transaction data are available. This setting is one of the most common in real-world applications. Our work aims to find a pricing strategy that allows defining optimal prices at different volume thresholds to serve different classes of users. Furthermore, we face the major challenge, common in real-world settings, of dealing with limited data available. We design a two-phase online learning algorithm, namely PVD-B, capable of exploiting the data incrementally in an online fashion. The algorithm first estimates the demand curve and retrieves the optimal average price, and subsequently it offers discounts to differentiate the prices for each volume threshold. We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B-corresponding to A configuration-has been compared with human pricing specialists-corresponding to B configuration. At the end of the experiment, our algorithm produced a total turnover of about 300 KEuros, outperforming the B configuration performance by about 55%. The Italian company we collaborated with decided to adopt our algorithm for more than 1,200 products since January 2022.

Underline digital video library

Increasing PTSD Symptom Severity Predicts Increasing Healthcare Utilization Among OEF/OIF Veterans.

Making Social Media “Social” May Improve Anesthesiology Residency Program Outreach

Is Reliability of Cognitive Measures in Children Dependent on Participant Age? A Case Study with Two Large-Scale Datasets

Evidence for Cross-situational Syntactic Bootstrapping: Three-year olds Generalize Verb Meaning across Different Syntactic Frames

WAX: A Dataset for Word Association eXplanations

Limited Neural Capacity and Hyper-Excitability Affect Quantity Processing: A Computational Account

High Similarity Comparison Experience Helps Japanese- and English-speaking Children Learn New Verbs

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms

Going beyond research datasets: Novel intent discovery in the industry setting

On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction

Going beyond research datasets: Novel intent discovery in the industry setting

IAAI-6473 - Dynamic Pricing with Volume Discounts in Online Settings

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