The presence of occlusions has provided substantial challenges to typically-powerful object recognition algorithms. Additional sources of information can be extremely valuable to reduce errors caused by occlusions. Scene context is known to aid in object recognition in biological vision. In this work, we attempt to add robustness into existing Region Proposal Network-Deep Convolutional Neural Network (RPN-DCNN) object detection networks through two distinct scene-based information fusion techniques. We present one algorithm under each methodology: the first operates prior to prediction, selecting a custom object network to use based on the identified background scene, and the second operates after detection, fusing scene knowledge into initial object scores output by the RPN. We demonstrate our algorithms on challenging datasets featuring partial occlusions, which show overall improvement in both recall and precision against baseline methods. In addition, our experiments contrast multiple training methodologies for occlusion handling, finding that training on a combination of both occluded and unoccluded images demonstrates an improvement over the others. Our method is interpretable and can easily be adapted to other datasets, offering many future directions for research and practical applications.

BACKGROUND:Timely and appropriate medical care after concussion presents a difficult public health problem. Concussion identification and treatment rely heavily on self-report, but more than half of concussions go unreported or are reported after a delay. If incomplete self-report increases exposure to harm, blood biomarkers may objectively indicate this neurobiological dysfunction.PURPOSE/HYPOTHESIS:The purpose of this study was to compare postconcussion biomarker levels between individuals with different previous concussion diagnosis statuses and care-seeking statuses. It was hypothesized that individuals with undiagnosed concussions and poorer care seeking would show altered biomarker profiles.STUDY DESIGN:Cohort study; Level of evidence, 3.METHODS:Blood samples were collected from 287 military academy cadets and collegiate athletes diagnosed with concussion in the Advanced Research Core of the Concussion Assessment, Research and Education Consortium. The authors extracted each participant's self-reported previous concussion diagnosis status (no history, all diagnosed, ≥1 undiagnosed) and whether they had delayed or immediate symptom onset, symptom reporting, and removal from activity after the incident concussion. The authors compared the following blood biomarkers associated with neural injury between previous concussion diagnosis status groups and care-seeking groups: glial fibrillary acidic protein, ubiquitin c-terminal hydrolase-L1 (UCH-L1), neurofilament light chain (NF-L), and tau protein, captured at baseline, 24 to 48 hours, asymptomatic, and 7 days after unrestricted return to activity using tests of parallel profiles.RESULTS:The undiagnosed previous concussion group (n = 21) had higher levels of NF-L at 24- to 48-hour and asymptomatic time points relative to all diagnosed (n = 72) or no previous concussion (n = 194) groups. For those with delayed removal from activity (n = 127), UCH-L1 was lower at 7 days after return to activity than that for athletes immediately removed from activity (n = 131). No other biomarker differences were observed.CONCLUSION:Individuals with previous undiagnosed concussions or delayed removal from activity showed some different biomarker levels after concussion and after clinical recovery, despite a lack of baseline differences. This may indicate that poorer care seeking can create neurobiological differences in the concussed brain.

Objective Approximately 50% of concussions go undiagnosed and are never treated, but it is not clear how this impacts subsequent concussion recovery. Our objective was to compare post-concussion outcomes among individuals with 1) No concussion history, 2) All previous concussions diagnosed, and 3) 1+ previous concussions undiagnosed. Design Longitudinal cohort. Setting Clinical. Participants Through the CARE Consortium, 2,717 military academy cadets and college student-athletes were diagnosed with concussion. Based on baseline self-report, we established concussion diagnosis status history (1. No history [n=1,743], 2. All previous concussions diagnosed [n=755], 3. 1+ previous concussions undiagnosed [n=219]). Outcome Measures We compared symptom severity (SCAT3), psychological status (Brief Symptom Inventory [BSI-18]), cognition, and balance at pre-injury baseline (before subsequent concussion), 24–48 hours, asymptomatic, and unrestricted return-to-activity after subsequent concussion using tests of parallel profiles while controlling for several covariates. Main Results Athletes/Cadets with 1+ previous undiagnosed concussions reported higher total symptom severity at baseline relative to both the no history (p=0.007, Cohen's d=0.372) and all previous concussions diagnosed groups (p=0.001, Cohen's d=0.247). The 1+ previous concussions undiagnosed group performed worse on verbal memory at baseline relative to the all previous concussions diagnosed group (p=0.014, Cohen's d=0.221). Across all time points, individuals with 1+ previous concussions undiagnosed had significantly higher BSI-18 total scores relative to the no history (p=0.001) and all previous concussions diagnosed groups (p<0.001). Conclusions Undiagnosed concussions may lead to subtle lingering symptom and verbal memory deficits that persist beyond the undiagnosed concussion and remain evident after subsequent concussion.

BACKGROUND:Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, meaning that early identification is often contingent on individuals reporting their injury to medical staff. A fundamental understanding of the types and levels of factors that explain when concussions are reported can help identify promising directions for intervention. PURPOSE:To identify individual and institutional factors that predict immediate (vs delayed) injury reporting. STUDY DESIGN:Case-control study; Level of evidence, 3. METHODS:This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported "immediately" or "at a delay"), including both individual athlete/cadet and institutional characteristics. RESULTS:In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers. CONCLUSION:Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated.

Objective Approximately 51–64% of athletes and military service academy cadets delay seeking care following concussion. Delayed care-seeking is associated with longer recovery. We aimed to determine whether longer duration of continued participation after concussion was associated with recovery duration. Design Longitudinal cohort. Setting Clinical. Participants 717 CARE Consortium concussion cases from collegiate athletes and military cadets. Outcome Measures Recovery Duration was defined: first, as the number of days between injury and asymptomatic evaluation date, and second, as the number of days from injury to unrestricted return to activity. We used two multivariable linear regression models with duration of continued participation after injury (minutes – clinician reported) predicting days to asymptomatic and return to activity, while controlling for site, sex, race, ethnicity, sport contact level, and concussion history. Main Results The 717 athletes and cadets that continued to participate did so for an average 33.9±35.7 minutes after incident injury. We observed a positive association between the duration of time until removal from activity and the number of days until becoming asymptomatic (P=0.04). For every additional 30-minute delay until removal from play, the number of days until becoming asymptomatic increased 8.1% (95%CI: 0.3–16.4%). There was no association with days until return to activity (p=0.11). Conclusions When applied to a 15-day recovery, a two-hour delay in removal may extend time to asymptomatic by 5 days, resulting in a 20-day recovery. Continued participation after injury was associated with longer symptom recovery, which should be a clinical consideration when managing concussion.

University undergraduate course grades have several purposes: they provide feedback to the student and motivation to perform well; serve as admission criteria for entering a major; and are used as selection criteria for future employers and graduate programs. Accurate assignment of grades is therefore important and critical to ensure fairness. However, grades may also impact the student’s assessment of the instructor, which leads to a conflict of interest when such assessments are a component of employment, salary, or tenure decisions. This paper performs a detailed descriptive analysis of undergraduate grades collected over an eight year period from a major metropolitan university. Interesting grading patterns are identified and discussed, and the analysis suggests that grading policies vary substantially at the department, course, and instructor level. A connection is observed between course/department enrollment and average grades assigned. A particular focus of this study involves describing the grading behavior of instructors, with the goal of identifying instructors that assign grades that are statistically far above or below the norm. The analysis performed in this study can be applied to grade data from other universities using our publicly available Python-based analytics tool. The results of these analyses can be used to better understand existing grading policies, identify potential sources of grading inequities, and, when appropriate, take corrective action.

BACKGROUND:Approximately half of concussions go undisclosed and therefore undiagnosed. Among diagnosed concussions, 51% to 64% receive delayed medical care. Understanding the influence of undiagnosed concussions and delayed medical care would inform medical and education practices.PURPOSE:To compare postconcussion longitudinal clinical outcomes among (1) individuals with no concussion history, all previous concussions diagnosed, and ≥1 previous concussion undiagnosed, as well as (2) those who have delayed versus immediate symptom onset, symptom reporting, and removal from activity after concussion.STUDY DESIGN:Cohort study; Level of evidence, 2.METHODS:Participants included 2758 military academy cadets and intercollegiate athletes diagnosed with concussion in the CARE Consortium. We determined (1) each participant's previous concussion diagnosis status self-reported at baseline (no history, all diagnosed, ≥1 undiagnosed) and (2) whether the participant had delayed or immediate symptom onset, symptom reporting, and removal from activity. We compared symptom severities, cognition, balance, and recovery duration at baseline, 24 to 48 hours, date of asymptomatic status, and date of unrestricted return to activity using tests of parallel profiles.RESULTS:The ≥1 undiagnosed concussion group had higher baseline symptom burdens (P < .001) than the other 2 groups and poorer baseline verbal memory performance (P = .001) than the all diagnosed group; however, they became asymptomatic and returned to activity sooner than those with no history. Cadets/athletes who delayed symptom reporting had higher symptom burdens 24 to 48 hours after injury (mean ± SE; delayed, 28.8 ± 0.8; immediate, 20.6 ± 0.7), took a median difference of 2 days longer to become asymptomatic, and took 3 days longer to return to activity than those who had immediate symptom reporting. For every 30 minutes of continued participation after injury, days to asymptomatic status increased 8.1% (95% CI, 0.3%-16.4%).CONCLUSION:Clinicians should expect that cadets/athletes who delay reporting concussion symptoms will have acutely higher symptom burdens and take 2 days longer to become asymptomatic. Educational messaging should emphasize the clinical benefits of seeking immediate care for concussion-like symptoms.

Background: Untreated concussions are an important health concern. The number of concussions sustained each year is difficult to pinpoint due to diverse reporting routes and many people not reporting. A growing body of literature investigates the motivations for concussion under-reporting, proposing ties with knowledge of concussion outcomes and concussion culture. The present work employs machine learning to identify trends in knowledge and willingness to self-report concussions. Methods: 2,204 cadets completed a survey addressing athletic and pilot status, concussion symptoms and outcome beliefs, ethical beliefs, demographics, and reporting willingness. Results: Clustering and non-negative matrix analysis identified connections to self-report willingness within: knowledge of symptoms, ethical beliefs, reporting requirements, and belief of long-term concussion outcomes. Support vector machine classification of cadet reporting likelihood reveals symptom and outcome knowledge may be inversely related to reporting among those rating ethics considerations as low, while heightened ethics may predict higher reporting likeliness overall. Conclusions: Machine-learning analysis bolsters prior theories on the importance of concussion culture in reporting and indicate more symptom knowledge may decrease willingness to report. Uniquely, our analysis indicated importance of ethical behavior may be associated with general concussion reporting willingness, inviting further consideration from healthcare practitioners seeking increased reporting.

College students have great flexibility in choosing when they take specific courses. These choices sometimes are constrained by prerequisite requirements, which determines the order in which pairs of courses may be taken. However, even in these cases the student can choose the number of semesters, or gap, between the pairs of courses. In this paper we study the impact that this gap has on student learning, as measured by course grades. Our methodology accounts for differences in instructor grading policies and in student ability as measured by overall grade point average. Our results can be used to inform course selection and advising strategies. Our study is applied to eight years of undergraduate course data that spans all departments in a large university. Due to space limitations, in this paper we focus our analysis on the semester gaps in Computer Science courses and in Spanish courses. Our results do not show a consistent negative impact on increasing semester gaps between all pairs of courses in a department; however, a negative impact is shown when the gap increases between courses that have a particularly strong relationship and overlapping content.

Contextual associations play an important role in human vision and understanding. For example, objects that are contextually congruent with the environment are recognized faster. Do these same contextual associations play a role in artificial vision? If so, we would expect contextual associations between objects (e.g., tent – sleeping bag) to be included in the object representations within a convolutional neural network (CNN) designed for object recognition, even when the network is not explicitly trained to recognize contextual associations. To test this, we examined the similarity in CNN representations between pairs of contextually related object pairs (N=73). Stimuli were photographs of objects presented against a white background to ensure contextual associations are not merely a product of background information. Across each layer of the CNN, we compared the similarity in object representations across pairs of contextually related objects and unrelated objects. We found that across almost all layers of the CNN (except the first) contextually related objects had more similar representations across the units of the CNN than unrelated objects. This was true across 10 different CNNs tested that varied in number of layers, training data, and recurrent/non-recurrent architecture. Critically, we compared these context representations to human behavior to determine whether the contextual associations represented in a CNN were relevant to human vision. We found the similarity of object representations due to contextual associations correlated with human judgments on the relatedness of contextually related object pairs. The more similar the object representation in the CNN, the faster and more likely humans labeled the objects as contextually related. Most interestingly, despite context being represented across almost all layers of the CNN, correlation with behavior only emerged at the later layers. This segmentation in model--behavioral correlation suggested that only high level or complex regularities relating to context are relevant to human behavior.

Educational institutions rely on instructor assessment to determine course assignments, which instructors to retain or promote, and whom to provide with additional assistance or training. Instructor assessment is most commonly based on student surveys or peer evaluation—which are both subject to the evaluator’s personal biases. This study describes an assessment method based on future student grade performance, which has the potential to avoid these biases. This study is based on eight years of undergraduate course-grade data from over 24,000 students in a large metropolitan university. The methodology introduced in this paper accounts for confounding factors, such as diverse instructor grading policies and varying student abilities. Top and bottom performing instructors are identified for each course.

Background: The prevalence of unreported concussions is high, and undiagnosed concussions can lead to worse postconcussion outcomes. It is not clear how those with a history of undiagnosed concussion perform on subsequent standard concussion baseline assessments. Purpose: To determine if previous concussion diagnosis status was associated with outcomes on the standard baseline concussion assessment battery. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Concussion Assessment, Research, and Education (CARE) Consortium participants (N = 29,934) self-reported concussion history with diagnosis status and completed standard baseline concussion assessments, including assessments for symptoms, mental status, balance, and neurocognition. Multiple linear regression models were used to estimate mean differences and 95% CIs among concussion history groups (no concussion history [n = 23,037; 77.0%], all previous concussions diagnosed [n = 5315; 17.8%], ≥1 previous concussions undiagnosed [n = 1582; 5.3%]) at baseline for all outcomes except symptom severity and Brief Symptom Inventory–18 (BSI-18) score, in which negative binomial models were used to calculate incidence rate ratios (IRRs). All models were adjusted for sex, race, ethnicity, sport contact level, and concussion count. Mean differences with 95% CIs excluding 0.00 and at least a small effect size (≥0.20), and those IRRs with 95% CIs excluding 1.00 and at least a small association (IRR, ≥1.10) were considered significant. Results: The ≥1 previous concussions undiagnosed group reported significantly greater symptom severity scores (IRR, ≥1.38) and BSI-18 (IRR, ≥1.31) scores relative to the no concussion history and all previous concussions diagnosed groups. The ≥1 previous concussions undiagnosed group performed significantly worse on 6 neurocognitive assessments while performing better on only 2 compared with the no concussion history and all previous concussions diagnosed groups. There were no between-group differences on mental status or balance assessments. Conclusion: An undiagnosed concussion history was associated with worse clinical indicators at future baseline assessments. Individuals reporting ≥1 previous undiagnosed concussions exhibited worse baseline clinical indicators. This may suggest that concussion-related harm may be exacerbated when injuries are not diagnosed.

University students have a great deal of freedom in deciding the order in which to take their courses. In this paper we apply the Apri-ori-based Generalized Sequential Pattern (GSP) algorithm to undergraduate course data from a large university in order to identify frequent course sequences. Course sequencing results are primarily generated at the department level, with a special focus on Computer Science courses. This paper also introduces the course sequence flow diagram, which compactly represents a large amount of course sequencing information in an intuitive visual form. Our results and associated flow diagrams can help to answer a variety of important questions, such as: what course sequences are most common, how are courses between different departments ordered, and when are courses taken in an order that may contradict the advice given by academic advisors? In this paper we show that this form of descriptive data mining can identify standard core curriculum and pre-health sequences of study, as well as computer science courses that are either artificially pushed to the end of a student’s program of study or taken earlier than would be recommended.

ABSTRACT Purpose There is limited understanding of factors affecting concussion diagnosis status using large sample sizes. The study objective was to identify factors that can accurately classify previous concussion diagnosis status among collegiate student-athletes and service academy cadets with concussion history. Methods This retrospective study used support vector machine, Gaussian Naïve Bayes, and decision tree machine learning techniques to identify individual (e.g., sex) and institutional (e.g., academic caliber) factors that accurately classify previous concussion diagnosis status (all diagnosed vs 1+ undiagnosed) among Concussion Assessment, Research, and Education Consortium participants with concussion histories (n = 7714). Results Across all classifiers, the factors examined enable >50% classification between previous diagnosed and undiagnosed concussion histories. However, across 20-fold cross validation, ROC-AUC accuracy averaged between 56% and 65% using all factors. Similar performance is achieved considering individual risk factors alone. By contrast, classifications with institutional risk factors typically did not distinguish between those with all concussions diagnosed versus 1+ undiagnosed; average performances using only institutional risk factors were almost always <58%, including confidence intervals for many groups <50%. Participants with more extensive concussion histories were more commonly classified as having one or more of those previous concussions undiagnosed. Conclusions Although the current study provides preliminary evidence about factors to help classify concussion diagnosis status, more work is needed given the tested models’ accuracy. Future work should include a broader set of theoretically indicated factors, at levels ranging from individual behavioral determinants to features of the setting in which the individual was injured.

Contextual associations facilitate object recognition in human vision. However, the role of context in artificial vision remains elusive as does the characteristics that humans use to define context. We investigated whether contextually related objects (bicycle-helmet) are represented more similarly in convolutional neural networks (CNNs) used for image understanding than unrelated objects (bicycle-fork). Stimuli were of objects against a white background and consisted of a diverse set of contexts (N = 73). CNN representations of contextually related objects were more similar to one another than to unrelated objects across all CNN layers. Critically, the similarity found in CNNs correlated with human behavior across multiple experiments assessing contextual relatedness, emerging significant only in the later layers. The results demonstrate that context is inherently represented in CNNs as a result of object recognition training, and that the representation in the later layers of the network tap into the contextual regularities that predict human behavior.

Author(s): Reno, Laura G; Habeck, Christian G; Stern, Yaakov; Leeds, Daniel D | Abstract: Localizing function in the brain has been an elusive long-term goal in the study of cognition. Prior studies have utilized four reference abilities (RAs) to capture cognition (Salthouse, 2009). Full-brain cortical networks have been tied to these abilities using common multi-voxel patterns across subjects in distinct age groups. Using voxel searchlights the current study explores purely local cortical representations of cognition, less commonly explored. This work analyzes 240 subjects’ responses to cognitive tasks from the four RAs. The current study further employs representational similarity analysis (RSA, Kriegeskorte, Mur, a Bandettini, 2008) to the similarity of brain activities from tasks within the same RA; RSA can capture representational consistencies within each subject even when exact voxel pattern may vary across subjects. We found distinct topographical localizations for each RA that were mostly consistent across age and suggested refinements of broader functional divisions of the brain from prior literature.

Undergraduate college students have substantial flexibility in choosing the order in which they take courses, since most courses either have no prerequisites or only a single prerequisite. However, the specific order that courses are taken can have an impact on student performance. This paper describes a general methodology for assessing the impact of course sequencing on student performance, as measured by course grades, and applies this methodology to eight years of undergraduate academic data from Fordham University. The results demonstrate that certain course orderings are associated with improved student grade performance. This study introduces a methodology, new metrics, and a publicly available data-processing tool that can be applied to any student course-grade data set to measure course sequencing effects. The results can be used to inform student decisions, modify course recommendations, and even modify course prerequisites.

Contextual associations play a significant role in facilitating object recognition in human vision. However, the role of contextual information in artificial vision remains elusive. We aim to examine whether contextual associations are represented in an artificial neural network, and if so, to understand at what layer they potentially have a role. Addressing this, we examined whether objects that share contextual associations (e.g., bicycle-helmet) are represented more similarly in convolutional neural networks than objects that do not share the same context (e.g., bicycle-fork), and further examined where in the network these context-based representational similarities emerge. As a comparison, we also examined the representational similarity of objects that belong to the same category (e.g., two different shoes) in contrast to objects that do not share the same category (e.g., shoe-brush). In a VGG16 neural network trained on ImageNet and focused on object categorization, representational similarity among objects that share a context (N = 70) is substantially higher than similarity among objects that do not share a context. Representational similarities were computed as the correlation between unit responses to pairs of images in and out of context (or category as a comparison). This context-based rise in similarity emerged at very early layers of the network, remarkably, at the same layer that category-based similarity was found. Category-based similarity was significantly larger than context-based similarity throughout the network. Pixel similarities across contextually paired objects were no greater than objects that do not share the same context. Thus, even though the network was designed for categorical object recognition, contextual relationships were evident in the network across early, mid, and late layers. This suggests that context is inherently preserved and represented across the network, and may have a critical role in facilitating object recognition both in humans and in artificial models.