The University of South Dakota (USD) is a public research university in Vermillion, South Dakota. Established by the Dakota Territory legislature in 1862, 27 years before the establishment of the state of South Dakota, USD markets itself as the flagship university for the state of South Dakota and the state's oldest public university (but see “History” below—classes did not start until 20 years later). It occupies a 274 acres (1.11 km2) campus located in southeastern South Dakota, approximately 63 miles (101 km) southwest of Sioux Falls, 39 miles (63 km) northwest of Sioux City, Iowa, and north of the Missouri River.The university is home to South Dakota's only medical school and law school. It is also home to the National Music Museum, with over 15,000 American, European, and non-Western instruments. USD is governed by the South Dakota Board of Regents, and its president is Sheila Gestring. The university has been accredited by the North Central Association of College and Schools since 1913. It is classified among "R2: Doctoral Universities – High research activity".University of South Dakota's alumni include a total of 17 Truman Scholars, 12 Rhodes Scholars, and 1 Nobel Laureate, (Ernest Lawrence '22, 1939 Nobel Prize in Physics.) The athletic teams compete in the NCAA's Division I as members of The Summit League, except football, which competes in the Missouri Valley Football Conference.
Posttraumatic stress disorder (PTSD) and major depressive disorder are commonly comorbid among individuals with a history of trauma exposure, and this comorbidity can have implications for functioning and treatment. There is a substantial amount of prior research examining the co-occurrence of these two diagnoses, though additional investigation into the underlying mechanisms associated with high rates of comorbidity is warranted. The present study examined PTSD symptoms, depression symptoms, and emotion dysregulation in two college student samples using a network analysis approach, which allows for examining the dynamic interplay between symptoms. Study 1 included 410 students with a history of trauma from a large Hispanic-serving institute, and Study 2 included 244 student participants with a history of trauma exposure from a Midwestern public university. Both samples indicated high central strength for the Difficulties in Emotion Regulation Scale's Limited Access to Emotion Regulation Strategies subscale, and this strategies subscale also demonstrated high expected influence in both samples. Results revealed several nonzero regularized partial correlations among PTSD symptom clusters, depression, and emotion dysregulation subscales. These findings supported PTSD and depressive symptoms as distinguishable, though connected, constructs. PTSD's negative alterations in cognition and mood cluster, depression, and the Difficulties in Emotion Regulation Scale Limited Access to Emotion Regulation Strategies subscale seemed important for understanding the comorbidity of these two diagnoses, and therefore the belief that distress is persistent and unchangeable, despite one's actions, may contribute to the development or maintenance of both PTSD and major depressive disorder.
Over the last few years, AI integration has rapidly increased in counseling and counselor education. Given its prevalence, counselor educators have a responsibility to consider AI in their teaching to prepare students to intentionally and critically use AI in their careers. Yet, limited research exists to understand counselor educators' decisions and meaning making regarding AI in teaching. In this Interpretative Phenomenological Analysis study, we conducted individual interviews and a focus group interview and collected documents (e.g., syllabus statements, assignments) to examine counselor educators' experiences using AI in their teaching and promote intentional and reflective use of AI. The resulting themes of our data analysis were (a) concerns and risks, (b) navigating complex emotions, (c) responsibility to students and the profession, (d) influence of systems, and (e) application of AI in teaching. Participants shared engaging in a trial-and-error process of experimenting with AI in their teaching considering the lack of policies and resources from institutions and counseling organizations which could cause feelings of shame and fear resulting from the lack of open dialogue about AI in counselor education. Implications for counselor educators and institutions include creating continuously updated AI policies and resources and offering spaces where counselor educators can discuss challenges, successes, and feelings and share resources regarding AI in teaching.
The intent of new professional sport franchises is to increase public awareness, build a brand and provide opportunity for positive media reports. Houston’s new Major League Soccer (MLS) franchise the Houston Dynamo, formally known as Houston 1836, failed to do this. As such, this essay covers a number of social issues such as the attitudes of Anglo‐Americans and Hispanics to professional soccer within the United States and how the MLS promotes its product to Hispanic audiences, while utilizing the efforts of the MLS to relocate its San Jose franchise to Houston for the 2006 season as an example. Additionally, the essay further analyses the extent to which the Houston franchise successfully used crisis communications methods to deal with this public relations challenge. Finally this case study attempts to reinforce the idea that MLS franchises must develop strategies to get Hispanics excited about the league in order to succeed.
Interpreting Convolutional Neural Networks (CNNs) is critical for safety-sensitive applications such as healthcare and autonomous systems. Popular visual explanation methods like Grad-CAM use a single convolutional layer, potentially missing multi-scale cues and producing unstable saliency maps. We introduce Winsor-CAM, a single-pass gradient-based method that aggregates Grad-CAM maps from all convolutional layers and applies percentile-based Winsorization to attenuate outlier contributions. A user-controllable percentile parameter $p$p enables semantic-level tuning from low-level textures to high-level object patterns. We evaluate Winsor-CAM on six CNN architectures using PASCAL VOC 2012 and PolypGen, comparing localization (IoU, center-of-mass distance) and fidelity (insertion/deletion AUC) against seven baselines including Grad-CAM, Grad-CAM++, LayerCAM, ScoreCAM, AblationCAM, ShapleyCAM, and FullGrad. On DenseNet121 with a subset of Pascal VOC 2012, Winsor-CAM achieves 46.8% IoU and 0.059 CoM distance versus 39.0% and 0.074 for Grad-CAM, with improved insertion AUC (0.656vs. 0.623) and deletion AUC (0.197vs. 0.242). Notably, even the worst-performing fixed $p$p-value configuration outperforms FullGrad across all metrics. An ablation study confirms that incorporating earlier layers improves localization. Similar evaluation on PolypGen polyp segmentation further validates Winsor-CAM's effectiveness in medical imaging contexts. Winsor-CAM provides an efficient, robust, and human-tunable explanation tool for expert-in-the-loop analysis.
We derive a factorization formula for inclusive jet production in heavy-ion collisions using the tools of Effective Field Theory (EFT). We show how physics at widely separated scales in this process can be systematically separated by matching to EFTs at successively lower virtualities. Owing to a strong scale separation, we recover a vacuum-like DGLAP evolution above the jet scale, while the additional low-energy scales induced by the medium effectively probe the internal structure of the jet. As a result, the cross section can be written as a series with an increasing number of subjets characterized by perturbative matching coefficients each of which is convolved with a distinct function. These functions encode broadening, medium-induced radiations as well as quantum interference such as the Landau-Pomeranchuk-Migdal effect and color coherence dynamics to all orders in perturbation theory. As a first application of this EFT framework, we investigate the case of an unresolved jet and show how the cross section can be factorized and fully separate the jet dynamics from the universal physics of the medium. To compare to the existing literature, we explicitly compute the medium jet function at next-to-leading order in the coupling and leading order in medium opacity.
