Stochastic control problems for systems driven by normal martingales

In this paper we study a class of stochastic control problems in which the control of the jump size is essential. Such a model is a generalized version for various applied problems ranging from optimal reinsurance selections for general insurance models to queueing theory. The main novel point of such a control problem is that by changing the jump size of the system, one essentially changes the type of the driving martingale. Such a feature does not seem to have been investigated in any existing stochastic control literature. We shall first provide a rigorous theoretical foundation for the control problem by establishing an existence result for the multidimensional structure equation on a Wiener--Poisson space, given an arbitrary bounded jump size control process; and by providing an auxiliary counterexample showing the nonuniqueness for such solutions. Based on these theoretical results, we then formulate the control problem and prove the Bellman principle, and derive the corresponding Hamilton--Jacobi--Bellman (HJB) equation, which in this case is a mixed second-order partial differential/difference equation. Finally, we prove a uniqueness result for the viscosity solution of such an HJB equation.Comment: Published in at http://dx.doi.org/10.1214/07-AAP467 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Balancing Act: Unlearning and Embracing Chinese Immigrant Mothering

As a Chinese immigrant, motherhood involves unlearning patterns of thinking and behaving from one’s upbringing and learning healthier ways to mother. Many view Chinese mothers as tiger moms, a harmful stereotype that does little to embrace the diversity of Chinese motherhood. This article draws on my lived experiences as an immigrant mother to my three American-born Chinese children. For me, the act of immigrant mothering entails a delicate balancing act where Chinese and American values often conflict. These conflicts highlight the racial inequities in how mothers are allowed to mother and experience motherhood. There is no one way to mother, but the heteronormative white, middle-class mothering style is dominant in how society defines good mothers. Little has been written about racial equity among mothers and how motherhood often details negotiating between culture-specific and American norms. My article seeks to explore racial equity and widen the boundaries of motherhood by exploring the impact of immigrant mothering practices, navigating Chinese and American cultures as an immigrant mother, reflecting on how my immigrant mothering has affected my American-born children, and lastly, understanding my cultural history and its influence on my Chinese identity. To widen the rhetoric on mothering, we must engage the narratives of racially diverse mothers to understand motherhood’s multiplicities and complexities. Only then will we have a more inclusive view of motherhood that will build racial equity to benefit women and children

Reflections of a Chinese Academic Mom Struggling to Survive a Pandemic

Each of us has been negatively affected by this pandemic, but mothers have had to shoulder the brunt of it, as they have been forced to work from home, provide child/elder care, and ensure that their family survives. Sadly, academic mothers have been burdened not only with an increased workload at home but also with trying to juggle their academic careers, which have been gravely affected by this pandemic. As a Chinese academic mother, I have struggled with managing my publishing requirements, my service to my department, and my online teaching responsibilities. I have had to care for elderly family members, who are more susceptible to COVID-19, and for my children, who have been exposed to COVID-19. I have also had to work through filial piety failures as a daughter and daughter-in-law. Sharing the struggles of academic mothers aims to expose how the exorbitant workload that falls upon academic mothers is not sustainable and to address systemic problems that have been long plagued both the academic and home environment. All mothers cannot continue to support a system or a government that lacks leadership during global crises that do not value the visible and invisible labor of mothers because women have far too long been viewed as disposable. In sharing my experiences during this pandemic as a Chinese mother and academic navigating through this uncharted territory of pandemic survival, I hope my journey can provide support to other academic mothers as we advocate for structural change in how mothers should be supported as essential workers

Campus Racial Climate and Mental Well-being Among College Students: The Role of Feeling Valued, Sense of Belonging, and Racial Saliency

Around 73% of students report experiencing a mental health crisis during college, and 64% report dropping out for mental health reasons. Research indicates that negative campus racial climate contributes to poor mental health, but few studies have examined factors that may moderate this impact. The current study examined potential moderators of the impact of negative campus racial climate on students’ mental health, including whether: (1) feeling valued and (2) belongingness at school may buffer, or reduce, risk and (3) higher racial saliency may increase risk. Data were drawn from the Healthy Minds Study, administered to a random sample of 4,000 students at Old Dominion University in March 2021. Of the 413 students who completed the survey, 167 (40.4%) rated the climate at school for persons from various racial/ethnic backgrounds to be “disrespectful” or “very disrespectful”. Feeling valued and belonging at school buffered this risk. Specifically, compared to students who did not feel valued at school, those who felt valued had lower anxiety.https://digitalcommons.odu.edu/gradposters2022_sciences/1005/thumbnail.jp

Multicenter assessment of quantitative sensory testing (QST) for the detection of neuropathic-like pain responses using the topical capsaicin model

Background: The use of quantitative sensory testing (QST) in multicenter studies has been quite limited, due in part to lack of standardized procedures among centers. Aim: The aim of this study was to assess the application of the capsaicin pain model as a surrogate experimental human model of neuropathic pain in different centers and verify the variation in reports of QST measures across centers. Methods: A multicenter study conducted by the Quebec Pain Research Network in six laboratories allowed the evaluation of nine QST parameters in 60 healthy subjects treated with topical capsaicin to model unilateral pain and allodynia. The same measurements (without capsaicin) were taken in 20 patients with chronic neuropathic pain recruited from an independent pain clinic. Results: Results revealed that six parameters detected a significant difference between the capsaicin-treated and the control skin areas: (1) cold detection threshold (CDT) and (2) cold pain threshold (CPT) are lower on the capsaicin-treated side, indicating a decreased in cold sensitivity; (3) heat pain threshold (HPT) was lower on the capsaicin-treated side in healthy subjects, suggesting an increased heat pain sensitivity; (4) dynamic mechanical allodynia (DMA); (5) mechanical pain after two stimulations (MPS2); and (6) mechanical pain summation after ten stimulations (MPS10), are increased on the capsaicin-treated side, suggesting an increased in mechanical pain (P < 0.002). CDT, CPT and HPT showed comparable effects across all six centers, with CPT and HPT demonstrating the best sensitivity. Data from the patients showed significant difference between affected and unaffected body side but only with CDT. Conclusion: These results provide further support for the application of QST in multicenter studies examining normal and pathological pain responses

Resource Needs and Disparities Among University Members During COVID-19

Prior studies suggest that campus closures due to COVID-19 adversely impacted the well-being of college and university members. However, no research has examined the resources needed to assist university members as they return to pre-pandemic activities. The current study examined: (1) the resources university members wanted to assist their transition back to in-person activities, (2) their access to these resources, and (3) differences in access among various demographic groups, including those from minoritized backgrounds. Participants completed a novel Wants and Access Questionnaire to gauge their desires for and access to various campus and community resources. The study included 471 university members: 219 undergraduates (Age: Mage=22.78, SD=6.35), 91 graduate students (Age: Mage=33.77, SD =9.75), and 161 faculty/staff members (Age: Mage=49.53, SD =12.19). The study found that most undergraduates reported wanting access to financial support, followed by interpersonal support (friends and partners), and mental health support. However, 30-60% of students reported a lack of access to these desired resources. Graduate students reported wanting access to interpersonal support (friends, partners, family), followed by financial support, and mental health. However, 24-50% of the graduate students reported limited access to these resources. Most faculty/staff members reported wanting access to interpersonal support (friends, partners, family), and medical professionals. Only about 20-30% of the faculty/staff reported limited access to these resources. Faculty/staff reported the need for mental health resources in their write-in responses of the study. Additionally, in several instances, minoritized groups (LGBQ+ and people of color) reported lower access to resources. Findings indicate that university members (especially undergraduates, LGBQ+ and people of color) reported lack of access to desired resources to support them. The current study points to disparities in resource categories that may guide college/university priorities.https://digitalcommons.odu.edu/gradposters2023_sciences/1012/thumbnail.jp

Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage

Machine learning models are increasingly utilized across impactful domains to predict individual outcomes. As such, many models provide algorithmic recourse to individuals who receive negative outcomes. However, recourse can be leveraged by adversaries to disclose private information. This work presents the first attempt at mitigating such attacks. We present two novel methods to generate differentially private recourse: Differentially Private Model (DPM) and Laplace Recourse (LR). Using logistic regression classifiers and real world and synthetic datasets, we find that DPM and LR perform well in reducing what an adversary can infer, especially at low FPR. When training dataset size is large enough, we find particular success in preventing privacy leakage while maintaining model and recourse accuracy with our novel LR method.Comment: Proceedings of The Second Workshop on New Frontiers in Adversarial Machine Learning (AdvML-Frontiers @ ICML 2023

Legal Services Assessment for Trafficked Children- Cook County, Illinois Case Study

Child trafficking is one of the most disturbing human rights abuses of our time, involving cases of boys and girls exploited for labor and/or commercial sexual services. These children may suffer physical, sexual, and emotional violence at the hands of traffickers, who can be pimps, employers, and even family members. Trafficking schemes may involve various forms of force, fraud, and coercion, which can be physical and/or psychological in nature. Current research indicates that legal services are a critical component of a comprehensive service delivery model for victims of human trafficking and a realization of human rights. However, little to no effort has been made to identify the various legal needs of child trafficking victims, a particularly vulnerable population. In February 2012, the Center for the Human Rights of Children (CHRC) initiated a legal needs assessment project for child trafficking victims, using Cook County Illinois as a case study. The project identified: •Existing service providers working with both US citizen and foreign national child trafficking survivors •The legal needs of trafficked children •Current legal services available to this population •Gaps in those services in Cook County We chose Cook County as a case study for several reasons. It is the second most populous county in the nation, and houses the city of Chicago, which has been recognized as one several human trafficking hubs across the United States. Cook County has an established community of service providers and advocacy organizations working with survivors of human trafficking in various capacities, and two task forces. The project also included a preliminary assessment of legal services for child trafficking victims offered by organizations around the country as a comparison to the results of our research in Cook County

Identification of Regulatory Requirements Relevant to Business Processes: A Comparative Study on Generative AI, Embedding-based Ranking, Crowd and Expert-driven Methods

Organizations face the challenge of ensuring compliance with an increasing amount of requirements from various regulatory documents. Which requirements are relevant depends on aspects such as the geographic location of the organization, its domain, size, and business processes. Considering these contextual factors, as a first step, relevant documents (e.g., laws, regulations, directives, policies) are identified, followed by a more detailed analysis of which parts of the identified documents are relevant for which step of a given business process. Nowadays the identification of regulatory requirements relevant to business processes is mostly done manually by domain and legal experts, posing a tremendous effort on them, especially for a large number of regulatory documents which might frequently change. Hence, this work examines how legal and domain experts can be assisted in the assessment of relevant requirements. For this, we compare an embedding-based NLP ranking method, a generative AI method using GPT-4, and a crowdsourced method with the purely manual method of creating relevancy labels by experts. The proposed methods are evaluated based on two case studies: an Australian insurance case created with domain experts and a global banking use case, adapted from SAP Signavio's workflow example of an international guideline. A gold standard is created for both BPMN2.0 processes and matched to real-world textual requirements from multiple regulatory documents. The evaluation and discussion provide insights into strengths and weaknesses of each method regarding applicability, automation, transparency, and reproducibility and provide guidelines on which method combinations will maximize benefits for given characteristics such as process usage, impact, and dynamics of an application scenario

Using surface waves recorded by a large mesh of three-element arrays to detect and locate disparate seismic sources

Author Posting. © The Authors, 2018. This article is posted here by permission of The Royal Astronomical Society for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 215 (2018): 942–958, doi:10.1093/gji/ggy316.Surface waves recorded by global arrays have proven useful for locating tectonic earthquakes and in detecting slip events depleted in high frequency, such as glacial quakes. We develop a novel method using an aggregation of small- to continental-scale arrays to detect and locate seismic sources with Rayleigh waves at 20–50 s period. The proposed method is a hybrid approach including first dividing a large aperture aggregate array into Delaunay triangular subarrays for beamforming, and then using the resolved surface wave propagation directions and arrival times from the subarrays as data to formulate an inverse problem to locate the seismic sources and their origin times. The approach harnesses surface wave coherence and maximizes resolution of detections by combining measurements from stations spanning the whole U.S. continent. We tested the method with earthquakes, glacial quakes and landslides. The results show that the method can effectively resolve earthquakes as small as ∼M3 and exotic slip events in Greenland. We find that the resolution of the locations is non-uniform with respect to azimuth, and decays with increasing distance between the source and the array when no calibration events are available. The approach has a few advantages: the method is insensitive to seismic event type, it does not require a velocity model to locate seismic sources, and it is computationally efficient. The method can be adapted to real-time applications and can help in identifying new classes of seismic sources.WF is currently supported by the Postdoctoral Scholar Program at the Woods Hole Oceanographic Institution, with funding provided by the Weston Howland Jr. Postdoctoral Scholarship. This work was supported by National Science Foundation grant EAR-1358520 at Scripps Institution of Oceanography, UC San Diego