Search

We study the micorscopic origin of the multiple superconducting and magnetic phases observed in CeRh$_2$As$_2$. We exploit the existence of a van Hove singularity enforced by the nonsymmorphic symmetry to conduct a renormalization group analysis. When Fermi-surface nesting is strong, we find two closely-competing superconducting states with opposite parities, as well as an instability towards specific spin-density wave states, consistent with key features of the phase diagram of CeRh$_2$As$_2$., Comment: 6 pages, 4 figures. Supplemental material of 20 pages, 9 figures

$\textbf{Purpose}$: Automatic quantification of longitudinal changes in PET scans for lymphoma patients has proven challenging, as residual disease in interim-therapy scans is often subtle and difficult to detect. Our goal was to develop a longitudinally-aware segmentation network (LAS-Net) that can quantify serial PET/CT images for pediatric Hodgkin lymphoma patients. $\textbf{Materials and Methods}$: This retrospective study included baseline (PET1) and interim (PET2) PET/CT images from 297 patients enrolled in two Children's Oncology Group clinical trials (AHOD1331 and AHOD0831). LAS-Net incorporates longitudinal cross-attention, allowing relevant features from PET1 to inform the analysis of PET2. Model performance was evaluated using Dice coefficients for PET1 and detection F1 scores for PET2. Additionally, we extracted and compared quantitative PET metrics, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in PET1, as well as qPET and $\Delta$SUVmax in PET2, against physician measurements. We quantified their agreement using Spearman's $\rho$ correlations and employed bootstrap resampling for statistical analysis. $\textbf{Results}$: LAS-Net detected residual lymphoma in PET2 with an F1 score of 0.606 (precision/recall: 0.615/0.600), outperforming all comparator methods (P<0.01). For baseline segmentation, LAS-Net achieved a mean Dice score of 0.772. In PET quantification, LAS-Net's measurements of qPET, $\Delta$SUVmax, MTV and TLG were strongly correlated with physician measurements, with Spearman's $\rho$ of 0.78, 0.80, 0.93 and 0.96, respectively. The performance remained high, with a slight decrease, in an external testing cohort. $\textbf{Conclusion}$: LAS-Net achieved high performance in quantifying PET metrics across serial scans, highlighting the value of longitudinal awareness in evaluating multi-time-point imaging datasets., Comment: 6 figures, 4 tables in the main text

With the growing use of transformer-based language models in medicine, it is unclear how well these models generalize to nuclear medicine which has domain-specific vocabulary and unique reporting styles. In this study, we evaluated the value of domain adaptation in nuclear medicine by adapting language models for the purpose of 5-point Deauville score prediction based on clinical 18F-fluorodeoxyglucose (FDG) PET/CT reports. We retrospectively retrieved 4542 text reports and 1664 images for FDG PET/CT lymphoma exams from 2008-2018 in our clinical imaging database. Deauville scores were removed from the reports and then the remaining text in the reports was used as the model input. Multiple general-purpose transformer language models were used to classify the reports into Deauville scores 1-5. We then adapted the models to the nuclear medicine domain using masked language modeling and assessed its impact on classification performance. The language models were compared against vision models, a multimodal vision language model, and a nuclear medicine physician with seven-fold Monte Carlo cross validation, reported are the mean and standard deviations. Domain adaption improved all language models. For example, BERT improved from 61.3% five-class accuracy to 65.7% following domain adaptation. The best performing model (domain-adapted RoBERTa) achieved a five-class accuracy of 77.4%, which was better than the physician's performance (66%), the best vision model's performance (48.1), and was similar to the multimodal model's performance (77.2). Domain adaptation improved the performance of large language models in interpreting nuclear medicine text reports.

Clustering time-series data in healthcare is crucial for clinical phenotyping to understand patients' disease progression patterns and to design treatment guidelines tailored to homogeneous patient subgroups. While rich temporal dynamics enable the discovery of potential clusters beyond static correlations, two major challenges remain outstanding: i) discovery of predictive patterns from many potential temporal correlations in the multi-variate time-series data and ii) association of individual temporal patterns to the target label distribution that best characterizes the underlying clinical progression. To address such challenges, we develop a novel temporal clustering method, T-Phenotype, to discover phenotypes of predictive temporal patterns from labeled time-series data. We introduce an efficient representation learning approach in frequency domain that can encode variable-length, irregularly-sampled time-series into a unified representation space, which is then applied to identify various temporal patterns that potentially contribute to the target label using a new notion of path-based similarity. Throughout the experiments on synthetic and real-world datasets, we show that T-Phenotype achieves the best phenotype discovery performance over all the evaluated baselines. We further demonstrate the utility of T-Phenotype by uncovering clinically meaningful patient subgroups characterized by unique temporal patterns.

On the recent report of a magnetic field induced first order transition between an even-parity superconductivity and an odd-parity superconductivity in $\mathrm{CeRh_{2}As_{2}}$, the microscopic physics is still under investigation. However, if, in the vicinity of this transition, the coupling strengths of the even and odd pairing channels are comparable, a particle-particle excitonic collective mode referred to as the Bardasis-Schrieffer (BS) mode should generically exist below the pair-breaking continuum. This BS mode can couple to the light and thus affect the optical response of the superconductor, as it arises from a pairing channel with the parity opposite to that of the ground state pairs. Here, by using a generic bilayer model Hamiltonian for the electronic degree of freedom, which is globally centrosymmetric despite each layer being locally non-centrosymmetric, we study the change of the excitation gap of the BS mode with respect to the out-of-plane magnetic fields and demonstrate that its coupling to the light is possible even in the linear response regime. The linear coupling is attributed to the presence of multiple electronic bands, which is a generic feature of a bilayer system. Our result shows the microwave absorption as the signature of the BS mode, and hence a smoking gun signature of the parity-switching at the transition between two superconducting phases., Comment: 17 pages, 7 figures, 1 table

Internships and other forms of work-based learning are increasingly viewed as essential experiences for college students. Proponents point out that internships help students develop transferable skills, apply academic knowledge to authentic situations, develop professional networks, and facilitate students' socialization and entry into the professions. In recent years, online or virtual internships, which can vary according to duration, structure, and activities, have grown in prominence. The COVID-19 pandemic brought considerable interest in these unique types of internships, as many in-person positions were cancelled or shifted online. Yet little empirical research exists on the prevalence, quality, and commitment to equity and access among online internships in the United States, particularly during the pandemic period of 2020 to early 2021. In this multi-site case study, we collected survey and interview data from college students during the pandemic. Our findings focus on three distinct cases: (1) two independent websites that provide online internship networking platforms (OINP) for students seeking online internships and employers seeking student interns (n=183 surveys, n=45 interviews), (2) 11 colleges and universities (n=9,964 surveys), and (3) a single employer-hosted online internship program at TreeHouse Foods Inc, a multi-national firm engaged in manufacturing and distributing private label food and beverage products. In analyzing and interpreting our data, we used the Internship Scorecard framework (Hora et al., 2020) from the Center for Research on College-Workforce Transitions. The framework provides a structured approach to studying internships, as well as insights from research on remote work and digital learning. One of our primary conclusions is that while considerable variation exists within the world of internships writ large, an added layer of complexity exists for online positions with respect to information technology, internet access, work-life boundaries, and challenges associated with online or remote work that many occupations experienced during the COVID-19 pandemic. We argue that these additional concerns and factors make online internships--which are unlikely to disappear post-pandemic--a top priority for improvement, equitable access, and quality control in the field of higher education.

Scholars recognize that K-12 teachers' social interactions, particularly within teaching focused relationships, are important to professional development. This is true whether discussions take place in formal or informal settings. Few studies, however, seek to link the teaching-focused relationships of "college faculty" directly to their instructional practice, nor to explore perceptions of what these relationships entail and how they influence teaching at the college level. Using surveys (n=868), interviews (n=83), and a social capital theoretical framework, this mixed-methods social network analysis explores associations between teaching focused "personal networks"--compilations of relationships surrounding individual faculty-- and the use of evidence-based instructional methods. We also explore faculty perceptions of how network interactions shape their teaching. Quantitative results indicate that the size, range, and strength of faculty teaching-focused personal networks positively correlate with the use of evidence-based instructional methods, while qualitative results point to the ways faculty see different kinds of network ties, relational mechanisms and objects, and returns influencing teaching practice.

We study the problem of inferring heterogeneous treatment effects from time-to-event data. While both the related problems of (i) estimating treatment effects for binary or continuous outcomes and (ii) predicting survival outcomes have been well studied in the recent machine learning literature, their combination -- albeit of high practical relevance -- has received considerably less attention. With the ultimate goal of reliably estimating the effects of treatments on instantaneous risk and survival probabilities, we focus on the problem of learning (discrete-time) treatment-specific conditional hazard functions. We find that unique challenges arise in this context due to a variety of covariate shift issues that go beyond a mere combination of well-studied confounding and censoring biases. We theoretically analyse their effects by adapting recent generalization bounds from domain adaptation and treatment effect estimation to our setting and discuss implications for model design. We use the resulting insights to propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations. We investigate performance across a range of experimental settings and empirically confirm that our method outperforms baselines by addressing covariate shifts from various sources., Comment: Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

Competencies known variously as "soft" or "21st century skills" are increasingly linked to college students' academic and career success, and faculty with industry experience are hypothesized to be uniquely qualified to teach these skills. Yet little research exists on this topic. In this paper, we report findings from a mixed-methods study of the degree to which industry experience influences how faculty in science, technology, engineering, mathematics, and medicine teach teamwork, oral and written communication, problem-solving, and self-directed learning skills in 2- and 4-year postsecondary institutions. Using inductive thematic and hierarchical linear modeling techniques to analyze survey (n=1,140) and interview (n=89) data, we find that faculty place relatively low emphasis on these skills, but that industry experience is significantly associated with teaching oral communication, teamwork, and problem-solving skills. Other factors, including race and perceptions of departmental teaching norms, also influenced skills-focused instruction. Industry experience also informed problem-based learning activities, knowledge of desired workplace skills, and a focus on divergent thinking. Given that industry experience is an important, but not the only influence on skills-focused instruction, policies aimed solely at hiring faculty with industry experience will be of limited utility without a corresponding focus on training in teaching and instructional design.

We study three-dimensional time-reversal-invariant topological superconductivity in noncentrosymmetric materials such as RhSi, CoSi, and AlPt which host coupled multifold nodes energetically split by the spin-orbit coupling at the same time-reversal-invariant momentum (TRIM). The topological superconductivity arises from the $s_{+} \oplus s_{-}$ gap function, which is $\boldsymbol{k}$ independent, but with opposite signs for the two nodes split at the same TRIM. We consider various electron-electron interactions in the tight-binding model for RhSi and find that the topological superconducting phase supporting a surface Majorana cone and topological nodal rings is favored in a wide range of interaction parameters., Comment: 6+12 pages, 3+4 figures

Integration of data from multiple omics techniques is becoming increasingly important in biomedical research. Due to non-uniformity and technical limitations in omics platforms, such integrative analyses on multiple omics, which we refer to as views, involve learning from incomplete observations with various view-missing patterns. This is challenging because i) complex interactions within and across observed views need to be properly addressed for optimal predictive power and ii) observations with various view-missing patterns need to be flexibly integrated. To address such challenges, we propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Most importantly, by modeling the joint representations as a product of marginal representations, we can efficiently learn from observed views with various view-missing patterns. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks., Comment: This paper is accepted to AISTATS 2021

Greene-Jabuka and Lecuona confirmed the slice-ribbon conjecture for 3-stranded pretzel knots except for an infinite family $P(a,-a-2,-\frac{(a+1)^2}{2})$ where $a$ is an odd integer greater than $1$. Lecuona and Miller showed that $P(a,-a-2,-\frac{(a+1)^2}{2})$ are not slice unless $a\equiv 1, 11, 37, 47, 59 \pmod{60}$. In this note, we show that four-fifths of the remaining knots in the family are not slice.

Due to the wider availability of modern electronic health records, patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipating patients' prognoses by identifying "similar" patients, and designing treatment guidelines that are tailored to homogeneous patient subgroups. In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e.g., adverse events, the onset of comorbidities). To encourage each cluster to have homogeneous future outcomes, the clustering is carried out by learning discrete representations that best describe the future outcome distribution based on novel loss functions. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks and identifies meaningful clusters that can be translated into actionable information for clinical decision-making.

Research indicates that teamwork, communication, self-directed learning, and problem-solving skills are strongly linked to individual academic and professional success, yet little is known regarding how college educators and employer trainers learn to better teach or train others in these valuable skills in postsecondary and employment STEM contexts. This pilot study uses social network analysis--a research perspective studying relationships or "social ties" to better understand the ways interactions influence behavior--to explore the dimensions of educator and trainer discussions regarding methods for helping students or employees acquire important skills. The study also examines whether educators and employers believe such discussions influence their instruction. A descriptive analysis of data from online surveys collected from educators (n=192) and employers (n=70) in technology and manufacturing fields in southern Wisconsin and western New York indicates respondents frequently engage in such teaching- and training-focused discussions with people inside and outside their colleges and businesses. Though more college educators are involved in such conversations than employers, employer trainers who engage in such conversations do so with individuals affiliated with more diverse organizations. Results also indicate that educators and employers who have these discussions do so at a similar frequency. Finally, most educators and employers with teaching- and training-focused social networks perceive them to be beneficial to their teamwork, communication, self-directed learning, and problem-solving instruction. In light of these findings, leaders hoping to further develop teaching- and training-focused social networks in education and employment fields may find more success in openly promoting the importance of such social ties as well as providing more opportunities for intra- and interorganizational professional development in instruction.

Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are factors that affect both an intervention and its outcome. Most previous work relies on the unconfoundedness assumption, which posits that all the confounders are measured in the observational data. However, if there are unmeasurable (latent) confounders, then confounding bias is introduced. Fortunately, noisy proxies for the latent confounders are often available and can be used to make an unbiased estimate of the ITE. In this paper, we develop a novel adversarial learning framework to make unbiased estimates of the ITE using noisy proxies., Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

Understanding correlation effects in topological phases and their transitions is a cutting-edge area of research in recent condensed matter physics. We study topological quantum phase transitions (TQPTs) between double-Weyl semimetals (DWSMs) and insulators, and argue that a novel class of quantum criticality appears at the TQPT characterized by emergent anisotropic non-Fermi liquid behaviors, in which the interplay between the Coulomb interaction and electronic critical modes induces not only anisotropic renormalization of the Coulomb interaction but also strongly correlated electronic excitation in three spatial dimensions. Using the standard renormalization group methods, large $N_f$ theory and the $\epsilon= 4-d$ method with fermion flavor number $N_f$ and spatial dimension $d$, we obtain the anomalous dimensions of electrons ($\eta_f=0.366/N_f $) in large $N_f$ theory and the associated anisotropic scaling relations of various physical observables. Our results may be observed in candidate materials for DWSMs such as HgCr$_2$Se$_4$ or SrSi$_2$ when the system undergoes a TQPT., Comment: Published at Physical Review Letters (Phys. Rev. Lett. 122, 187601 (2019)). Main text: 6 pages, 3 figures, Supplemental Material: 21 pages, 5 figures; the first two authors contributed equally to this work