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Charge transport in solids at low temperature reveals a material’s mesoscopic properties and structure. Under a magnetic field, Shubnikov–de Haas (SdH) oscillations inform complex quantum transport phenomena that are not limited by the ground state characteristics and have facilitated extensive explorations of quantum and topological interest in two- and three-dimensional materials. Here, in elemental metal Cr with two incommensurately superposed lattices of ions and a spin-density-wave ground state, we reveal that the phases of several low-frequency SdH oscillations in${\sigma }_{\mathit{xx}} \left({\rho }_{\mathit{xx}}\right)$and${\sigma }_{\mathit{yy}} \left({\rho }_{\mathit{yy}}\right)$are no longer identical but opposite. These relationships contrast with the SdH oscillations from normal cyclotron orbits that maintain identical phases between${\sigma }_{\mathit{xx}} \left({\rho }_{\mathit{xx}}\right)$and${\sigma }_{\mathit{yy}} \left({\rho }_{\mathit{yy}}\right)$ . We trace the origin of the low-frequency SdH oscillations to quantum interference effects arising from the incommensurate orbits of Cr’s superposed reciprocal lattices and explain the observed$\pi$-phase shift by the reconnection of anisotropic joint open and closed orbits.

 

Noncollinear antiferromagnets with novel magnetic orders, vanishingly small net magnetization and exotic spin related properties hold enormous promise for developing next generation, transformative spintronic applications. A major ongoing research focus of this community is to explore, control, and harness unconventional magnetic phases of this emergent material system to deliver state-of-the-art functionalities for modern microelectronics. Here we report direct imaging of magnetic domains of polycrystalline Mn3Sn films, a prototypical noncollinear antiferromagnet, using nitrogen-vacancy-based single-spin scanning microscopy. Nanoscale evolution of local stray field patterns of Mn3Sn samples are systematically investigated in response to external driving forces, revealing the characteristic “heterogeneous” magnetic switching behaviors in polycrystalline textured Mn3Sn films. Our results contribute to a comprehensive understanding of inhomogeneous magnetic orders of noncollinear antiferromagnets, highlighting the potential of nitrogen-vacancy centers to study microscopic spin properties of a broad range of emergent condensed matter systems. 

Background Even before the onset of the COVID-19 pandemic, children and adolescents were experiencing a mental health crisis, partly due to a lack of quality mental health services. The rate of suicide for Black youth has increased by 80%. By 2025, the health care system will be short of 225,000 therapists, further exacerbating the current crisis. Therefore, it is of utmost importance for providers, schools, youth mental health, and pediatric medical providers to integrate innovation in digital mental health to identify problems proactively and rapidly for effective collaboration with other health care providers. Such approaches can help identify robust, reproducible, and generalizable predictors and digital biomarkers of treatment response in psychiatry. Among the multitude of digital innovations to identify a biomarker for psychiatric diseases currently, as part of the macrolevel digital health transformation, speech stands out as an attractive candidate with features such as affordability, noninvasive, and nonintrusive. Objective The protocol aims to develop speech-emotion recognition algorithms leveraging artificial intelligence/machine learning, which can establish a link between trauma, stress, and voice types, including disrupting speech-based characteristics, and detect clinically relevant emotional distress and functional impairments in children and adolescents. Methods Informed by theoretical foundations (the Theory of Psychological Trauma Biomarkers and Archetypal Voice Categories), we developed our methodology to focus on 5 emotions: anger, happiness, fear, neutral, and sadness. Participants will be recruited from 2 local mental health centers that serve urban youths. Speech samples, along with responses to the Symptom and Functioning Severity Scale, Patient Health Questionnaire 9, and Adverse Childhood Experiences scales, will be collected using an Android mobile app. Our model development pipeline is informed by Gaussian mixture model (GMM), recurrent neural network, and long short-term memory. Results We tested our model with a public data set. The GMM with 128 clusters showed an evenly distributed accuracy across all 5 emotions. Using utterance-level features, GMM achieved an accuracy of 79.15% overall, while frame selection increased accuracy to 85.35%. This demonstrates that GMM is a robust model for emotion classification of all 5 emotions and that emotion frame selection enhances accuracy, which is significant for scientific evaluation. Recruitment and data collection for the study were initiated in August 2021 and are currently underway. The study results are likely to be available and published in 2024. Conclusions This study contributes to the literature as it addresses the need for speech-focused digital health tools to detect clinically relevant emotional distress and functional impairments in children and adolescents. The preliminary results show that our algorithm has the potential to improve outcomes. The findings will contribute to the broader digital health transformation. International Registered Report Identifier (IRRID) DERR1-10.2196/46970 

A fundamental problem in legged locomotion is to verify whether a desired trajectory satisfies all physical constraints, especially those for maintaining contacts. Although foot tipping can be avoided via the Zero Moment Point (ZMP) condition, preventing foot sliding and twisting leads to the more complex Contact Wrench Cone (CWC) constraints. This paper proposes an efficient algorithm to certify the inclusion of a net contact wrench in the CWC on flat ground with uniform friction. In addition to checking the ZMP criterion, the proposed method also verifies whether the linear force and the yaw moment are feasible. The key step in the algorithm is a novel exact geometric characterization of the yaw moment limits in the case when the support polygon is approximated by a single supporting line. We propose two approaches to select this approximating line, providing an accurate inner approximation of the ground truth yaw moment limits with only 18.80% (resp. 7.13% ) error. The methods require only 1/150 (resp. 1/139 ) computation time compared to the exact CWC based on conic programming. As a benchmark, approximating the CWC using square friction pyramids requires similar computation time as the exact CWC, but has >19.35% error. Unlike the ZMP condition, our method provides a sufficient condition for contact wrench feasibility. 

Ischemic stroke is a serious cerebrovascular event that results in cell death, blood-brain barrier dysfunction, tissue degradation, and inflammation, often leading to permanent disability or death. As the incidence of ischemic stroke continues to rise globally, it is crucial to examine the mechanisms of the various proteins and molecules contributing to worsened patient outcome and recovery. Cathepsin L, a cysteine protease known for degrading tissues in lysosomes and elsewhere, may play a role in brain tissue loss and inflammation after stroke. Studies have suggested that cathepsin L appears in the ischemic core shortly after stroke is induced. Using immunohistochemical staining, mass spectrometry, and other assays, the increase of cathepsin L in the brain was correlated with extracellular matrix and perlecan degradation after ischemic stroke. Additionally, injection of a cathepsin L inhibitor significantly reduced brain infarct size and improved functional scores. More research is needed to elucidate cathepsin L's role in post-stroke inflammation and brain damage, in order to further explore the factors contributing to worsened patient outcome after ischemic stroke and work toward finding better therapeutic interventions. 

Novel noncollinear antiferromagnets with spontaneous time-reversal symmetry breaking, nontrivial band topology, and unconventional transport properties have received immense research interest over the past decade due to their rich physics and enormous promise in technological applications. One of the central focuses in this emerging field is exploring the relationship between the microscopic magnetic structure and exotic material properties. Here, the nanoscale imaging of both spin-orbit-torque-induced deterministic magnetic switching and chiral spin rotation in noncollinear antiferromagnet Mn3Sn films using nitrogen-vacancy (NV) centers is reported. Direct evidence of the off-resonance dipole-dipole coupling between the spin dynamics in Mn3Sn and proximate NV centers is also demonstrated with NV relaxometry measurements. These results demonstrate the unique capabilities of NV centers in accessing the local information of the magnetic order and dynamics in these emergent quantum materials and suggest new opportunities for investigating the interplay between topology and magnetism in a broad range of topological magnets.