Spin-current Seebeck effect in quantum dot systems

We first bring up the concept of spin-current Seebeck effect based on a recent experiment [Nat. Phys. {\bf 8}, 313 (2012)], and investigate the spin-current Seebeck effect in quantum dot (QD) systems. Our results show that the spin-current Seebeck coefficient $S$ is sensitive to different polarization states of QD, and therefore can be used to detect the polarization state of QD and monitor the transitions between different polarization states of QD. The intradot Coulomb interaction can greatly enhance the $S$ due to the stronger polarization of QD. By using the parameters for a typical QD, we demonstrate that the maximum $S$ can be enhanced by a factor of 80. On the other hand, for a QD whose Coulomb interaction is negligible, we show that one can still obtain a large $S$ by applying an external magnetic field.Comment: 6 pages, 8 figure

An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients

Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be widely studied. While there is no consensus on whether MCIs actually "convert" to AD, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline image brain scan, of whether an MCI individual will convert to AD within a multi-year period following the initial clinical visit. This is in fact not a traditional supervised learning problem since, in ADNI, there are no definitive labeled examples of MCI conversion. Prior works have defined MCI subclasses based on whether or not clinical/cognitive scores such as CDR significantly change from baseline. There are concerns with these definitions, however, since e.g. most MCIs (and ADs) do not change from a baseline CDR=0.5, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative conversion definition, wherein an MCI patient is declared to be a converter if any of the patient's brain scans (at follow-up visits) are classified "AD" by an (accurately-designed) Control-AD classifier. This novel definition bootstraps the design of a second classifier, specifically trained to predict whether or not MCIs will convert. This second classifier thus predicts whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate this new definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations much more consistent with known AD brain region biomarkers. We also identify key prognostic region biomarkers, essential for accurately discriminating the converter and nonconverter groups

Superconducting state in the non-centrosymmetric Mg_{9.3}Ir_{19}B_{16.7} and Mg_{10.5}Ir_{19}B_{17.1} revealed by NMR

We report ^{11}B NMR measurements in non-centrosymmetric superconductors Mg_{9.3}Ir_{19}B_{16.7} (T_c=5.8 K) and Mg_{10.5}Ir_{19}B_{17.1} (T_c=4.8 K). The spin lattice relaxation rate and the Knight shift indicate that the Cooper pairs are predominantly in the spin-singlet state with an isotropic gap. However, Mg_{10.5}Ir_{19}B_{17.1} is found to have more defects and the spin susceptibility remains finite even in the zero-temperature limit. We interpret this result as that the defects enhance the spin-orbit coupling and bring about more spin-triplet component.Comment: for a proper, high-resolution Fig.5, contact the corresponding autho

Fidelity susceptibility and long-range correlation in the Kitaev honeycomb model

We study exactly both the ground-state fidelity susceptibility and bond-bond correlation function in the Kitaev honeycomb model. Our results show that the fidelity susceptibility can be used to identify the topological phase transition from a gapped A phase with Abelian anyon excitations to a gapless B phase with non-Abelian anyon excitations. We also find that the bond-bond correlation function decays exponentially in the gapped phase, but algebraically in the gapless phase. For the former case, the correlation length is found to be $1/\xi=2\sinh^{-1}[\sqrt{2J_z -1}/(1-J_z)]$, which diverges around the critical point $J_z=(1/2)^+$.Comment: 7 pages, 6 figure

Using Machine Learning to Identify the Most At-Risk Students in Physics Classes

Machine learning algorithms have recently been used to predict students' performance in an introductory physics class. The prediction model classified students as those likely to receive an A or B or students likely to receive a grade of C, D, F or withdraw from the class. Early prediction could better allow the direction of educational interventions and the allocation of educational resources. However, the performance metrics used in that study become unreliable when used to classify whether a student would receive an A, B or C (the ABC outcome) or if they would receive a D, F or withdraw (W) from the class (the DFW outcome) because the outcome is substantially unbalanced with between 10\% to 20\% of the students receiving a D, F, or W. This work presents techniques to adjust the prediction models and alternate model performance metrics more appropriate for unbalanced outcome variables. These techniques were applied to three samples drawn from introductory mechanics classes at two institutions ($N=7184$, $1683$, and $926$). Applying the same methods as the earlier study produced a classifier that was very inaccurate, classifying only 16\% of the DFW cases correctly; tuning the model increased the DFW classification accuracy to 43\%. Using a combination of institutional and in-class data improved DFW accuracy to 53\% by the second week of class. As in the prior study, demographic variables such as gender, underrepresented minority status, first-generation college student status, and low socioeconomic status were not important variables in the final prediction models.Comment: arXiv admin note: substantial text overlap with arXiv:2002.0196

Distribution of Spectral Lags in Gamma Ray Bursts

Using the data acquired in the Time To Spill (TTS) mode for long gamma-ray bursts (GRBs) collected by the Burst and Transient Source Experiment on board the Compton Gamma Ray Observatory (BATSE/CGRO), we have carefully measured spectral lags in time between the low (25-55 keV) and high (110-320 keV) energy bands of individual pulses contained in 64 multi-peak GRBs. We find that the temporal lead by higher-energy gamma-ray photons (i.e., positive lags) is the norm in this selected sample set of long GRBs. While relatively few in number, some pulses of several long GRBs do show negative lags. This distribution of spectral lags in long GRBs is in contrast to that in short GRBs. This apparent difference poses challenges and constraints on the physical mechanism(s) of producing long and short GRBs. The relation between the pulse peak count rates and the spectral lags is also examined. Observationally, there seems to be no clear evidence for systematic spectral lag-luminosity connection for pulses within a given long GRB.Comment: 20 pages, 4 figure