2,456 research outputs found
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 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 due to the stronger
polarization of QD. By using the parameters for a typical QD, we demonstrate
that the maximum 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 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 , which diverges
around the critical point .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 (, , and ). 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
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