7,170 research outputs found
Condensate wave function and elementary excitations of bosonic polar molecules: beyond the first Born approximation
We investigate the condensate wave function and elementary excitations of
strongly interacting bosonic polar molecules in a harmonic trap, treating the
scattering amplitude beyond the standard first Born approximation (FBA). By
using an appropriate trial wave function in the variational method, effects of
the leading order correction beyond the FBA have been investigated and shown to
be significantly enhanced when the system is close to the phase boundary of
collapse. How such leading order effect of going beyond the FBA can be observed
in a realistic experiment is also discussed.Comment: 7 pages, 4 figure
IsaB Inhibits Autophagic Flux to Promote Host Transmission of Methicillin-Resistant Staphylococcus aureus.
Methicillin-resistant Staphylococcus aureus (MRSA) has emerged as a major nosocomial pathogen that is widespread in both health-care facilities and in the community at large, as a result of direct host-to-host transmission. Several virulence factors are associated with pathogen transmission to naive hosts. Immunodominant surface antigen B (IsaB) is a virulence factor that helps Staphylococcus aureus to evade the host defense system. However, the mechanism of IsaB on host transmissibility remains unclear. We found that IsaB expression was elevated in transmissible MRSA. Wild-type isaB strains inhibited autophagic flux to promote bacterial survival and elicit inflammation in THP-1 cells and mouse skin. MRSA isolates with increased IsaB expression showed decreased autophagic flux, and the MRSA isolate with the lowest IsaB expression showed increased autophagic flux. In addition, recombinant IsaB rescued the virulence of the isaB deletion strain and increased the group A streptococcus (GAS) virulence in vivo. Together, these results reveal that IsaB diminishes autophagic flux, thereby allowing MRSA to evade host degradation. These findings suggest that IsaB is a suitable target for preventing or treating MRSA infection
Andreev and Single Particle Tunneling Spectroscopies in Underdoped Cuprates
We study tunneling spectroscopy between a normal metal and underdoped cuprate
superconductor modeled by a phenomenological theory in which the pseudogap is a
precursor to the undoped Mott insulator. In the transparent tunneling limit,
the spectra show a small energy gap associated with Andreev reflection. In the
Giaever limit, the spectra show a large energy gap associated with single
particle tunneling. Our theory semi-quantitatively describes the two gap
behavior observed in tunneling experiments.Comment: 5 pages, 4 figures, submitted to Phys. Rev. Lett. minor changes of
reference
PSYCHOMETRIC ANALYSES BASED ON EVIDENCE-CENTERED DESIGN AND COGNITIVE SCIENCE OF LEARNING TO EXPLORE STUDENTS' PROBLEM-SOLVING IN PHYSICS
Most analyses of physics assessment tests have been done within the framework of classical test theory in which only the number of correct answers is considered in the scoring. More sophisticated analyses have been developed recently by physics researchers to further study students' conceptions/misconceptions in physics learning to improve physics instruction. However, they are not connected with the well-developed psychometric machinery.
The goal of this dissertation is to use a formal psychometric model to study students' conceptual understanding in physics (in particular, Newtonian mechanics). The perspective is based on the evidence-centered design (ECD) framework, building on
previous analyses of the cognitive processes of physics problem-solving and the task design from two physics tests (Force Concept Inventory, FCI and Force Motion Concept Evaluation, FMCE) that are commonly used to measure students' conceptual understanding about force-motion relationships.
Within the ECD framework, the little-known Andersen/Rasch (AR) multivariate IRT model that can deal with mixtures of strategies within individuals is then introduced and discussed, including the issue of identification of the model. To demonstrate its usefulness, four data sets (one from FCI and three from FMCE) were used and analyzed with the AR model using a Markov Chain Monte Carlo estimation procedure, carried out with the BUGS computer program.
Results from the first three data sets (questions were used to assess students' understanding about force-motion relationships) indicate that most students are in a mixed model state (i.e., in a transition toward understanding Newtonian mechanics) after one semester of physics learning. In particular, they incorrectly tend to believe that there must be a force acting on an object to maintain its movement, one of the common misconceptions indicated in physics literature. Findings from the last data set (which deals with acceleration) indicate that although students have improved their understanding about acceleration after one semester of instruction, they may still find it difficult to represent their understanding in terms of acceleration-time graphs. This is especially so when the object is slowing down or moving toward the left, in which case the sign of acceleration in both task scenarios is negative
OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
Recent works have shown that by using large pre-trained models along with
learnable prompts, rehearsal-free methods for class-incremental learning (CIL)
settings can achieve superior performance to prominent rehearsal-based ones.
Rehearsal-free CIL methods struggle with distinguishing classes from different
tasks, as those are not trained together. In this work we propose a
regularization method based on virtual outliers to tighten decision boundaries
of the classifier, such that confusion of classes among different tasks is
mitigated. Recent prompt-based methods often require a pool of task-specific
prompts, in order to prevent overwriting knowledge of previous tasks with that
of the new task, leading to extra computation in querying and composing an
appropriate prompt from the pool. This additional cost can be eliminated,
without sacrificing accuracy, as we reveal in the paper. We illustrate that a
simplified prompt-based method can achieve results comparable to previous
state-of-the-art (SOTA) methods equipped with a prompt pool, using much less
learnable parameters and lower inference cost. Our regularization method has
demonstrated its compatibility with different prompt-based methods, boosting
those previous SOTA rehearsal-free CIL methods' accuracy on the ImageNet-R and
CIFAR-100 benchmarks. Our source code is available at
https://github.com/jpmorganchase/ovor.Comment: Accepted by ICLR 202
Catastrophic Emission of Charges from Near-Extremal Nariai Black Holes
Using the in-out formalism and also the monodromy method, we study the
emission of charges from near-extremal charged Nariai black holes with the
black hole and cosmological horizons close to each other. The emission becomes
catastrophic for a charge with energy greater than its chemical potential,
whose leading exponential factor increases inversely proportional to the
separation of two horizons. This implies that near-extremal Nariai black holes
quickly evaporate through the charge emission and end in the de Sitter space,
in contrast to near-extremal RN-dS black holes that have the
Breitenlohner-Friedman bound below which they become stable against Hawking
radiation and Schwinger effect of charge emission. We illuminate the origin of
the catastrophic emission in the phase-integral formulation by comparing
near-extremal charged Nariai black holes with near-extremal RN-dS black holes.Comment: 15 page
An Integrated Web-based System for MEDLINE Analysis: A Case Study of Chronic Kidney Disease
In the era of big data, medical researchers attempt to utilize some analysis techniques like machine learning and text mining on their large-scale corpora to save valuable labor work and time. Consequently, many data analysis platforms are built to support medical professionals such as Pubtator, GeneWays, BioContext, etc. These platforms are helpful to medical entities recognition and relation extraction, but there is not an integrated platform to support researchers’ various needs, and medical projects are isolated from each other, which is hard to be shared and reused. As a result, we present an integrated system containing ‘name entity recognition’, ‘document categorization’ and ‘association extraction’. Besides, we add the concept of ‘socialization’ making projects reusable for further analyses. A case study of chronic kidney disease was adopted to indicate the effectiveness of the proposed system
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