2,769 research outputs found
Macroscopic Quantum Coherence in Small Antiferromagnetic Particle and the Quantum Interference Effects
Starting from the Hamiltonian operator of the noncompensated two-sublattice
model of a small antiferromagnetic particle, we derive the effective Lagrangian
of a biaxial antiferromagnetic particle in an external magnetic field with the
help of spin-coherent-state path integrals. Two unequal level-shifts induced by
tunneling through two types of barriers are obtained using the instanton
method. The energy spectrum is found from Bloch theory regarding the periodic
potential as a superlattice. The external magnetic field indeed removes
Kramers' degeneracy, however a new quenching of the energy splitting depending
on the applied magnetic field is observed for both integer and half-integer
spins due to the quantum interference between transitions through two types of
barriers.Comment: 9 pages, Latex, 4 Postscript figure
Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles.
The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended.IMPORTANCE The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile
Many-Body Coherence in Quantum Transport
In this study, we propose the concept of harnessing quantum coherence to
control electron transport in a many-body system. Combining an open quantum
system technique based on Hubbard operators, we show that many-body coherence
can eliminate the well-known Coulomb staircase and cause strong negative
differential resistance. To explore the mechanism, we analytically derive the
current-coherence relationship in the zero electron-phonon coupling limit.
Furthermore, by incorporating a gate field, we demonstrate the possibility of
constructing a coherence-controlled transistor. This development opens up a new
direction for creating quantum electronic devices based on many-body coherence.Comment: 5 pages, 3 figure
The electromagnetic and gravitational-wave radiations of X-ray transient CDF-S XT2
Binary neutron star (NS) mergers may result in remnants of supra-massive or
even stable NS, which have been supported indirectly by observed X-ray plateau
of some gamma-ray bursts (GRBs) afterglow. Recently, Xue et al. (2019)
discovered a X-ray transient CDF-S XT2 that is powered by a magnetar from
merger of double NS via X-ray plateau and following stepper phase. However, the
decay slope after the plateau emission is a little bit larger than the
theoretical value of spin-down in electromagnetic (EM) dominated by losing its
rotation energy. In this paper, we assume that the feature of X-ray emission is
caused by a supra-massive magnetar central engine for surviving thousands of
seconds to collapse black hole. Within this scenario, we present the
comparisons of the X-ray plateau luminosity, break time, and the parameters of
magnetar between CDF-S XT2 and other short GRBs with internal plateau samples.
By adopting the collapse time to constrain the equation of state (EOS), we find
that three EOSs (GM1, DD2, and DDME2) are consistent with the observational
data. On the other hand, if the most released rotation energy of magnetar is
dominated by GW radiation, we also constrain the upper limit of ellipticity of
NS for given EOS, and it is range in . Its GW signal
can not be detected by aLIGO or even for more sensitive Einstein Telescope in
the future.Comment: 13 pages, 5 figures,1 table. Accepted for publication by Research in
Astronomy and Astrophysic
MO-VLN: A Multi-Task Benchmark for Open-set Zero-Shot Vision-and-Language Navigation
Given a natural language, a general robot has to comprehend the instruction
and find the target object or location based on visual observations even in
unexplored environments. Most agents rely on massive diverse training data to
achieve better generalization, which requires expensive labor. These agents
often focus on common objects and fewer tasks, thus are not intelligent enough
to handle different types of instructions. To facilitate research in open-set
vision-and-language navigation, we propose a benchmark named MO-VLN, aiming at
testing the effectiveness and generalization of the agent in the multi-task
setting. First, we develop a 3D simulator rendered by realistic scenarios using
Unreal Engine 5, containing more realistic lights and details. The simulator
contains three scenes, i.e., cafe, restaurant, and nursing house, of high value
in the industry. Besides, our simulator involves multiple uncommon objects,
such as takeaway cup and medical adhesive tape, which are more complicated
compared with existing environments. Inspired by the recent success of large
language models (e.g., ChatGPT, Vicuna), we construct diverse high-quality data
of instruction type without human annotation. Our benchmark MO-VLN provides
four tasks: 1) goal-conditioned navigation given a specific object category
(e.g., "fork"); 2) goal-conditioned navigation given simple instructions (e.g.,
"Search for and move towards a tennis ball"); 3) step-by-step instruction
following; 4) finding abstract object based on high-level instruction (e.g., "I
am thirsty").Comment: 18 page
Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving
Aiming towards a holistic understanding of multiple downstream tasks
simultaneously, there is a need for extracting features with better
transferability. Though many latest self-supervised pre-training methods have
achieved impressive performance on various vision tasks under the prevailing
pretrain-finetune paradigm, their generalization capacity to multi-task
learning scenarios is yet to be explored. In this paper, we extensively
investigate the transfer performance of various types of self-supervised
methods, e.g., MoCo and SimCLR, on three downstream tasks, including semantic
segmentation, drivable area segmentation, and traffic object detection, on the
large-scale driving dataset BDD100K. We surprisingly find that their
performances are sub-optimal or even lag far behind the single-task baseline,
which may be due to the distinctions of training objectives and architectural
design lied in the pretrain-finetune paradigm. To overcome this dilemma as well
as avoid redesigning the resource-intensive pre-training stage, we propose a
simple yet effective pretrain-adapt-finetune paradigm for general multi-task
training, where the off-the-shelf pretrained models can be effectively adapted
without increasing the training overhead. During the adapt stage, we utilize
learnable multi-scale adapters to dynamically adjust the pretrained model
weights supervised by multi-task objectives while leaving the pretrained
knowledge untouched. Furthermore, we regard the vision-language pre-training
model CLIP as a strong complement to the pretrain-adapt-finetune paradigm and
propose a novel adapter named LV-Adapter, which incorporates language priors in
the multi-task model via task-specific prompting and alignment between visual
and textual features.Comment: Accepted at NeurIPS 202
Detecting Exploit Primitives Automatically for Heap Vulnerabilities on Binary Programs
Automated Exploit Generation (AEG) is a well-known difficult task, especially
for heap vulnerabilities. Previous works first detected heap vulnerabilities
and then searched for exploitable states by using symbolic execution and
fuzzing techniques on binary programs. However, it is not always easy to
discovery bugs using fuzzing or symbolic technologies and solvable for internal
overflow of heap objects. In this paper, we present a solution DEPA to detect
exploit primitives based on primitive-crucial-behavior model for heap
vulnerabilities. The core of DEPA contains two novel techniques, 1)
primitive-crucial-behavior identification through pointer dependence analysis,
and 2) exploit primitive determination method which includes triggering both
vulnerabilities and exploit primitives. We evaluate DEPA on eleven real-world
CTF(capture the flag) programs with heap vulnerabilities and DEPA can discovery
arbitrary write and arbitrary jump exploit primitives for ten programs except
for program multi-heap. Results showed that primitive-crucial-behavior
identification and determining exploit primitives are accurate and effective by
using our approach. In addition, DEPA is superior to the state-of-the-art tools
in determining exploit primitives for the heap object internal overflowComment: 11 pages 9 figure
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