9,004 research outputs found
Forbidden Channels and SIMP Dark Matter
In this review, we focus on dark matter production from thermal freeze-out
with forbidden channels and SIMP processes. We show that forbidden channels can
be dominant to produce dark matter depending on the dark photon and / or dark
Higgs mass compared to SIMP.Comment: 5 pages, Prepared for the proceedings of the 13th International
Conference on Gravitation, 3-7 July 201
On thermal production of self-interacting dark matter
We consider thermal production mechanisms of self-interacting dark matter in
models with gauged symmetry. A complex scalar dark matter is stabilized
by the , that is the remnant of a local dark . Light dark matter
with large self-interaction can be produced from thermal freeze-out in the
presence of SM-annihilation, SIMP and/or forbidden channels. We show that dark
photon and/or dark Higgs should be relatively light for unitarity and then
assist the thermal freeze-out. We identify the constraints on the parameter
space of dark matter self-interaction and mass in cases that one or some of the
channels are important in determining the relic density.Comment: 26 pages, 11 figures, Version to appear in Journal of High Energy
Physic
Unitary inflaton as decaying dark matter
We consider the inflation model of a singlet scalar field (sigma field) with
both quadratic and linear non-minimal couplings where unitarity is ensured up
to the Planck scale. We assume that a symmetry for the sigma field is
respected by the scalar potential in Jordan frame but it is broken explicitly
by the linear non-minimal coupling due to quantum gravity. We discuss the
impacts of the linear non-minimal coupling on various dynamics from inflation
to low energy, such as a sizable tensor-to-scalar ratio, a novel reheating
process with quartic potential dominance, and suppressed physical parameters in
the low energy, etc. In particular, the linear non-minimal coupling leads to
the linear couplings of the sigma field to the Standard Model through the trace
of the energy-momentum tensor in Einstein frame. Thus, regarding the sigma
field as a decaying dark matter, we consider the non-thermal production
mechanisms for dark matter from the decays of Higgs and inflaton condensate and
show the parameter space that is compatible with the correct relic density and
cosmological constraints.Comment: 36 pages, 7 figures, v2: minor corrections made and references added,
v3: discussion on preheating added, accepted for Journal of High Energy
Physics, v4: Lyman-alpha bound included and inflationary predictions refined
for perturbative reheatin
A minimal flavored for -meson anomalies
We consider an anomaly-free model with favorable couplings to heavy
flavors in the Standard Model(SM), as motivated by -meson anomalies at LHCb.
Taking the charge to be , we can
explain the -meson anomalies without invoking extra charged fermions or
flavor violation beyond the SM. We show that there is a viable parameter space
with a small that is compatible with other meson decays, tau lepton and
neutrino experiments as well as the LHC dimuon searches. We briefly discuss the
prospects of discovering the gauge boson at the LHC in the proposed model.Comment: 20 pages, 4 figures, v2: references and discussion on electroweak
precision test added, v3: Version to appear in Physical Review
EFFECT OF AEROBIC EXERCISE INTERVENTION ON PAINFUL DIABETIC NEUROPATHY
Background: Painful diabetic peripheral neuropathy (DPN) is a common complication of diabetes. While the beneficial effect of exercise on diabetes has been well established, its effect specifically on painful DPN has not been thoroughly explored. The objective of this pilot study is to examine the effect of aerobic exercise on pain in DPN. Methods: Twelve Sedentary individuals with type 2 diabetes mellitus between ages 40-70 with clinical diagnosis of DPN were enrolled in a 16-week, 3X week supervised aerobic exercise program. Brief Pain Inventory-Diabetic Peripheral Neuropathy (BPI-DPN) was used to assess pain intensity (worst, least, average, now) and pain interference with daily life (activity, mood, walk, normal work, relationship, sleep, enjoyment of life) pre and post the intervention. BMI, maximum oxygen uptake (VO2max), hemoglobin A1c (HbA1c), and blood pressure were also measured pre and post the intervention as secondary outcomes of interest. Results: 10 of 12 (83.3%) (5 males/5 females; age 57 ± 4.59 years; duration of diabetes 12.2 ± 5.94 years) participants reported pain due to DPN on the BPI-DPN and were included in the analysis. In these participants, significant reductions in pain interference on walking (4.95±2.83pre/2.8±2.74post, 0.0073), normal work (5.3±3.16pre/3.5±3.06post, P=0.0478), relationship with others (3.55±3.62pre/1±1.15post, P=0.0264), and sleep (5.05±2.77pre/3.2±3.12post, P=0.0407) were observed following the intervention. The overall pain interference was also reduced (4.50±2.48pre/2.56±2.01post, P=0.0267). However, there was no change in pain intensity scores. VO2max showed a significant increase post-intervention, while BMI, HbA1c, and blood pressure remained unchanged. Conclusion: These preliminary results show reductions in perceived pain interference in people with painful DPN following an aerobic exercise intervention, without a change in pain intensity. Further validation by a randomized controlled trial is needed
Data Augmentation for Spoken Language Understanding via Joint Variational Generation
Data scarcity is one of the main obstacles of domain adaptation in spoken
language understanding (SLU) due to the high cost of creating manually tagged
SLU datasets. Recent works in neural text generative models, particularly
latent variable models such as variational autoencoder (VAE), have shown
promising results in regards to generating plausible and natural sentences. In
this paper, we propose a novel generative architecture which leverages the
generative power of latent variable models to jointly synthesize fully
annotated utterances. Our experiments show that existing SLU models trained on
the additional synthetic examples achieve performance gains. Our approach not
only helps alleviate the data scarcity issue in the SLU task for many datasets
but also indiscriminately improves language understanding performances for
various SLU models, supported by extensive experiments and rigorous statistical
testing.Comment: 8 pages, 3 figures, 4 tables, Accepted in AAAI201
Learning to Compose Task-Specific Tree Structures
For years, recursive neural networks (RvNNs) have been shown to be suitable
for representing text into fixed-length vectors and achieved good performance
on several natural language processing tasks. However, the main drawback of
RvNNs is that they require structured input, which makes data preparation and
model implementation hard. In this paper, we propose Gumbel Tree-LSTM, a novel
tree-structured long short-term memory architecture that learns how to compose
task-specific tree structures only from plain text data efficiently. Our model
uses Straight-Through Gumbel-Softmax estimator to decide the parent node among
candidates dynamically and to calculate gradients of the discrete decision. We
evaluate the proposed model on natural language inference and sentiment
analysis, and show that our model outperforms or is at least comparable to
previous models. We also find that our model converges significantly faster
than other models.Comment: AAAI 201
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