132 research outputs found
One symbol blind synchronization in SIMO molecular communication systems
Molecular communication offers new possibilities in the micro-and nano-scale application environments. Similar to other communication paradigms, molecular communication also requires clock synchronization between the transmitter and the receiver nanomachine in many time-and control-sensitive applications. This letter presents a novel high-efficiency blind clock synchronization mechanism. Without knowing the channel parameters of the diffusion coefficient and the transmitter-receiver distance, the receiver only requires one symbol to achieve synchronization. The samples are used to estimate the propagation delay by least square method and achieve clock synchronization. Single-input multiple-output (SIMO) diversity design is then proposed to mitigate channel noise and therefore to improve the synchronization accuracy. The simulation results show that the proposed clock synchronization mechanism has a good performance and may help chronopharmaceutical drug delivery applications
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models
Multivariate signals are prevalent in various domains, such as healthcare,
transportation systems, and space sciences. Modeling spatiotemporal
dependencies in multivariate signals is challenging due to (1) long-range
temporal dependencies and (2) complex spatial correlations between sensors. To
address these challenges, we propose representing multivariate signals as
graphs and introduce GraphS4mer, a general graph neural network (GNN)
architecture that captures both spatial and temporal dependencies in
multivariate signals. Specifically, (1) we leverage Structured State Spaces
model (S4), a state-of-the-art sequence model, to capture long-term temporal
dependencies and (2) we propose a graph structure learning layer in GraphS4mer
to learn dynamically evolving graph structures in the data. We evaluate our
proposed model on three distinct tasks and show that GraphS4mer consistently
improves over existing models, including (1) seizure detection from
electroencephalography signals, outperforming a previous GNN with
self-supervised pretraining by 3.1 points in AUROC; (2) sleep staging from
polysomnography signals, a 4.1 points improvement in macro-F1 score compared to
existing sleep staging models; and (3) traffic forecasting, reducing MAE by
8.8% compared to existing GNNs and by 1.4% compared to Transformer-based
models
Infrared Variability of Two Dusty White Dwarfs
The most heavily polluted white dwarfs often show excess infrared radiation
from circumstellar dust disks, which are modeled as a result of tidal
disruption of extrasolar minor planets. Interaction of dust, gas, and
disintegrating objects can all contribute to the dynamical evolution of these
dust disks. Here, we report on two infrared variable dusty white dwarfs, SDSS
J1228+1040 and G29-38. For SDSS J1228+1040, compared to the first measurements
in 2007, the IRAC [3.6] and [4.5] fluxes decreased by 20% by 2014 to a level
also seen in the recent 2018 observations. For G29-38, the infrared flux of the
10 m silicate emission feature became 10% stronger between 2004 and 2007,
We explore several scenarios that could account for these changes, including
tidal disruption events, perturbation from a companion, and runaway accretion.
No satisfactory causes are found for the flux drop in SDSS J1228+1040 due to
the limited time coverage. Continuous tidal disruption of small planetesimals
could increase the mass of small grains and concurrently change the strength of
the 10 m feature of G29-38. Dust disks around white dwarfs are actively
evolving and we speculate that there could be different mechanisms responsible
for the temporal changes of these disks.Comment: ApJ, in pres
Near-infrared variability in dusty white dwarfs: tracing the accretion of planetary material
The inwards scattering of planetesimals towards white dwarfs is expected to
be a stochastic process with variability on human time-scales. The
planetesimals tidally disrupt at the Roche radius, producing dusty debris
detectable as excess infrared emission. When sufficiently close to the white
dwarf, this debris sublimates and accretes on to the white dwarf and pollutes
its atmosphere. Studying this infrared emission around polluted white dwarfs
can reveal how this planetary material arrives in their atmospheres. We report
a near-infrared monitoring campaign of 34 white dwarfs with infrared excesses
with the aim to search for variability in the dust emission. Time series
photometry of these white dwarfs from the United Kingdom Infrared Telescope
(Wide Field Camera) in the J, H and K bands were obtained over baselines of up
to three years. We find no statistically significant variation in the dust
emission in all three near-infrared bands. Specifically, we can rule out
variability at ~1.3% for the 13 white dwarfs brighter than 16th mag in K band,
and at ~10% for the 32 white dwarfs brighter than 18th mag over time-scales of
three years. Although to date two white dwarfs, SDSS J095904.69-020047.6 and WD
1226+110, have shown K band variability, in our sample we see no evidence of
new K band variability at these levels. One interpretation is that the tidal
disruption events which lead to large variabilities are rare, occur on short
time-scales, and after a few years the white dwarfs return to being stable in
the near-infrared.Comment: 14 pages + supplementary figures, 9 figures, accepted for publication
in MNRA
Disk or Companion: Characterizing Excess Infrared Flux in Seven White Dwarf Systems with Near-Infrared Spectroscopy
Excess infrared flux from white dwarf stars is likely to arise from a dusty
debris disk or a cool companion. In this work, we present near-infrared
spectroscopic observations with Keck/MOSFIRE, Gemini/GNIRS, and
Gemini/Flamingos-2 of seven white dwarfs with infrared excesses identified in
previous studies. We confirmed the presence of dust disks around four white
dwarfs (Gaia J0611-6931, Gaia J0006+2858, Gaia J2100+2122, and WD 0145+234) as
well as two new white dwarf brown dwarf pairs (Gaia J0052+4505 and Gaia
J0603+4518). In three of the dust disk systems, we detected for the first time
near-infrared metal emissions (Mg I, Fe I, and Si I) from a gaseous component
of the disk. We developed a new Markov Chain Monte Carlo framework to constrain
the geometric properties of each dust disk. In three systems, the dust disk and
the gas disk appear to coincide spatially. For the two brown dwarf white dwarf
pairs, we identified broad molecular absorption features typically seen in L
dwarfs. The origin of the infrared excess around Gaia J0723+6301 remains a
mystery. Our study underlines how near-infrared spectroscopy can be used to
determine sources of infrared excess around white dwarfs, which has now been
detected in hundreds of systems photometrically.Comment: 23 pages, 10 figures, 5 tables, AJ, in pres
Shallow Ultraviolet Transits of WD 1145+017
WD 1145+017 is a unique white dwarf system that has a heavily polluted
atmosphere, an infrared excess from a dust disk, numerous broad absorption
lines from circumstellar gas, and changing transit features, likely from
fragments of an actively disintegrating asteroid. Here, we present results from
a large photometric and spectroscopic campaign with Hubble, Keck , VLT,
Spitzer, and many other smaller telescopes from 2015 to 2018. Somewhat
surprisingly, but consistent with previous observations in the u' band, the UV
transit depths are always shallower than those in the optical. We develop a
model that can quantitatively explain the observed "bluing" and the main
findings are: I. the transiting objects, circumstellar gas, and white dwarf are
all aligned along our line of sight; II. the transiting object is blocking a
larger fraction of the circumstellar gas than of the white dwarf itself.
Because most circumstellar lines are concentrated in the UV, the UV flux
appears to be less blocked compared to the optical during a transit, leading to
a shallower UV transit. This scenario is further supported by the strong
anti-correlation between optical transit depth and circumstellar line strength.
We have yet to detect any wavelength-dependent transits caused by the
transiting material around WD 1145+017.Comment: 16 pages, 11 figures, 6 tables, ApJ, in pres
New chondritic bodies identified in eight oxygen-bearing white dwarfs
We present observations and analyses of eight white dwarf stars that have
accreted rocky material from their surrounding planetary systems. The spectra
of these helium-atmosphere white dwarfs contain detectable optical lines of all
four major rock-forming elements (O, Mg, Si, Fe). This work increases the
sample of oxygen-bearing white dwarfs with parent body composition analyses by
roughly thirty-three percent. To first order, the parent bodies that have been
accreted by the eight white dwarfs are similar to those of chondritic
meteorites in relative elemental abundances and oxidation states. Seventy-five
percent of the white dwarfs in this study have observed oxygen excesses
implying volatiles in the parent bodies with abundances similar to those of
chondritic meteorites. Three white dwarfs have oxidation states that imply more
reduced material than found in CI chondrites, indicating the possible detection
of Mercury-like parent bodies, but are less constrained. These results
contribute to the recurring conclusion that extrasolar rocky bodies closely
resemble those in our solar system, and do not, as a whole, yield unusual or
unique compositions.Comment: Accepted for publication in ApJ. 7 Figures, 7 Table
Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes
Background: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes
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