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 $\mu$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 $\mu$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

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