Learning How to Demodulate from Few Pilots via Meta-Learning

Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training in order to learn a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data

Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning

This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characterized by unique transmission non-idealities, such as I/Q imbalance. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side distortion. This paper proposes to tackle this problem by using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated, including Model-Agnostic Meta-Learning (MAML), First-Order MAML (FOMAML), REPTILE, and fast Context Adaptation VIA meta-learning (CAVIA). Both offline and online solutions are developed. In the latter case, an integrated online meta-learning and adaptive pilot number selection scheme is proposed. Numerical results validate the advantages of meta-learning as compared to training schemes that either do not leverage prior transmissions or apply a standard joint learning algorithms on previously received data.Comment: journal paper to appear in IEEE Transactions on Signal Processing, subsumes (arXiv:1903.02184

The Rice CHD3/Mi-2 Chromatin Remodeling Factor Rolled Fine Striped Promotes Flowering Independent of Photoperiod

Genetic studies have revealed that chromatin modifications affect flowering time, but the underlying mechanisms by which chromatin remodeling factors alter flowering remain largely unknown in rice (Oryza sativa). Here, we show that Rolled Fine Striped (RFS), a chromodomain helicase DNA-binding 3 (CHD3)/Mi-2 subfamily ATP-dependent chromatin remodeling factor, promotes flowering in rice. Diurnal expression of RFS peaked at night under short-day (SD) conditions and at dawn under long-day (LD) conditions. The rfs-1 and rfs-2 mutants (derived from different genetic backgrounds) displayed a late-flowering phenotype under SD and LD conditions. Reverse transcription-quantitative PCR analysis revealed that among the flowering time-related genes, the expression of the major floral repressor Grain number and heading date 7 (Ghd7) was mainly upregulated in rfs mutants, resulting in downregulation of its downstream floral inducers, including Early heading date 1 (Ehd1), Heading date 3a (Hd3a), and Rice FLOWERING LOCUS T 1 (RFT1). The rfs mutation had pleiotropic negative effects on rice grain yield and yield components, such as plant height and fertility. Taking these observations together, we propose that RFS participates in multiple aspects of rice development, including the promotion of flowering independent of photoperiod

Factors to consider during anesthesia in patients undergoing preemptive kidney transplantation: a propensity-score matched analysis

Abstract Background International guidelines have recommended preemptive kidney transplantation (KT) as the preferred approach, advocating for transplantation before the initiation of dialysis. This approach is advantageous for graft and patient survival by avoiding dialysis-related complications. However, recipients of preemptive KT may undergo anesthesia without the opportunity to optimize volume status or correct metabolic disturbances associated with end-stage renal disease. In these regard, we aimed to investigate the anesthetic events that occur more frequently during preemptive KT compared to nonpreemptive KT. Methods This is a single-center retrospective study. Of the 672 patients who underwent Living donor KT (LDKT), 388 of 519 who underwent nonpreemptive KT were matched with 153 of 153 who underwent preemptive KT using propensity score based on preoperative covariates. The primary outcome was intraoperative hypotension defined as area under the threshold (AUT), with a threshold set at a mean arterial blood pressure below 70 mmHg. The secondary outcomes were intraoperative metabolic acidosis estimated by base excess and serum bicarbonate, electrolyte imbalance, the use of inotropes or vasopressors, intraoperative transfusion, immediate graft function evaluated by the nadir creatinine, and re-operation due to bleeding. Results After propensity score matching, we analyzed 388 and 153 patients in non-preemptive and preemptive groups. The multivariable analysis revealed the AUT of the preemptive group to be significantly greater than that of the nonpreemptive group (mean ± standard deviation, 29.7 ± 61.5 and 14.5 ± 37.7, respectively, P = 0.007). Metabolic acidosis was more severe in the preemptive group compared to the nonpreemptive group. The differences in the nadir creatinine value and times to nadir creatinine were statistically significant, but clinically insignificant. Conclusion Intraoperative hypotension and metabolic acidosis occurred more frequently in the preemptive group during LDKT. These findings highlight the need for anesthesiologists to be prepared and vigilant in managing these events during surgery

A top-crossover-to-bottom addressed segmented annular array using piezoelectric micromachined ultrasonic transducers

We design and fabricate segmented annular arrays (SAAs) using piezoelectric micromachined ultrasonic transducers (pMUTs) to demonstrate the feasibility of acoustic focusing of ultrasound. The fabricated SAAs have 25 concentric top-electrode signal lines and eight bottom-electrodes for grounding to enable electronic steering of selectively grouped ultrasonic transducers from 2393 pMUT elements. Each element in the array is connected by top-crossover-to-bottom metal bridges, which reduce the parasitic capacitance. Circular-shaped pMUT elements, 120 μm in diameter, are fabricated using 1 μm-thick sol-gel lead zirconate titanate on a silicon wafer. To utilize the high-density pMUT array, a deep reactive ion etching process is used for anisotropic silicon etching to realize the transducer membranes. The resonant frequency and effective coupling coefficient of the elements, measured with an impedance analyzer, yields 1.517 MHz and 1.29%, respectively, in air. The SAAs using pMUTs are packaged on a printed circuit board and coated with parylene C for acoustic intensity measurements in water. The ultrasound generated by each segmented array is focused on a selected point in space. When a 5 Vpp, 1.5 MHz square wave is applied, the maximum spatial peak temporal average intensity () is found to be 79 mW cm-2 5 mm from the SAAs' surface without beamforming. The beam widths (-3 dB) of ultrasonic radiation patterns in the elevation and azimuth directions are recorded as 3 and 3.4 mm, respectively. The results successfully show the feasibility of focusing ultrasound on a small area with SAAs using pMUTs. © 2015 IOP Publishing Ltd.1