Search for: All records

Abstract The transition towards designs which co-package electronic and photonic die together in data center switch packages has created a scaling path to Petabyte per second (Pbps) input/output (I/O) in such systems. In a co-packaged design, the scaling of bandwidth, cost, and energy will be governed by the number of optical I/O channels and the data rate per channel. While optical communication provide an opportunity to exploit wavelength division multiplexing to scale data rate, the limited 127 µm pitch of V-groove based single mode fiber arrays and the use of active alignment and bonding for their packaging present challenges to scaling the number of optical channels. Flip-chip optical couplers which allow for low loss, broadband operation and automated passive assembly represent a solution for continued scaling. In this paper, we propose a novel scheme to vertically couple between silicon based waveguides on separate chips using graded index couplers in combination with an evanescent coupler. Simulation results using a 3D finite-difference time-domain solver are presented, demonstrating coupling losses as low as 0.35 dB for a chip-to-chip gap of 11 µm; 1 dB vertical and lateral alignment tolerances of approximately 2.45 µm and ± 2.66 µm, respectively; and a possible 1 dB bandwidth of greater than 1500 nm. These results demonstrate the potential of our coupler as a universal interface in future co-packaged optics systems. 

This paper describes a new abstract interpretation-based approach to verify temporal safety properties of recursive, higher-order programs. While prior works have provided theoretical impact and some automation, they have had limited scalability. We begin with a new automata-based abstract effect domain for summarizing context-sensitive dependent effects, capable of abstracting relations between the program environment and the automaton control state. Our analysis includes a new transformer for abstracting event prefixes to automatically computed context-sensitive effect summaries, and is instantiated in a type-and-effect system grounded in abstract interpretation. Since the analysis is parametric on the automaton, we next instantiate it to a broader class of history/register (or accumulator) automata, beyond finite state automata to express some context-free properties, input-dependency, event summation, resource usage, cost, equal event magnitude, etc. We implemented a prototype evDrift that computes dependent effect summaries (and validates assertions) for OCaml-like recursive higher-order programs. As a basis of comparison, we describe reductions to assertion checking for higher-order but effect-free programs, and demonstrate that our approach outperforms prior tools Drift, RCaml/Spacer, MoCHi, and ReTHFL. Overall, across a set of 23 benchmarks, Drift verified 12 benchmarks, RCaml/Spacer verified 6, MoCHi verified 11, ReTHFL verified 18, and evDrift verified 21; evDrift also achieved a 6.3x, 5.3x, 16.8x, and 6.4x speedup over Drift, RCaml/Spacer, MoCHi, and ReTHFL, respectively, on those benchmarks that both tools could solve. 

Randomized load-balancing algorithms play an important role in improving performance in large-scale networks at relatively low computational cost. A common model of such a system is a network of N parallel queues in which incoming jobs with independent and identically distributed service times are routed on arrival using the join-the-shortest-of-d-queues routing algorithm. Under fairly general conditions, it was shown by Aghajani and Ramanan that as the size of the system goes to infinity, the state dynamics converge to the unique solution of a countable system of coupled deterministic measure-valued equations called the hydrodynamic equations. In this article, a characterization of invariant states of these hydrodynamic equations is obtained and, when d=2, used to construct a numerical algorithm to compute the queue length distribution and mean virtual waiting time in the invariant state. Additionally, it is also shown that under a suitable tail condition on the service distribution, the queue length distribution of the invariant state exhibits a doubly exponential tail decay, thus demonstrating a vast improvement in performance over the case [Formula: see text], which corresponds to random routing, when the tail decay could even be polynomial. Furthermore, numerical evidence is provided to support the conjecture that the invariant state is the limit of the steady-state distributions of the N-server models. The proof methodology, which entails analysis of a coupled system of measure-valued equations, can potentially be applied to other many-server systems with general service distributions, where measure-valued representations are useful. 

Harnessing the potential of exhaled breath analysis is an emerging frontier in medical diagnostics, given breath is a rich source of volatile organic compound (VOC) biomarkers for different medical conditions. A current downfall in this field, however, is the lack of standardized and widely available methods for offline sampling of exhaled VOCs. Herein, strides are taken toward the standardization of breath sampling in Tedlar bags by exploring several factors that can impact VOC heterogeneity, including tubing material, chemical composition of collection bags, breath fractionation, exhalation volume, and transfer flow rate. After bag-based sampling standardization, performance was benchmarked using two offline breath sampling methods, Tedlar bags and the Respiration Collector for In Vitro Analysis (ReCIVA). Three volunteers from the laboratory with no known respiratory diseases donated ≥ n = 5 samples collected onto adsorption tubes via each method, which were analyzed through thermal desorption (TD) coupled with gas chromatography-mass spectrometry (GC–MS). Data processing revealed a set of 15 highly reliable on-breath VOCs detected across volunteers, and most analytes (except indole) demonstrated higher sensitivity using Tedlar bags. Calculating relative standard deviation (RSD) values showed Tedlar bags were also significantly more reproducible compared to the ReCIVA (p < 0.03). Agreement between the two methods was demonstrated through correlating VOC signals with high statistical significance (R2 = 0.70), indicating both devices are well situated for biomarker discovery applications. 

We present a high-speed underwater optical backscatter communication technique based on acousto-optic light steering. Our approach enables underwater assets to transmit data at rates potentially reaching hundreds of Mbps, vastly outperforming current state-of-the-art optical and underwater backscatter systems, which typically operate at only a few kbps. In our system, a base station illuminates the backscatter device with a pulsed laser and captures the retroreflected signal using an ultrafast photodetector. The backscatter device comprises a retroreflector and a 2 MHz ultrasound transducer. The transducer generates pressure waves that dynamically modulate the refractive index of the surrounding medium, steering the light either toward the photodetector (encodingbit1) or away from it (encodingbit0). Using a 3-bit redundancy scheme, our prototype achieves a communication rate of approximately 0.66 Mbps with an energy consumption of ≤ 1 μJ/bit, representing a 60× improvement over prior techniques. We validate its performance through extensive laboratory experiments in which remote underwater assets wirelessly transmit multimedia data to the base station under various environmental conditions. 

In recent years, neural networks (NNs) have been embraced by several scientific and engineering disciplines for diverse modeling and inferencing applications. The importance of quantifying the confidence in NN predictions has escalated due to the increasing adoption of these decision models. Nevertheless, conventional NN do not furnish uncertainty estimates associated with their predictions and are therefore ill-calibrated. Uncertainty quantification techniques offer probability distributions or CIs to represent the uncertainty associated with NN predictions, instead of solely presenting the point predictions/estimates. Once the uncertainty in NN is quantified, it is crucial to leverage this information to modify training objectives and improve the accuracy and reliability of the corresponding decision models. This work presents a novel framework to utilize the knowledge of input and output uncertainties in NN to guide querying process in the context of Active Learning. We also derive the lower and upper bounds for label complexity. The efficacy of the proposed framework is established by conducting experiments across classification and regression tasks.