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Per- and Polyfluoroalkyl Substances (PFAS) are an emerging class of persistent organic pollutants. Although their thermal/chemical stability and water/stain repellence enable their widespread use in various products, such as personal care products, food packaging and firefighting foams, these properties also make them particularly resistant to degradation. This unwelcome persistence, with their trace concentrations, environmental prevalence, bioaccumulation and probable toxicities, poses a potential threat to environmental and human health. As such, much work is directed to finding ways to efficiently abate PFAS in the environment. Per- and Polyfluoroalkyl Substance Treatment Technologies provides a thorough review of the current state of research in treatment technologies for removing PFAS from the environment, particularly water. Beginning with a brief introduction to PFAS challenges and research needs, it covers established and promising technologies for PFAS removal from drinking water, wastewater, and groundwater. This is a great book for environmental engineers, environmental chemists, and industrialists interested in pollution remediation. 

Recent advances in Dynamic Sparse Training (DST) have pushed the frontier of sparse neural network training in structured and unstructured contexts, matching dense-model performance while drastically reducing parameter counts to facilitate model scaling. However, unstructured sparsity often fails to translate into practical speedups on modern hardware. To address this shortcoming, we propose DynaDiag, a novel structured sparse-to-sparse DST method that performs at par with unstructured sparsity. DynaDiag enforces a diagonal sparsity pattern throughout training and preserves sparse computation in forward and backward passes. We further leverage the diagonal structure to accelerate computation via a custom CUDA kernel, rendering the method hardware-friendly. Empirical evaluations on diverse neural architectures demonstrate that our method maintains accuracy on par with unstructured counterparts while benefiting from tangible computational gains. Notably, with 90% sparse linear layers in ViTs, we observe up to a 3.13x speedup in online inference without sacrificing model performance and a 1.59x speedup in training on a GPU compared to equivalent unstructured layers. 

Energy-efficient image acquisition on the edge is crucial for enabling remote sensing applications where the sensor node has weak compute capabilities and must transmit data to a remote server/cloud for processing. To reduce the edge energy consumption, this paper proposes a sensor-algorithm co-designed system called SNAPPIX, which compresses raw pixels in the analog domain inside the sensor. We use coded exposure (CE) as the in-sensor compression strategy as it offers the flexibility to sample, i.e., selectively expose pixels, both spatially and temporally. SNAPPIX has three contributions. First, we propose a task-agnostic strategy to learn the sampling/exposure pattern based on the classic theory of efficient coding. Second, we co- design the downstream vision model with the exposure pattern to address the pixel-level non-uniformity unique to CE-compressed images. Finally, we propose lightweight augmentations to the image sensor hardware to support our in-sensor CE compres- sion. Evaluating on action recognition and video reconstruction, SNAPPIX outperforms state-of-the-art video-based methods at the same speed while reducing the energy by up to 15.4×. We have open-sourced the code at: https://github.com/horizon- research/SnapPix. 

Posterior sampling with the spike-and-slab prior [MB88], a popular multimodal distribution used to model uncertainty in variable selection, is considered the theoretical gold standard method for Bayesian sparse linear regression [CPS09, Roc18]. However, designing provable algorithms for performing this sampling task is notoriously challenging. Existing posterior samplers for Bayesian sparse variable selection tasks either require strong assumptions about the signal-to-noise ratio (SNR) [YWJ16], only work when the measurement count grows at least linearly in the dimension [MW24], or rely on heuristic approximations to the posterior. We give the first provable algorithms for spike-and-slab posterior sampling that apply for any SNR, and use a measurement count sublinear in the problem dimension. Concretely, assume we are given a measurement matrix X∈ℝn×d and noisy observations y=Xθ⋆+ξ of a signal θ⋆ drawn from a spike-and-slab prior π with a Gaussian diffuse density and expected sparsity k, where ξ∼(𝟘n,σ2In). We give a polynomial-time high-accuracy sampler for the posterior π(⋅∣X,y), for any SNR σ−1 > 0, as long as n≥k3⋅polylog(d) and X is drawn from a matrix ensemble satisfying the restricted isometry property. We further give a sampler that runs in near-linear time ≈nd in the same setting, as long as n≥k5⋅polylog(d). To demonstrate the flexibility of our framework, we extend our result to spike-and-slab posterior sampling with Laplace diffuse densities, achieving similar guarantees when σ=O(1k) is bounded. 

Abstract We use the Karl G. Jansky Very Large Array (VLA) and the Atacama Large Millimeter/submillimeter Array to detect CO(1–0), CO(3–2), and rest-frame 349 GHz continuum emission from an Hi-selected galaxy, DLA1020+2733g, atz ≈ 2.3568 in the field of thez= 2.3553 damped Lyαabsorber (DLA) toward QSO J1020+2733. The VLA CO(1–0) detection yields a molecular gas mass of (2.84 ± 0.42) × 10¹¹ × (α_CO/4.36)M_⊙, the largest ever measured in an Hi-selected galaxy. The DLA metallicity is +0.28 ± 0.16, from the Zniiλ2026 absorption line detected in a Keck Echellette Spectrograph and Imager spectrum. This continues the trend of high-metallicity DLAs being frequently associated with massive galaxies. We obtain a star formation rate (SFR) of ≲400M_⊙yr⁻¹from the rest-frame 349 GHz continuum emission and a relatively long molecular gas depletion timescale of ≳0.6 Gyr. The excitation of theJ= 3 rotational level is subthermal, with $r_{31}\equiv L{\prime }_{\mathrm{CO}(3-2)}/L{\prime }_{\mathrm{CO}(1-0)}=0.513\pm 0.081$, suggesting that DLA1020+2733g has a low SFR surface density. The large velocity spread of the CO lines, ≈500 km s⁻¹, and the long molecular gas depletion timescale suggest that DLA1020+2733g is likely to be a cold rotating-disk galaxy. 

Growing interconnect bandwidth demand in large datacenters requires energy-efficient optical transceivers that operate with four-level pulse amplitude modulation (PAM4) to enable high per-wavelength data rates. Further increases in bandwidth density is possible by leveraging wavelength-division multiplexing (WDM), which optical link architectures based on silicon photonic microring modulators (MRMs) and drop filters inherently enable. This paper presents high-speed PAM4 transmitter and receiver front-ends implemented in a 28nm CMOS process that are co-designed with these silicon photonic optical devices to enable energy-efficient operation. The transmitter utilizes an optical digital-to-analog converter (DAC) approach with two PAM2 AC-coupled pulsed-cascode high-swing voltage-mode output stages to drive the MRM MSB/LSB segments. A 3.42Vppd output swing is achieved when operating at 80Gb/s PAM4 with an energy efficiency of 3.66pJ/bit. The receiver front-end interfaces with a silicon-germanium avalanche photodiode (APD) and utilizes a low-bandwidth input transimpedance amplifier followed by continuous-time linear equalizer and variable-gain amplifier stages. Biasing the APD to realize a gain of 2 allows for -7dBm optical modulation amplitude (OMA) sensitivity at 56Gb/s PAM4 with a BER=10-4 and an energy efficiency of 1.61pJ/bit. Experimental verification of the full PAM4 transceiver at 50Gb/s operation shows -4.66dBm OMA sensitivity at a BER~4x10-4. 

Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software.