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1. Primordial Helium-3 Redux: The Helium Isotope Ratio of the Orion Nebula*

   https://doi.org/10.3847/1538-4357/ac6503  

   Cooke, Ryan J.; Noterdaeme, Pasquier; Johnson, James W.; Pettini, Max; Welsh, Louise; Peroux, Celine; Murphy, Michael T.; Weinberg, David H. (June 2022, The Astrophysical Journal)

   Abstract We report the first direct measurement of the helium isotope ratio, 3 He/ 4 He, outside of the Local Interstellar Cloud, as part of science-verification observations with the upgraded CRyogenic InfraRed Echelle Spectrograph. Our determination of 3 He/ 4 He is based on metastable He i * absorption along the line of sight toward Θ 2 A Ori in the Orion Nebula. We measure a value 3 He/ 4 He = (1.77 ± 0.13) × 10 −4 , which is just ∼40% above the primordial relative abundance of these isotopes, assuming the Standard Model of particle physics and cosmology, ( 3 He/ 4 He) p = (1.257 ± 0.017) × 10 −4 . We calculate a suite of galactic chemical evolution simulations to study the Galactic build up of these isotopes, using the yields from Limongi & Chieffi for stars in the mass range M = 8–100 M ⊙ and Lagarde et al. for M = 0.8–8 M ⊙ . We find that these simulations simultaneously reproduce the Orion and protosolar 3 He/ 4 He values if the calculations are initialized with a primordial ratio 3 He / 4 He p = ( 1.043 ± 0.089 ) × 10 − 4 . Even though the quoted error does not include the model uncertainty, this determination agrees with the Standard Model value to within ∼2 σ . We also use the present-day Galactic abundance of deuterium (D/H), helium (He/H), and 3 He/ 4 He to infer an empirical limit on the primordial 3 He abundance, 3 He / H p ≤ ( 1.09 ± 0.18 ) × 10 − 5 , which also agrees with the Standard Model value. We point out that it is becoming increasingly difficult to explain the discrepant primordial 7 Li/H abundance with nonstandard physics, without breaking the remarkable simultaneous agreement of three primordial element ratios (D/H, 4 He/H, and 3 He/ 4 He) with the Standard Model values. 

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2. SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference

   Ran, Ran; Luo, Xinwei; Wang, Wei; Liu, Tao; Quan, Gang; Xu, Xiaolin; Ding, Caiwen; Wen, Wujie (July 2023, Proceedings of the 40th International Conference on Machine Learning, PMLR)

   Homomorphic Encryption (HE) is a promising technology to protect clients’ data privacy for Machine Learning as a Service (MLaaS) on public clouds. However, HE operations can be orders of magnitude slower than their counterparts for plaintexts and thus result in prohibitively high inference latency, seriously hindering the practicality of HE. In this paper, we propose a HE-based fast neural network (NN) inference framework–SpENCNN built upon the co-design of HE operation-aware model sparsity and the single-instruction-multiple-data (SIMD)-friendly data packing, to improve NN inference latency. In particular, we first develop an encryption-aware HE-group convolution technique that can partition channels among different groups based on the data size and ciphertext size, and then encode them into the same ciphertext by novel group-interleaved encoding, so as to dramatically reduce the number of bottlenecked operations in HE convolution. We further tailor a HE-friendly sub-block weight pruning to reduce the costly HE-based convolution operation. Our experiments show that SpENCNN can achieve overall speedups of 8.37×, 12.11×, 19.26×, and 1.87× for LeNet, VGG-5, HEFNet, and ResNet-20 respectively, with negligible accuracy loss. Our code is publicly available at https://github.com/ranran0523/SPECNN. 

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3. AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference

   Qian, Lou; Bian, Song; Lei, Jiang (October 2020, Advances in Neural Information Processing Systems)

   Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear layers by Homomorphic Encryption (HE) and nonlinear layers by Garbled Circuit (GC) is one of the most promising secure solutions to emerging Machine Learning as a Service (MLaaS). Unfortunately, a HPPNN suffers from long inference latency, e.g., ∼100 seconds per image, which makes MLaaS unsatisfactory. Because HE-based linear layers of a HPPNN cost 93% inference latency, it is critical to select a set of HE parameters to minimize computational overhead of linear layers. Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network. In this paper, for fast and accurate secure neural network inference, we propose an automated layer-wise parameter selector, AutoPrivacy, that leverages deep reinforcement learning to automatically determine a set of HE parameters for each linear layer in a HPPNN. The learning-based HE parameter selection policy outperforms conventional rule-based HE parameter selection policy. Compared to prior HPPNNs, AutoPrivacy-optimized HPPNNs reduce inference latency by 53%∼70% with negligible loss of accuracy. 

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4. Hematite (U-Th)/He thermochronometry detects asperity flash heating during laboratory earthquakes

   https://doi.org/10.1130/G46965.1  

   Calzolari, Gabriele; Ault, Alexis K.; Hirth, Greg; McDermott, Robert G. (March 2020, Geology)

   Abstract Evidence for coseismic temperature rise that induces dynamic weakening is challenging to directly observe and quantify in natural and experimental fault rocks. Hematite (U-Th)/He (hematite He) thermochronometry may serve as a fault-slip thermometer, sensitive to transient high temperatures associated with earthquakes. We test this hypothesis with hematite deformation experiments at seismic slip rates, using a rotary-shear geometry with an annular ring of silicon carbide (SiC) sliding against a specular hematite slab. Hematite is characterized before and after sliding via textural and hematite He analyses to quantify He loss over variable experimental conditions. Experiments yield slip surfaces localized in an ∼5–30-µm-thick layer of hematite gouge with <300-µm-diameter fault mirror (FM) zones made of sintered nanoparticles. Hematite He analyses of undeformed starting material are compared with those of FM and gouge run products from high-slip-velocity experiments, showing >71% ± 1% (1σ) and 18% ± 3% He loss, respectively. Documented He loss requires short-duration, high temperatures during slip. The spatial heterogeneity and enhanced He loss from FM zones are consistent with asperity flash heating (AFH). Asperities >200–300 µm in diameter, producing temperatures >900 °C for ∼1 ms, can explain observed He loss. Results provide new empirical evidence describing AFH and the role of coseismic temperature rise in FM formation. Hematite He thermochronometry can detect AFH and thus seismicity on natural FMs and other thin slip surfaces in the upper seismogenic zone of Earth’s crust. 

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5. Short-term variability of atmospheric helium revealed through a cryo-enrichment method

   https://doi.org/10.5194/amt-16-1551-2023  

   Birner, Benjamin; Morgan, Eric; Keeling, Ralph F. (January 2023, Atmospheric Measurement Techniques)

   Chen, Huilin (Ed.)

   Abstract. Tropospheric helium variations are tightly linked to CO2 due to the co-emission of He and CO2 from natural-gasburning. Recently, Birner et al. (2022a) showed that the global consumption of natural gas has measurably increased the He content of theatmosphere. Like CO2, He is also predicted to exhibit complex spatial and temporal variability on shorter timescales, butmeasurements of these short-term variations are lacking. Here, we present the development of an improved gas delivery and purification system for thesemi-continuous mass spectrometric measurement of the atmospheric He-to-nitrogen ratio (He/N2). The method replaces the chemicalgetter used previously by Birner et al. (2021, 2022a) to preconcentrate He in an air stream with a cryogenic trap which can be more simplyregenerated by heating and which improves the precision of the measurement to 22 per meg (i.e., 0.022 ‰) in10 min (1σ). Using this “cryo-enrichment” method, we measured the He/N2 ratios in ambient air at La Jolla (California,USA) over 5 weeks in 2022. During this period, He/N2 was strongly correlated with atmospheric CO2 concentrations, as expectedfrom anthropogenic emissions, with a diurnal cycle of 450–500 per meg (max–min) caused by the sea–land breeze pattern of local winds,which modulates the influence of local pollution sources. 

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