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1. Dating of young (<1 Ma) tephras: Using U‐Pb (zircon) and (U‐Th[‐Sm])/He (zircon, apatite, magnetite) chronometers to unravel the eruption age of a tephra in the Woodlark Rift of Papua New Guinea

   https://doi.org/10.1111/ter.12464  

   Österle, Jürgen E. ; Stockli, Daniel F. ; Seward, Diane ; Little, Timothy A. ( April 2020 , Terra Nova)

   The dating of volcanic tephras forms a critical cornerstone of chronostratigraphy and is paramount for the resolution of the geological timescale. (U‐Th[‐Sm])/He dating is an emerging tool in Quaternary tephrochronology and ideally suited to date tephras <1 Ma. We present zircon, magnetite and apatite (U‐Th[‐Sm])/He combined with zircon U‐Pb data for a Pleistocene tephra in syn‐rift strata of the Woodlark Rift in Papua New Guinea. The results reveal a young He age mode (~0.5 to 0.8 Ma), consistent with an autocrystic zircon U‐Pb crystallisation age of 0.8 ± 0.1 Ma, as well as a broad range of older (U‐Th[‐Sm])/He (~1.6 to 10.2 Ma) and U‐Pb (~4.4 to 107 Ma) ages. These data demonstrate the potential of integrated U‐Pb and (U‐Th[‐Sm])/He multi‐method chronometry for dating the youngest coherent age mode, detecting contaminant grains and evaluating the isotopic systematics of these techniques.

    

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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. 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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5. Introducing a Query Acceleration Path for Analytics in SQLite3

   Martin Prammer, Suryadev Sahadevan ( January 2022 , Conference on Innovative Data Systems Research (CIDR))

   As large scale data processing becomes an ever more prominent component of modern computing tasks, databases now exist as a fundamental necessity of most computational platforms. However, in many cases there exists a disparity between the specializations of database management systems and the needs of the applications that run on them. The distinction between transactional and analyt- ical workloads for databases has been well established, but not fully addressed within the space of the most widely used embedded data- base system, namely SQLite3. To overcome this shortcoming, we implement SQLite3/HE, an analytical database engine implemented as an alternative execution path for SQLite. Through the utilization of an additional, complementary storage layer, SQLite3/HE trans- forms SQLite into a hybrid database system, able to fully leverage the benefits of both row and columnar storage layouts. SQLite3/HE improves the performance of analytical queries in the 100x-1000x speedup range, at no cost to the existing transactional query perfor- mance. These results validate the decision to implement SQLite3/HE as an alternative execution path, enabling it to serve as a drop-in replacement for SQLite3 in existing systems. 

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