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The demand for Deep Neural Network (DNN) execution (including both inference and training) on mobile system-ona-chip (SoCs) has surged, driven by factors like the need for real-time latency, privacy, and reducing vendors’ costs. Mainstream mobile GPUs (eg, Qualcomm Adreno GPUs) usually have a 2.5 D L1 texture cache that offers throughput superior to that of on-chip memory. However, to date, there is limited understanding of the performance features of such a 2.5 D cache, which limits the optimization potential. This paper introduces TMModel, a framework with three components: 1) a set of micro-benchmarks and a novel performance assessment methodology to characterize a non-well-documented architecture with 2D memory, 2) a complete analytical performance model configurable for different data access pattern (s), tiling size (s), and other GPU execution parameters for a given operator (and associated size and shape), and 3) a compilation framework incorporating this model and generating optimized code with low overhead. TMModel is validated both on a set of DNN kernels and for training complete models on mobile GPU. 

Halogen (F, Cl, Br, and I) concentrations for 129 loess samples from worldwide localities yield geometric means of 517 ± 53 μg/g F, 150 ± 20 μg/g Cl, 1.58 ± 0.16 μg/g Br, 1.16 ± 0.11 μg/g I (2 standard errors). These concentrations, notably for Br and I, are substantially higher than previous estimates for the average upper continental crystalline bedrocks, with enrichment factors of 1.3 +0.7/−0.4 (F), 1.8 +2.4/−0.8 (Cl), 3.8 +1.3/−1.0 (Br), and 39 +71/−16 (I) (95%confidence), documenting enrichment of halogens on the continental surface. These surface halogens are likely sourced from the oceans and may be influenced by climate fluctuations. Halogen ratios (Br/Cl, I/Cl, and Br/I) in loess are similar to those of organic-rich soils/sediments from both terrigenous and marine settings, suggesting that terrigenous and marine organic matter have indistinguishable halogen ratios. The Br/I ratios differ from those in the fine grained matrix of glacial diamictites, indicating that another process (beyond biological influence) is responsible for fractionating halogens in the upper continental crust. Using a mixing model, we calculate that over 80–90 % of loess originates from crystalline bedrocks, while the remainder (<10–20 %) derives from the halogen- and organic-rich sedimentary cover or other sources (e.g., marine aerosols). 

Generic statements like “tigers are striped” and “cars have radios” com- municate information that is, in general, true. However, while the first state- ment is true *in principle*, the second is true only statistically. People are exquisitely sensitive to this principled-vs-statistical distinction. It has been argued that this ability to distinguish between something being true by virtue of it being a category member versus being true because of mere statistical regularity, is a general property of people’s conceptual machinery and cannot itself be learned. We investigate whether the distinction between principled and statistical properties can be learned from language itself. If so, it raises the possibility that language experience can bootstrap core conceptual dis- tinctions and that it is possible to learn sophisticated causal models directly from language. We find that language models are all sensitive to statistical prevalence, but struggle with representing the principled-vs-statistical dis- tinction controlling for prevalence. Until GPT-4, which succeeds. 

Equations of motion for compressible point vortices in the plane are obtained in the limit of small Mach number, M , using a Rayleigh–Jansen expansion and the method of Matched Asymptotic Expansions. The solution in the region between vortices is matched to solutions around each vortex core. The motion of the vortices is modified over long time scales O ( M 2 log ⁡ M ) and O ( M 2 ) . Examples are given for co-rotating and co-propagating vortex pairs. The former show a correction to the rotation rate and, in general, to the centre and radius of rotation, while the latter recover the known result that the steady propagation velocity is unchanged. For unsteady configurations, the vortex solution matches to a far field in which acoustic waves are radiated. This article is part of the theme issue ‘Mathematical problems in physical fluid dynamics (part 2)’. 

Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CANDELA. It consists of a powerful retrieval system and a novel two-step generation model, where a text planning decoder first decides on the main talking points and a proper language style for each sentence, then a content realization decoder reflects the decisions and constructs an informative paragraph-level argument. Furthermore, our generation model is empowered by a retrieval system indexed with 12 million articles collected from Wikipedia and popular English news media, which provides access to high-quality content with diversity. Automatic evaluation on a large-scale dataset collected from Reddit shows that our model yields significantly higher BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial comparisons. Human evaluation further indicates that our system arguments are more appropriate for refutation and richer in content. 

A search for proton decay into $e^{+}/{\mu }^{+}$and a $\eta$meson has been performed using data from a $0.373\mathrm{Mton}·\mathrm{year}$exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$interaction cross section, resulting in a factor of 2 reduction in uncertainties from this source and $\sim 10\%$increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times {10}^{34}\mathrm{years}$for $p\to e^{+}\eta$and $7.3\times {10}^{33}\mathrm{years}$for $p\to {\mu }^{+}\eta$at the 90% CL were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date. Published by the American Physical Society2024