Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory

Dataset distillation methods aim to compress a large dataset into a small set of synthetic samples, such that when being trained on, competitive performances can be achieved compared to regular training on the entire dataset. Among recently proposed methods, Matching Training Trajectories (MTT) achieves state-of-the-art performance on CIFAR-10/100, while having difficulty scaling to ImageNet-1k dataset due to the large memory requirement when performing unrolled gradient computation through back-propagation. Surprisingly, we show that there exists a procedure to exactly calculate the gradient of the trajectory matching loss with constant GPU memory requirement (irrelevant to the number of unrolled steps). With this finding, the proposed memory-efficient trajectory matching method can easily scale to ImageNet-1K with 6x memory reduction while introducing only around 2% runtime overhead than original MTT. Further, we find that assigning soft labels for synthetic images is crucial for the performance when scaling to larger number of categories (e.g., 1,000) and propose a novel soft label version of trajectory matching that facilities better aligning of model training trajectories on large datasets. The proposed algorithm not only surpasses previous SOTA on ImageNet-1K under extremely low IPCs (Images Per Class), but also for the first time enables us to scale up to 50 IPCs on ImageNet-1K. Our method (TESLA) achieves 27.9% testing accuracy, a remarkable +18.2% margin over prior arts.Comment: ICLR 2023 submission link: https://openreview.net/forum?id=dN70O8pmW

GAME-UP: Game-Aware Mode Enumeration and Understanding for Trajectory Prediction

Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictors do not account for the interactive nature of multi-agent predictions, they may miss these important interaction outcomes. In this paper, we propose GAME-UP, a framework for trajectory prediction that leverages game-theoretic inverse reinforcement learning to improve coverage of multi-modal predictions. We use a training-time game-theoretic numerical analysis as an auxiliary loss resulting in improved coverage and accuracy without presuming a taxonomy of actions for the agents. We demonstrate our approach on the interactive subset of Waymo Open Motion Dataset, including three subsets involving scenarios with high interaction complexity. Experiment results show that our predictor produces accurate predictions while covering twice as many possible interactions versus a baseline model.Comment: 10 pages, 6 figure

Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions

Nitrous oxide (N_2O) is a potent long‐lived greenhouse gas (GHG) and the strongest current emissions of global anthropogenic stratospheric ozone depletion weighted by its ozone depletion potential. In California, N_2O is the third largest contributor to the state's anthropogenic GHG emission inventory, though no study has quantified its statewide annual emissions through top‐down inverse modeling. Here we present the first annual (2013–2014) statewide top‐down estimates of anthropogenic N_2O emissions. Utilizing continuous N_2O observations from six sites across California in a hierarchical Bayesian inversion, we estimate that annual anthropogenic emissions are 1.5–2.5 times (at 95% confidence) the state inventory (41 Gg N_2O in 2014). Without mitigation, this estimate represents 4–7% of total GHG emissions assuming that other reported GHG emissions are reasonably correct. This suggests that control of N_2O could be an important component in meeting California's emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO_2 equivalent) by 2030 and 2050, respectively. Our seasonality analysis suggests that emissions are similar across seasons within posterior uncertainties. Future work is needed to provide source attribution for subregions and further characterization of seasonal variability

Cell-specific transcriptome changes in the hypothalamic arcuate nucleus in a mouse deoxycorticosterone acetate-salt model of hypertension

A common preclinical model of hypertension characterized by low circulating renin is the “deoxycorticosterone acetate (DOCA)-salt” model, which influences blood pressure and metabolism through mechanisms involving the angiotensin II type 1 receptor (AT1R) in the brain. More specifically, AT1R within Agouti-related peptide (AgRP) neurons of the arcuate nucleus of the hypothalamus (ARC) has been implicated in selected effects of DOCA-salt. In addition, microglia have been implicated in the cerebrovascular effects of DOCA-salt and angiotensin II. To characterize DOCA-salt effects upon the transcriptomes of individual cell types within the ARC, we used single-nucleus RNA sequencing (snRNAseq) to examine this region from male C57BL/6J mice that underwent sham or DOCA-salt treatment. Thirty-two unique primary cell type clusters were identified. Sub-clustering of neuropeptide-related clusters resulted in identification of three distinct AgRP subclusters. DOCA-salt treatment caused subtype-specific changes in gene expression patterns associated with AT1R and G protein signaling, neurotransmitter uptake, synapse functions, and hormone secretion. In addition, two primary cell type clusters were identified as resting versus activated microglia, and multiple distinct subtypes of activated microglia were suggested by sub-cluster analysis. While DOCA-salt had no overall effect on total microglial density within the ARC, DOCA-salt appeared to cause a redistribution of the relative abundance of activated microglia subtypes. These data provide novel insights into cell-specific molecular changes occurring within the ARC during DOCA-salt treatment, and prompt increased investigation of the physiological and pathophysiological significance of distinct subtypes of neuronal and glial cell types

The pyruvate decarboxylase activity of IpdC is a limitation for isobutanol production by Klebsiella pneumoniae

BACKGROUND: Klebsiella pneumoniae contains an endogenous isobutanol synthesis pathway. The ipdC gene annotated as an indole-3-pyruvate decarboxylase (Kp-IpdC), was identified to catalyze the formation of isobutyraldehyde from 2-ketoisovalerate. RESULTS: Compared with 2-ketoisovalerate decarboxylase from Lactococcus lactis (KivD), a decarboxylase commonly used in artificial isobutanol synthesis pathways, Kp-IpdC has an 2.8-fold lower Km for 2-ketoisovalerate, leading to higher isobutanol production without induction. However, expression of ipdC by IPTG induction resulted in a low isobutanol titer. In vitro enzymatic reactions showed that Kp-IpdC exhibits promiscuous pyruvate decarboxylase activity, which adversely consume the available pyruvate precursor for isobutanol synthesis. To address this, we have engineered Kp-IpdC to reduce pyruvate decarboxylase activity. From computational modeling, we identified 10 amino acid residues surrounding the active site for mutagenesis. Ten designs consisting of eight single-point mutants and two double-point mutants were selected for exploration. Mutants L546W and T290L that showed only 5.1% and 22.1% of catalytic efficiency on pyruvate compared to Kp-IpdC, were then expressed in K. pneumoniae for in vivo testing. Isobutanol production by K. pneumoniae T290L was 25% higher than that of the control strain, and a final titer of 5.5 g/L isobutanol was obtained with a substrate conversion ratio of 0.16 mol/mol glucose. CONCLUSIONS: This research provides a new way to improve the efficiency of the biological route of isobutanol production

A Statistical Model for Estimating Maternal-Zygotic Interactions and Parent-of-Origin Effects of QTLs for Seed Development

Proper development of a seed requires coordinated exchanges of signals among the three components that develop side by side in the seed. One of these is the maternal integument that encloses the other two zygotic components, i.e., the diploid embryo and its nurturing annex, the triploid endosperm. Although the formation of the embryo and endosperm contains the contributions of both maternal and paternal parents, maternally and paternally derived alleles may be expressed differently, leading to a so-called parent-of-origin or imprinting effect. Currently, the nature of how genes from the maternal and zygotic genomes interact to affect seed development remains largely unknown. Here, we present a novel statistical model for estimating the main and interaction effects of quantitative trait loci (QTLs) that are derived from different genomes and further testing the imprinting effects of these QTLs on seed development. The experimental design used is based on reciprocal backcrosses toward both parents, so that the inheritance of parent-specific alleles could be traced. The computing model and algorithm were implemented with the maximum likelihood approach. The new strategy presented was applied to study the mode of inheritance for QTLs that control endoreduplication traits in maize endosperm. Monte Carlo simulation studies were performed to investigate the statistical properties of the new model with the data simulated under different imprinting degrees. The false positive rate of imprinting QTL discovery by the model was examined by analyzing the simulated data that contain no imprinting QTL. The reciprocal design and a series of analytical and testing strategies proposed provide a standard procedure for genomic mapping of QTLs involved in the genetic control of complex seed development traits in flowering plants

nIFTy galaxy cluster simulations - IV. Quantifying the influence of baryons on halo properties

Building on the initial results of the nIFTy simulated galaxy cluster comparison, we compare and contrast the impact of baryonic physics with a single massive galaxy cluster, run with 11 state-of-the-art codes, spanning adaptive mesh, moving mesh, classic and modern smoothed particle hydrodynamics (SPH) approaches. For each code represented we have a dark-matteronly (DM) and non-radiative (NR) version of the cluster, as well as a full physics (FP) version for a subset of the codes. We compare both radial mass and kinematic profiles, as well as global measures of the cluster (e.g. concentration, spin, shape), in the NR and FP runs with that in the DM runs. Our analysis reveals good consistency (<≈ 20 per cent) between global properties of the cluster predicted by different codes when integrated quantities are measured within the virial radius R200. However, we see larger differences for quantities within R2500, especially in the FP runs. The radial profiles reveal a diversity, especially in the cluster centre, between the NR runs, which can be understood straightforwardly from the division of codes into classic SPH and non-classic SPH (including the modern SPH, adaptive and moving mesh codes); and between the FP runs, which can also be understood broadly from the division of codes into those that include active galactic nucleus feedback and those that do not. The variation with respect to the median is much larger in the FP runs with different baryonic physics prescriptions than in the NR runs with different hydrodynamics solvers

Single Feature Polymorphism Discovery in Rice

The discovery of nucleotide diversity captured as single feature polymorphism (SFP) by using the expression array is a high-throughput and effective method in detecting genome-wide polymorphism. The efficacy of such method was tested in rice, and the results presented in the paper indicate high sensitivity in predicting SFP. The sensitivity of polymorphism detection was further demonstrated by the fact that no biasness was observed in detecting SFP with either single or multiple nucleotide polymorphisms. The high density SFP data that can be generated quite effectively by the current method has promise for high resolution genetic mapping studies, as physical location of features are well-defined on rice genome