A Quadratic Synchronization Rule for Distributed Deep Learning

In distributed deep learning with data parallelism, synchronizing gradients at each training step can cause a huge communication overhead, especially when many nodes work together to train large models. Local gradient methods, such as Local SGD, address this issue by allowing workers to compute locally for $H$ steps without synchronizing with others, hence reducing communication frequency. While $H$ has been viewed as a hyperparameter to trade optimization efficiency for communication cost, recent research indicates that setting a proper $H$ value can lead to generalization improvement. Yet, selecting a proper $H$ is elusive. This work proposes a theory-grounded method for determining $H$, named the Quadratic Synchronization Rule (QSR), which recommends dynamically setting $H$ in proportion to $\frac{1}{\eta^2}$ as the learning rate $\eta$ decays over time. Extensive ImageNet experiments on ResNet and ViT show that local gradient methods with QSR consistently improve the test accuracy over other synchronization strategies. Compared with the standard data parallel training, QSR enables Local AdamW on ViT-B to cut the training time on 16 or 64 GPUs down from 26.7 to 20.2 hours or from 8.6 to 5.5 hours and, at the same time, achieves $1.16\%$ or $0.84\%$ higher top-1 validation accuracy

Assessing the influence of visual-taste congruency on perceived sweetness and product liking in immersive VR

This study was designed to assess whether the combined effect of taste-congruent and incongruent extrinsic visual cues presented in virtual reality (VR) influences the perception of sweetness and product liking. Three VR environments (sweet-congruent, sweet-incongruent, and neutral) were created based on the evidence in existing literature. Participants tasted the same beverage in three VR environments and evaluated the environment and beverage liking, as well as perceived taste intensity (sweetness, sourness, and bitterness), congruency, comfort, and environment vividness. Frontal EEG alpha asymmetry (FAA) was also recorded as a complementary physiological measurement of overall liking. The results showed that the perceived sweetness of the beverage was significantly elevated in a sweet-congruent environment versus the other environments. Visual-taste congruency did not seem to have an effect on beverage liking and overall liking, whereas an increase in environment liking was found in the incongruent environment versus the other environments. These findings confirmed the significant influence of taste-specific visual cues on flavour perception, while the successful use of VR in the study provided insight into future applications of taste-specific VR environment in the modulation of flavour perception and sugar reduction

Nitro-compounds and GHG exhaust emissions of a pilot diesel-ignited ammonia dual-fuel engine under various operating conditions

In the transportation sector, ammonia used as a power source plays a significant role in the scenario of carbon neutralization. However, the engine-out emissions correlations of ammonia-diesel dual-fuel (DF) engines are still unclear, especially the nitro-compounds of great concern and GHG. In this study, the engine-out emissions are evaluated by using a four-cylinder ammonia/diesel DF engine. Various operating conditions consisting of ammonia energy ratio (AER), engine load, and speed were carried out. Unburned NH3 increases with raising ammonia content but decreases with increasing engine load and speed. The NO+NO2 tendency shows a non-linearity trend with increasing ammonia content, while a trade-off correlation is linked to N2O. The N2O emission of ammonia engine significantly weakens the beneficial effect of GHG reduction, the 30% and 50% decarbonization targets need at least 40% and 60% ammonia energy without regard to N2O effect, while at least 65% and 80% ammonia energy respectively with considering N2O. N2O presents a parabolic-like tendency with AERs. Advanced pilot-diesel injection timing helps to reduce both NH3 and N2O, but this effect becomes insignificant as the AER is less than 0.4. A combustion strategy of the rapid heat release and ammonia-governed heat release respectively are revealed

PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking

Planar object tracking is a critical computer vision problem and has drawn increasing interest owing to its key roles in robotics, augmented reality, etc. Despite rapid progress, its further development, especially in the deep learning era, is largely hindered due to the lack of large-scale challenging benchmarks. Addressing this, we introduce PlanarTrack, a large-scale challenging planar tracking benchmark. Specifically, PlanarTrack consists of 1,000 videos with more than 490K images. All these videos are collected in complex unconstrained scenarios from the wild, which makes PlanarTrack, compared with existing benchmarks, more challenging but realistic for real-world applications. To ensure the high-quality annotation, each frame in PlanarTrack is manually labeled using four corners with multiple-round careful inspection and refinement. To our best knowledge, PlanarTrack, to date, is the largest and most challenging dataset dedicated to planar object tracking. In order to analyze the proposed PlanarTrack, we evaluate 10 planar trackers and conduct comprehensive comparisons and in-depth analysis. Our results, not surprisingly, demonstrate that current top-performing planar trackers degenerate significantly on the challenging PlanarTrack and more efforts are needed to improve planar tracking in the future. In addition, we further derive a variant named PlanarTrack$_{\mathbf{BB}}$ for generic object tracking from PlanarTrack. Our evaluation of 10 excellent generic trackers on PlanarTrack$_{\mathrm{BB}}$ manifests that, surprisingly, PlanarTrack$_{\mathrm{BB}}$ is even more challenging than several popular generic tracking benchmarks and more attention should be paid to handle such planar objects, though they are rigid. All benchmarks and evaluations will be released at the project webpage.Comment: Tech. Repor

Band Structure Engineering of Interfacial Semiconductors Based on Atomically Thin Lead Iodide Crystals

To explore new constituents in two-dimensional materials and to combine their best in van der Waals heterostructures, are in great demand as being unique platform to discover new physical phenomena and to design novel functionalities in interface-based devices. Herein, PbI2 crystals as thin as few-layers are first synthesized, particularly through a facile low-temperature solution approach with the crystals of large size, regular shape, different thicknesses and high-yields. As a prototypical demonstration of flexible band engineering of PbI2-based interfacial semiconductors, these PbI2 crystals are subsequently assembled with several transition metal dichalcogenide monolayers. The photoluminescence of MoS2 is strongly enhanced in MoS2/PbI2 stacks, while a dramatic photoluminescence quenching of WS2 and WSe2 is revealed in WS2/PbI2 and WSe2/PbI2 stacks. This is attributed to the effective heterojunction formation between PbI2 and these monolayers, but type I band alignment in MoS2/PbI2 stacks where fast-transferred charge carriers accumulate in MoS2 with high emission efficiency, and type II in WS2/PbI2 and WSe2/PbI2 stacks with separated electrons and holes suitable for light harvesting. Our results demonstrate that MoS2, WS2, WSe2 monolayers with very similar electronic structures themselves, show completely distinct light-matter interactions when interfacing with PbI2, providing unprecedent capabilities to engineer the device performance of two-dimensional heterostructures.Comment: 36 pages, 5 figure

Astrocytic p75NTR expression provoked by ischemic stroke exacerbates the blood-brain barrier disruption

The disruption of the blood–brain barrier (BBB) plays a critical role in the pathology of ischemic stroke. p75 neurotrophin receptor (p75NTR) contributes to the disruption of the blood-retinal barrier in retinal ischemia. However, whether p75NTR influences the BBB permeability after acute cerebral ischemia remains unknown. The present study investigated the role and underlying mechanism of p75NTR on BBB integrity in an ischemic stroke mouse model, middle cerebral artery occlusion (MCAO). After 24 h of MCAO, astrocytes and endothelial cells in the infarct-affected brain area up-regulated p75NTR. Genetic p75NTR knockdown (p75NTR+/ ) or pharmacological inhibition of p75NTR using LM11A-31, a selective inhibitor of p75NTR, both attenuated brain damage and BBB leakage in MCAO mice. Astrocyte-specific conditional knockdown of p75NTR mediated with an adeno-associated virus significantly ameliorated BBB disruption and brain tissue damage, as well as the neurological functions after stroke. Further molecular biological examinations indicated that astrocytic p75NTR activated NF-κB and HIF-1α signals, which upregulated the expression of MMP-9 and vascular endothelial growth factor (VEGF), subsequently leading to tight junction degradation after ischemia. As a result, increased leukocyte infiltration and microglia activation exacerbated brain injury after stroke. Overall, our results provide novel insight into the role of astrocytic p75NTR in BBB disruption after acute cerebral ischemia. The p75NTR may therefore be a potential therapeutic target for the treatment of ischemic stroke

Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation

Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem with machine learning and fragmentation-based methods, however the two approaches fail to give a satisfactory description of long-range and many-body interactions, respectively. Inspired by fragmentation-based methods, we propose the Long-Short-Range Message-Passing (LSR-MP) framework as a generalization of the existing equivariant graph neural networks (EGNNs) with the intent to incorporate long-range interactions efficiently and effectively. We apply the LSR-MP framework to the recently proposed ViSNet and demonstrate the state-of-the-art results with up to $40\%$ error reduction for molecules in MD22 and Chignolin datasets. Consistent improvements to various EGNNs will also be discussed to illustrate the general applicability and robustness of our LSR-MP framework

Visit-to-visit variability in triglyceride-glucose index and diabetes:A 9-year prospective study in the Kailuan Study

Instruction/Aims: It is unknown whether variability in the triglyceride-glucose index (TyG-index) is associated with the risk of diabetes. Here, we sought to characterize the relationship between TyG-index variability and incident diabetes. Methods: We performed a prospective study of 48,013 participants in the Kailuan Study who did not have diabetes. The TyG-index was calculated as ln [triglyceride (TG, mg/dL) concentration × fasting blood glucose concentration (FBG, mg/dL)/2]. The TyG-index variability was assessed using the standard deviation (SD) of three TyG-index values that were calculated during 2006/07, 2008/09, and 2010/11. We used the Cox proportional hazard models to analyze the effect of TyG-index variability on incident diabetes. Results: A total of 4,055 participants were newly diagnosed with diabetes during the study period of 8.95 years (95% confidence interval (CI) 8.48–9.29 years). After adjustment for confounding factors, participants in the highest and second-highest quartiles had significantly higher risks of new-onset diabetes versus the lowest quartile, with hazard ratios (95% CIs) of 1.18 (1.08–1.29) and 1.13 (1.03–1.24), respectively (P trend< 0.05). These higher risks remained after further adjustment for the baseline TyG-index. Conclusions: A substantial fluctuation in TyG-index is associated with a higher risk of diabetes in the Chinese population, implying that it is important to maintain a normal and consistent TyG-index