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

Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions

In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.Comment: Best Paper Award at CSCW 201

Elucidating the Interactive Roles of Glia in Alzheimer's Disease Using Established and Newly Developed Experimental Models

Alzheimer's disease (AD) is an irreversible neurodegenerative illness and the exact etiology of the disease remains unknown. It is characterized by long preclinical and prodromal phases with pathological features including an accumulation of amyloid-beta (Aβ) peptides into extracellular Aβ plaques in the brain parenchyma and the formation of intracellular neurofibrillary tangles (NFTs) within neurons as a result of abnormal phosphorylation of microtubule-associated tau proteins. In addition, prominent activation of innate immune cells is also observed and/or followed by marked neuroinflammation. While such neuroinflammatory responses may function in a neuroprotective manner by clearing neurotoxic factors, they can also be neurotoxic by contributing to neurodegeneration via elevated levels of proinflammatory mediators and oxidative stress, and altered levels of neurotransmitters, that underlie pathological symptoms including synaptic and cognitive impairment, neuronal death, reduced memory, and neocortex and hippocampus malfunctions. Glial cells, particularly activated microglia and reactive astrocytes, appear to play critical and interactive roles in such dichotomous responses. Accumulating evidences clearly point to their critical involvement in the prevention, initiation, and progression, of neurodegenerative diseases, including AD. Here, we review recent findings on the roles of astrocyte-microglial interactions in neurodegeneration in the context of AD and discuss newly developed in vitro and in vivo experimental models that will enable more detailed analysis of glial interplay. An increased understanding of the roles of glia and the development of new exploratory tools are likely to be crucial for the development of new interventions for early stage AD prevention and cures

Prevalence and Prediction of Obstructive Coronary Artery Disease in Patients Referred for Valvular Heart Surgery

Current guidelines recommend a coronary evaluation before valvular heart surgery (VHS). Diagnostic coronary angiography is recommended in patients with known coronary artery disease (CAD) and those with high pretest probability of CAD. In patients with low or intermediate pretest probability of CAD, the guidelines recommend coronary computed tomographic angiography. However, there are no tools available to objectively assess a patient’s risk for obstructive CAD before VHS. To address this deficit, 5,360 patients without histories of CAD who underwent diagnostic coronary angiography as part of preoperative evaluation for VHS were identified. Obstructive CAD was defined as ≥50% stenosis in ≥1 artery. Of the patients assessed, 1,035 (19.3%) were found to have obstructive CAD. Through multivariate analysis, age, gender, diabetes, renal dysfunction, hyperlipidemia, and a family history of premature CAD were found to be associated with the presence of obstructive CAD (p \u3c0.001 for all). After adjustment, the specific dysfunctional valve was not associated with the presence of obstructive CAD. Patients were then randomly split into derivation and validation cohorts. Within the derivation cohort, using only age, gender, and the presence or absence of risk factors, a model was constructed to predict the risk for obstructive CAD (C statistic 0.766, 95% confidence interval 0.750 to 0.783). The risk prediction model performed well within the validation cohort (C statistic 0.767, 95% confidence interval 0.751 to 0.784, optimism 0.004). The bias-corrected C statistic for the model was 0.765 (95% confidence interval 0.748 to 0.782). In conclusion, this novel risk prediction tool can be used to objectively risk-stratify patients who undergo preoperative evaluation before VHS and to facilitate appropriate triage to computed tomographic angiography or diagnostic coronary angiography

Welcome to Molecular Brain

We are delighted to announce the arrival of a brand new journal dedicated to the ever-expanding field of neuroscience. Molecular Brain is a peer-reviewed, open-access online journal that aims at publishing high quality articles as rapidly as possible. The journal will cover a broad spectrum of neuroscience ranging from molecular/cellular to behavioral/cognitive neuroscience and from basic to clinical research. Molecular Brain will publish not only research articles, but also methodology articles, editorials, reviews, and short reports. It will be a premier platform for neuroscientists to exchange their ideas with researchers from around the world to help improve our understanding of the molecular mechanisms of the brain and mind

Kink scaling functions in 2D non--integrable quantum field theories

We determine the semiclassical energy levels for the \phi^4 field theory in the broken symmetry phase on a 2D cylindrical geometry with antiperiodic boundary conditions by quantizing the appropriate finite--volume kink solutions. The analytic form of the kink scaling functions for arbitrary size of the system allows us to describe the flow between the twisted sector of c=1 CFT in the UV region and the massive particles in the IR limit. Kink-creating operators are shown to correspond in the UV limit to disorder fields of the c=1 CFT. The problem of the finite--volume spectrum for generic 2D Landau--Ginzburg models is also discussed.Comment: 30 pages, 10 figure

Characterization and Magnetic Properties of Core/Shell Structured Fe/Au Nanoparticles

Au-coated Fe nanoparticles have been prepared by using a reverse micelle method through reduction of an aqueous solution. Characterizations have been carried out over time to probe the oxidation of Fe. Immediately after synthesis, the samples exhibit metallic conduction and a negative magnetoresistance, consistent with the presence of α-Fe. The temperature dependence of magnetization displays a maximum at a blocking temperature of around 150 K. After a period of 1 month, the samples exhibit insulating behavior, indicating the oxidation of the Fe core. Mössbauer spectroscopy indicates the presence of an α-Fe component and a broad distribution of local environments

Effects of meta-human characteristics on user acceptance: from the perspective of Uncanny Valley theory

Despite the potential of meta-humans in the virtual space, research on how consumers react to meta-humans is scarce. This study investigates the effects of meta-human characteristics on user acceptance. 280 responses from the online survey were analysed using structural equation modelling. Findings revealed that meta-humans outshine digital humans in terms of performance and user acceptance. Users encountering digital humans are affected by the uncanny valley in terms of appearance and function. However, users encountering meta-humans are affected only in terms of function. Social presence and perceived novelty are additional factors affecting user acceptance. Theoretically, this study contributes to the literature by confirming the existence of the uncanny valley effect in meta-humans and by expanding human likeness to appearance and behaviour. Although meta-humans have surpassed the uncanny valley in appearance, they still lack familiarity in terms of behaviour. Practically, meta-humans and meta-human modelling tools have been found to surpass existing digital human technology both in performance and user acceptance. However, behavioural human likeness must continue to be developed in order to further increase user acceptance. Furthermore, familiarity does not directly affect user acceptance but mediates satisfaction. As user acceptance follows satisfaction, marketers should investigate user satisfaction rather than improving human likeness