Predictive Inference with Feature Conformal Prediction

Conformal prediction is a distribution-free technique for establishing valid prediction intervals. Although conventionally people conduct conformal prediction in the output space, this is not the only possibility. In this paper, we propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces by leveraging the inductive bias of deep representation learning. From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions. Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods. Apart from experiments on existing predictive inference benchmarks, we also demonstrate the state-of-the-art performance of the proposed methods on large-scale tasks such as ImageNet classification and Cityscapes image segmentation.The code is available at \url{https://github.com/AlvinWen428/FeatureCP}.Comment: Published as a conference paper at ICLR 202

When do Models Generalize? A Perspective from Data-Algorithm Compatibility

One of the major open problems in machine learning is to characterize generalization in the overparameterized regime, where most traditional generalization bounds become inconsistent (Nagarajan and Kolter, 2019). In many scenarios, their failure can be attributed to obscuring the crucial interplay between the training algorithm and the underlying data distribution. To address this issue, we propose a concept named compatibility, which quantitatively characterizes generalization in a both data-relevant and algorithm-relevant manner. By considering the entire training trajectory and focusing on early-stopping iterates, compatibility exploits the data and the algorithm information and is therefore a more suitable notion for generalization. We validate this by theoretically studying compatibility under the setting of solving overparameterized linear regression with gradient descent. Specifically, we perform a data-dependent trajectory analysis and derive a sufficient condition for compatibility in such a setting. Our theoretical results demonstrate that in the sense of compatibility, generalization holds with significantly weaker restrictions on the problem instance than the previous last iterate analysis

Targeting the complex I and III of mitochondrial electron transport chain as a potentially viable option in liver cancer management

Abstract Liver cancer is one of the most common and lethal types of oncological disease in the world, with limited treatment options. New treatment modalities are desperately needed, but their development is hampered by a lack of insight into the underlying molecular mechanisms of disease. It is clear that metabolic reprogramming in mitochondrial function is intimately linked to the liver cancer process, prompting the possibility to explore mitochondrial biochemistry as a potential therapeutic target. Here we report that depletion of mitochondrial DNA, pharmacologic inhibition of mitochondrial electron transport chain (mETC) complex I/complex III, or genetic of mETC complex I restricts cancer cell growth and clonogenicity in various preclinical models of liver cancer, including cell lines, mouse liver organoids, and murine xenografts. The restriction is linked to the production of reactive oxygen species, apoptosis induction and reduced ATP generation. As a result, our findings suggest that the mETC compartment of mitochondria could be a potential therapeutic target in liver cancer

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions. Multi-modal models are expected to benefit from cross-modal interactions on the basis of ensuring uni-modal feature learning. However, recent supervised multi-modal late-fusion training approaches still suffer from insufficient learning of uni-modal features on each modality. We prove that this phenomenon does hurt the model's generalization ability. To this end, we propose to choose a targeted late-fusion learning method for the given supervised multi-modal task from Uni-Modal Ensemble(UME) and the proposed Uni-Modal Teacher(UMT), according to the distribution of uni-modal and paired features. We demonstrate that, under a simple guiding strategy, we can achieve comparable results to other complex late-fusion or intermediate-fusion methods on various multi-modal datasets, including VGG-Sound, Kinetics-400, UCF101, and ModelNet40

Mapping global prevalence of depression among postpartum women

Postpartum depression (PPD) is the most common psychological condition following childbirth, and may have a detrimental effect on the social and cognitive health of spouses, infants, and children. The aim of this study was to complete a comprehensive overview of the current literature on the global epidemiology of PPD. A total of 565 studies from 80 different countries or regions were included in the final analysis. Postpartum depression was found in 17.22% (95% CI 16.00–18.51) of the world’s population. Meta-regression analysis showed that study size, country or region development, and country or region income were the causes of heterogeneity. Multivariable meta-regression analysis found that study size and country or area development were the most important predictors. Varied prevalence rates were noted in geographic regions with the highest rate found in Southern Africa (39.96%). Of interested was a significantly lower rate of PPD in developed countries or high-income countries or areas. Furthermore, the findings showed that there was a substantial difference in rates of PPD when marital status, educational level, social support, spouse care, violence, gestational age, breast feeding, child mortality, pregnancy plan, financial difficulties, partnership, life stress, smoking, alcohol intake, and living conditions were considered in the pooled estimates. Our results indicated that one out of every five women experiences PPD which is linked to income and geographic development. It is triggered by a variety of causes that necessitate the attention and committed intervention of primary care providers, clinicians, health authorities, and the general population.</p

Direct determination of band-gap renormalization in degenerately doped ultrawide band gap β-Ga_{2}O_{3} semiconductor

Ga2O3 is emerging as a promising wide band-gap semiconductor for high-power electronics and deep ultraviolet optoelectronics. It is highly desirable to dope it with controllable carrier concentrations for different device applications. This work reports a combined photoemission spectroscopy and theoretical calculation study on the electronic structure of Si doped Ga_{2}O_{3} films with carrier concentration varying from 4.6×10^{18} cm^{−3} to 2.6×10^{20} cm^{−3}. Hard x-ray photoelectron spectroscopy was used to directly measure the widening of the band gap as a result of occupation of conduction band and band-gap renormalization associated with many-body interactions. A large band-gap renormalization of 0.3 eV was directly observed in heavily doped Ga_{2}O_{3}. Supplemented with hybrid density functional theory calculations, we demonstrated that the band-gap renormalization results from the decrease in energy of the conduction band edge driven by the mutual electrostatic interaction between added electrons. Moreover, our work reveals that Si is a superior dopant over Ge and Sn, because Si 3s forms a resonant donor state above the conduction band minimum, leaving the host conduction band mostly unperturbed and a high mobility is maintained though the doping level is high. Insights of the present work have significant implications in doping optimization of Ga_{2}O_{3} and realization of optoelectronic devices

Deep UV transparent conductive oxide thin films realized through degenerately doped wide-bandgap gallium oxide

Deep UV transparent thin films have recently attracted considerable attention owing to their potential in UV and organic-based optoelectronics. Here, we report the achievement of a deep UV transparent and highly conductive thin film based on Si-doped Ga_{2}O_{3} (SGO) with high conductivity of 2500 S/cm. The SGO thin films exhibit high transparency over a wide spectrum ranging from visible light to deep UV wavelength and, meanwhile, have a very low work-function of approximately 3.2 eV. A combination of photoemission spectroscopy and theoretical studies reveals that the delocalized conduction band derived from Ga 4s orbitals is responsible for the Ga_{2}O_{3} films’ high conductivity. Furthermore, Si is shown to act as an efficient shallow donor, yielding high mobility up to approximately 60 cm^{2}/Vs. The superior optoelectronic properties of SGO films make it a promising material for use as electrodes in high-power electronics and deep UV and organic-based optoelectronic devices