Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation

Domain Adaptation (DA) is always challenged by the spurious correlation between domain-invariant features (e.g., class identity) and domain-specific features (e.g., environment) that does not generalize to the target domain. Unfortunately, even enriched with additional unsupervised target domains, existing Unsupervised DA (UDA) methods still suffer from it. This is because the source domain supervision only considers the target domain samples as auxiliary data (e.g., by pseudo-labeling), yet the inherent distribution in the target domain -- where the valuable de-correlation clues hide -- is disregarded. We propose to make the U in UDA matter by giving equal status to the two domains. Specifically, we learn an invariant classifier whose prediction is simultaneously consistent with the labels in the source domain and clusters in the target domain, hence the spurious correlation inconsistent in the target domain is removed. We dub our approach "Invariant CONsistency learning" (ICON). Extensive experiments show that ICON achieves the state-of-the-art performance on the classic UDA benchmarks: Office-Home and VisDA-2017, and outperforms all the conventional methods on the challenging WILDS 2.0 benchmark. Codes are in https://github.com/yue-zhongqi/ICON.Comment: Accepted by NeurIPS 202

Comparison of continuous and pulsed labeling amide hydrogen exchange/mass spectrometry for studies of protein dynamics

AbstractIn contrast to the rigid structures portrayed by X-ray diffraction, proteins in solution display constant motion which leads to populations that are momentarily unfolded. To begin to understand protein dynamics, we must have experimental methods for determining rates of folding and unfolding, as well as for identifying structures of folding and unfolding intermediates. Amide hydrogen exchange has become an important tool for such measurements. When urea is used to stabilize unfolded forms of proteins, the refolding rates may become slower than the rates of isotope exchange. In such cases, the intermolecular distribution of deuterium among the entire population of molecules may become bimodal, giving rise to a bimodal distribution of isotope peaks in mass spectra of the protein or its peptic fragments. When the protein is exposed continuously to D2O, the relative intensities of the two envelopes of isotope peaks give an integrated account of populations participating in the folding/unfolding process. However, when the protein is exposed only briefly to D2O, the relative intensities of the two envelopes of isotope peaks give an instantaneous measure of the folded/unfolded populations. Application of these two labeling methods to a large protein, aldolase, is described along with a discussion of specific parameters required to optimize these experiments

Interventional few-shot learning

Ministry of Education, Singapore under its Academic Research Funding Tier 1 and 2; Alibaba Innovative Research (AIR) programm

CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model

With the development of deep generative models, recent years have seen great success of Chinese landscape painting generation. However, few works focus on controllable Chinese landscape painting generation due to the lack of data and limited modeling capabilities. In this work, we propose a controllable Chinese landscape painting generation method named CCLAP, which can generate painting with specific content and style based on Latent Diffusion Model. Specifically, it consists of two cascaded modules, i.e., content generator and style aggregator. The content generator module guarantees the content of generated paintings specific to the input text. While the style aggregator module is to generate paintings of a style corresponding to a reference image. Moreover, a new dataset of Chinese landscape paintings named CLAP is collected for comprehensive evaluation. Both the qualitative and quantitative results demonstrate that our method achieves state-of-the-art performance, especially in artfully-composed and artistic conception. Codes are available at https://github.com/Robin-WZQ/CCLAP.Comment: 8 pages,13 figure

An enhanced fall detection system for elderly person monitoring using consumer home networks

Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities. The proposed system has been deployed in a prototype system as detailed in this paper. From a test group of 30 healthy participants, it was found that the proposed fall detection system can achieve a high detection accuracy of 97.5%, while the sensitivity and specificity are 96.8% and 98.1% respectively. Therefore, this system can reliably be developed and deployed into a consumer product for use as an elderly person monitoring device with high accuracy and a low false positive rate

LncRNA RUNX1-IT1 is downregulated in gastric cancer and suppresses the maturation of miR-20a by binding to its precursor

Background. RUNX1-IT1 has been characterized as a tumor suppressive long non-coding RNA (lncRNA) in several types of cancer but not gastric cancer (GC). This study aimed to explore the role of RUNX1-IT1 in GC. Methods. The expression of RUNX1-IT1, microRNA (miR)-20a precursor and mature miR-20a in GC and healthy tissues donated by GC patients (n=62) were measured by RT-qPCR. Correlation analysis was performed by linear regression. The expression of mature miR-20a and miR-20a precursor in cells with overexpression of RUNX1-IT1 was also determined by RT-qPCR. Cell invasion and migration were evaluated by Transwell assays. Results. RUNX1-IT1 was downregulated in GC. Across GC tissues, RUNX1-IT1 and mature miR-20a were inversely correlated. However, RUNX1-IT1 and miR-20a precursor were not closely correlated. RUNX1- IT1 and miR-20a precursor were predicted to interact with each other, and overexpression of RUNX1-IT1 in GC cells decreased the expression levels of mature miR20a. Transwell assay showed that the enhancing effect of miR-20a on cell invasion and migration was reduced by overexpression of RUNX1-IT1. Conclusions. RUNX1-IT1 may suppress the GC cell movement by inhibiting the maturation of miR-20

Nitrogen and Phosphorus Accumulation in Pasture Soil from Repeated Poultry Litter Application

Poultry litter (PL) is a traditionally inexpensive and effective fertilizer to improve soil quality and agricultural productivity. However, over application to soil has raised concern because excess nutrients in runoff could accelerate the eutrophication of fresh water. In this work, we determined the contents of total phosphorus (P), Mehlich 3 extracted P, total nitrogen (N), ammonium (NH4)-N, and nitrate (NO3)-N, in pasture soils receiving annual poultry litter applications of 0, 2.27, 2.27, 3.63, and 1.36 Mg/ha/ yr, respectively, for 0, 5, 10, 15, and 20 years. Samples were collected from three soil depths (0–20, 20–40, and 40–60 cm) of the Hartsells series (fine-loamy, siliceous, subactive, thermic, Typic Hapludults) on a 3–8% slope in the Sand Mountain region of north Alabama. PL application increased levels of total P, Mehlich-3 extractable P, and total N significantly. However, the change in NH4-N and NO3-N contents by the PL application was not statistically significant. Correlation analysis indicated that the contents of total P, Mehlich 3 extracted P, and total N were more related to cumulative amounts of poultry litter applied than the years of application or annual application rates alone. This observation suggested that N and P from poultry litter accumulated in soil. Predicting the build-up based on the cumulative amounts of PL application, rather than isolated factors (i.e., application year or rate), would improve the accuracy of evaluating long-term impacts of poultry litter application on soil nutrient levels

Invariant Feature Regularization for Fair Face Recognition

Fair face recognition is all about learning invariant feature that generalizes to unseen faces in any demographic group. Unfortunately, face datasets inevitably capture the imbalanced demographic attributes that are ubiquitous in real-world observations, and the model learns biased feature that generalizes poorly in the minority group. We point out that the bias arises due to the confounding demographic attributes, which mislead the model to capture the spurious demographic-specific feature. The confounding effect can only be removed by causal intervention, which requires the confounder annotations. However, such annotations can be prohibitively expensive due to the diversity of the demographic attributes. To tackle this, we propose to generate diverse data partitions iteratively in an unsupervised fashion. Each data partition acts as a self-annotated confounder, enabling our Invariant Feature Regularization (INV-REG) to deconfound. INV-REG is orthogonal to existing methods, and combining INV-REG with two strong baselines (Arcface and CIFP) leads to new state-of-the-art that improves face recognition on a variety of demographic groups. Code is available at https://github.com/PanasonicConnect/InvReg.Comment: Accepted by International Conference on Computer Vision (ICCV) 202