Vector-based Representation is the Key: A Study on Disentanglement and Compositional Generalization

Recognizing elementary underlying concepts from observations (disentanglement) and generating novel combinations of these concepts (compositional generalization) are fundamental abilities for humans to support rapid knowledge learning and generalize to new tasks, with which the deep learning models struggle. Towards human-like intelligence, various works on disentangled representation learning have been proposed, and recently some studies on compositional generalization have been presented. However, few works study the relationship between disentanglement and compositional generalization, and the observed results are inconsistent. In this paper, we study several typical disentangled representation learning works in terms of both disentanglement and compositional generalization abilities, and we provide an important insight: vector-based representation (using a vector instead of a scalar to represent a concept) is the key to empower both good disentanglement and strong compositional generalization. This insight also resonates the neuroscience research that the brain encodes information in neuron population activity rather than individual neurons. Motivated by this observation, we further propose a method to reform the scalar-based disentanglement works ($\beta$-TCVAE and FactorVAE) to be vector-based to increase both capabilities. We investigate the impact of the dimensions of vector-based representation and one important question: whether better disentanglement indicates higher compositional generalization. In summary, our study demonstrates that it is possible to achieve both good concept recognition and novel concept composition, contributing an important step towards human-like intelligence.Comment: Preprin

Post-stroke experiences and health information needs among Chinese elderly ischemic stroke survivors in the internet environment: a qualitative study

BackgroundElderly stroke survivors are encouraged to receive appropriate health information to prevent recurrences. After discharge, older patients seek health information in everyday contexts, examining aspects that facilitate or impair healthy behavior.ObjectivesTo explore the experiences of older stroke patients when searching for health information, focusing on search methods, identification of health information, and difficulties faced during the search process.MethodsUsing the qualitative descriptive methodology, semi-structured interviews were conducted with fifteen participants.ResultsParticipants associated the health information they sought with concerns about future life prospects triggered by perceived intrusive changes in their living conditions. Based on the participants’ descriptions, four themes were refined: participants’ motivation to engage in health information acquisition behavior, basic patterns of health information search, source preferences for health information, and difficulties and obstacles in health information search, and two search motivation subthemes, two search pattern subthemes, four search pathway subthemes, and four search difficulty subthemes were further refined.ConclusionOlder stroke patients face significant challenges in searching for health information online. Healthcare professionals should assess survivors’ health information-seeking skills, develop training programs, provide multichannel online access to health resources, and promote secondary prevention for patients by improving survivors’ health behaviors and self-efficacy

EST analysis of gene expression in the tentacle of Cyanea capillata

AbstractJellyfish, Cyanea capillata, has an important position in head patterning and ion channel evolution, in addition to containing a rich source of toxins. In the present study, 2153 expressed sequence tags (ESTs) from the tentacle cDNA library of C. capillata were analyzed. The initial ESTs consisted of 198 clusters and 818 singletons, which revealed approximately 1016 unique genes in the data set. Among these sequences, we identified several genes related to head and foot patterning, voltage-dependent anion channel gene and genes related to biological activities of venom. Five kinds of proteinase inhibitor genes were found in jellyfish for the first time, and some of them were highly expressed with unknown functions

First identification of long non-coding RNAs in fungal parasite Nosema ceranae

International audienceAbstractNosema ceranae is a unicellular fungal parasite of honey bees and causes huge losses for apiculture. Until present, no study on N. ceranae long non-coding RNAs (lncRNAs) was documented. Here, we sequenced purified spores of N. ceranae using strand-specific library construction and high-throughput RNA sequencing technologies. In total, 83 novel lncRNAs were predicted from N. ceranae spore samples, including lncRNAs, long intergenic non-coding RNAs (lincRNAs), and sense lncRNAs. Moreover, these lncRNAs share similar characteristics with those identified in mammals and plants, such as shorter length and fewer exon number and transcript isoforms than protein-coding genes. Finally, the expression of 12 lncRNAs was confirmed with RT-PCR, confirming their true existence. To our knowledge, this is the first evidence of lncRNAs produced by a microsporidia species, offering novel insights into basic biology such as regulation of gene expression of this widespread taxonomic group

Bi-Functional Silica Nanoparticles Doped with Iron Oxide and CdTe Prepared by a Facile Method

Cadmium telluride (CdTe) and iron oxide nanoparticles doped silica nanospheres were prepared by a multistep method. Iron oxide nanoparticles were first coated with silica and then modified with amino group. Thereafter, CdTe nanoparticles were assembled on the particle surfaces by their strong interaction with amino group. Finally, an outer silica shell was deposited. The final products were characterized by X-ray powder diffraction, transmission electron microscopy, vibration sample magnetometer, photoluminescence spectra, Fourier transform infrared spectra (FT-IR), and fluorescent microscopy. The characterization results showed that the final nanomaterial possessed a saturation magnetization of about 5.8 emu g−1and an emission peak at 588 nm when the excitation wavelength fixed at 380 nm

Clinical Characteristics of 26 Human Cases of Highly Pathogenic Avian Influenza A (H5N1) Virus Infection in China

BACKGROUND: While human cases of highly pathogenic avian influenza A (H5N1) virus infection continue to increase globally, available clinical data on H5N1 cases are limited. We conducted a retrospective study of 26 confirmed human H5N1 cases identified through surveillance in China from October 2005 through April 2008. METHODOLOGY/PRINCIPAL FINDINGS: Data were collected from hospital medical records of H5N1 cases and analyzed. The median age was 29 years (range 6-62) and 58% were female. Many H5N1 cases reported fever (92%) and cough (58%) at illness onset, and had lower respiratory findings of tachypnea and dyspnea at admission. All cases progressed rapidly to bilateral pneumonia. Clinical complications included acute respiratory distress syndrome (ARDS, 81%), cardiac failure (50%), elevated aminotransaminases (43%), and renal dysfunction (17%). Fatal cases had a lower median nadir platelet count (64.5 x 10(9) cells/L vs 93.0 x 10(9) cells/L, p = 0.02), higher median peak lactic dehydrogenase (LDH) level (1982.5 U/L vs 1230.0 U/L, p = 0.001), higher percentage of ARDS (94% [n = 16] vs 56% [n = 5], p = 0.034) and more frequent cardiac failure (71% [n = 12] vs 11% [n = 1], p = 0.011) than nonfatal cases. A higher proportion of patients who received antiviral drugs survived compared to untreated (67% [8/12] vs 7% [1/14], p = 0.003). CONCLUSIONS/SIGNIFICANCE: The clinical course of Chinese H5N1 cases is characterized by fever and cough initially, with rapid progression to lower respiratory disease. Decreased platelet count, elevated LDH level, ARDS and cardiac failure were associated with fatal outcomes. Clinical management of H5N1 cases should be standardized in China to include early antiviral treatment for suspected H5N1 cases

Mask-based Latent Reconstruction for Reinforcement Learning

For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance. However, in practice, limited experience and high-dimensional input prevent effective representation learning. To address this, motivated by the success of masked modeling in other research fields, we introduce mask-based reconstruction to promote state representation learning in RL. Specifically, we propose a simple yet effective self-supervised method, Mask-based Latent Reconstruction (MLR), to predict the complete state representations in the latent space from the observations with spatially and temporally masked pixels. MLR enables the better use of context information when learning state representations to make them more informative, which facilitates RL agent training. Extensive experiments show that our MLR significantly improves the sample efficiency in RL and outperforms the state-of-the-art sample-efficient RL methods on multiple continuous and discrete control benchmarks. The code will be released soon.Comment: A latent-space masked modeling method for improving RL sample efficienc

Innovative Telerehabilitation Enhanced Care Programme (ITECP) in young and middle-aged patients with haemorrhagic stroke to improve exercise adherence: protocol of a multicentre randomised controlled trial

Introduction Exercise rehabilitation is crucial for promoting the rehabilitation of limb motor function in people who had stroke and is related to a better prognosis. However, the exercise adherence of patients is low, which affects the effect of exercise rehabilitation. This study aims to evaluate the effects of the Innovative Telerehabilitation Enhanced Care Programme (ITECP) on exercise adherence in young and middle-aged patients with haemorrhagic stroke. We hypothesise that patients trained with ITECP will show greater improvement in exercise adherence and muscle strength than patients with routine exercise rehabilitation.Methods and analysis This is a randomised controlled, evaluator-blinded multicentre superiority trial to be implemented at four tertiary grade-A hospitals in eastern, western, northern and central China. Patients in the experimental group will receive ITECP while those in the control group will receive routine exercise rehabilitation. Both groups will receive routine care. The primary outcome measure is exercise adherence, while secondary outcome measures include muscle strength, activities of daily living, exercise self-efficacy, quality of life, rate of exercise-related adverse events and readmission. These will be measured at baseline, predischarge as well as 1 and 3 months postdischarge.Ethics and dissemination The study has obtained ethical approval from the Medical Ethics Committee of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School (2021-381-02). The results will be shared with young and middle-aged patients with haemorrhagic stroke, policy-makers, the general public, as well as academia.Trial registration number Chinese Clinical Trials Registry (ChiCTR 2200066498)

Active Token Mixer

The three existing dominant network families, i.e., CNNs, Transformers and MLPs, differ from each other mainly in the ways of fusing spatial contextual information, leaving designing more effective token-mixing mechanisms at the core of backbone architecture development. In this work, we propose an innovative token-mixer, dubbed Active Token Mixer (ATM), to actively incorporate contextual information from other tokens in the global scope into the given query token. This fundamental operator actively predicts where to capture useful contexts and learns how to fuse the captured contexts with the query token at channel level. In this way, the spatial range of token-mixing can be expanded to a global scope with limited computational complexity, where the way of token-mixing is reformed. We take ATMs as the primary operators and assemble them into a cascade architecture, dubbed ATMNet. Extensive experiments demonstrate that ATMNet is generally applicable and comprehensively surpasses different families of SOTA vision backbones by a clear margin on a broad range of vision tasks, including visual recognition and dense prediction tasks. Code is available at https://github.com/microsoft/ActiveMLP

Generalizing to Unseen Domains: A Survey on Domain Generalization

Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen distributions. Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increasing interests in recent years. Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. Great progress has been made in the area of domain generalization for years. This paper presents the first review of recent advances in this area. First, we provide a formal definition of domain generalization and discuss several related fields. We then thoroughly review the theories related to domain generalization and carefully analyze the theory behind generalization. We categorize recent algorithms into three classes: data manipulation, representation learning, and learning strategy, and present several popular algorithms in detail for each category. Third, we introduce the commonly used datasets, applications, and our open-sourced codebase for fair evaluation. Finally, we summarize existing literature and present some potential research topics for the future.Comment: 19 pages (add more related work as of 12/2021); short version (6 pages) has been accepted by IJCAI-21 survey track; codebase: https://github.com/jindongwang/transferlearning/tree/master/code/DeepD