Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics

Errors in labels obtained via human annotation adversely affect a model's performance. Existing approaches propose ways to mitigate the effect of label error on a model's downstream accuracy, yet little is known about its impact on a model's disparity metrics. Here we study the effect of label error on a model's disparity metrics. We empirically characterize how varying levels of label error, in both training and test data, affect these disparity metrics. We find that group calibration and other metrics are sensitive to train-time and test-time label error -- particularly for minority groups. This disparate effect persists even for models trained with noise-aware algorithms. To mitigate the impact of training-time label error, we present an approach to estimate the influence of a training input's label on a model's group disparity metric. We empirically assess the proposed approach on a variety of datasets and find significant improvement, compared to alternative approaches, in identifying training inputs that improve a model's disparity metric. We complement the approach with an automatic relabel-and-finetune scheme that produces updated models with, provably, improved group calibration error.
Learning with Logical Constraints but without Shortcut Satisfaction

Recent studies in neuro-symbolic learning have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the knowledge. In this paper, we present a new framework for learning with logical constraints. Specifically, we address the shortcut satisfaction issue by introducing dual variables for logical connectives, encoding how the constraint is satisfied. We further propose a variational framework where the encoded logical constraint is expressed as a distributional loss that is compatible with the model's original training loss. The theoretical analysis shows that the proposed approach bears salient properties, and the experimental evaluations demonstrate its superior performance in both model generalizability and constraint satisfaction.
HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork

Fast and accurate predictions for complex physical dynamics are a significant challenge across various applications. Real-time prediction on resource-constrained hardware is even more crucial in real-world problems. The deep operator network (DeepONet) has recently been proposed as a framework for learning nonlinear mappings between function spaces. However, the DeepONet requires many parameters and has a high computational cost when learning operators, particularly those with complex (discontinuous or non-smooth) target functions. This study proposes HyperDeepONet, which uses the expressive power of the hypernetwork to enable the learning of a complex operator with a smaller set of parameters. The DeepONet and its variant models can be thought of as a method of injecting the input function information into the target function. From this perspective, these models can be viewed as a particular case of HyperDeepONet. We analyze the complexity of DeepONet and conclude that HyperDeepONet needs relatively lower complexity to obtain the desired accuracy for operator learning. HyperDeepONet successfully learned various operators with fewer computational resources compared to other benchmarks.
Optimal Scheduling of Bevacizumab and Pemetrexed/Cisplatin Dosing in Non‐Small Cell Lung Cancer

Bevacizumab-pemetrexed/cisplatin (BEV-PEM/CIS) is a first line therapeutic for advanced non-squamous non-small cell lung cancer (NSCLC). Bevacizumab potentiates PEM/CIS cytotoxicity by inducing transient tumor vasculature normalization. BEV-PEM/CIS has a narrow therapeutic window. Therefore, it is an attractive target for administration schedule optimization. The present study leverages our previous work on BEV-PEM/CIS pharmacodynamic modeling in NSCLC-bearing mice to estimate the optimal gap in the scheduling of sequential BEV-PEM/CIS. We predicted the optimal gap in BEV-PEM/CIS dosing to be 2.0 days in mice and 1.2 days in humans. Our simulations suggest that the efficacy loss in scheduling BEV-PEM/CIS at too great of a gap is much less than the efficacy loss in scheduling BEV-PEM/CIS at too short of a gap.
Softened Symbol Grounding for Neuro-symbolic Systems

Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol grounding process, bridging the gap between the two worlds, and resulting in an effective and efficient neuro-symbolic learning framework. Technically, the framework features (1) modeling of symbol solution states as a Boltzmann distribution, which avoids expensive state searching and facilitates mutually beneficial interactions between network training and symbolic reasoning;(2) a new MCMC technique leveraging projection and SMT solvers, which efficiently samples from disconnected symbol solution spaces; (3) an annealing mechanism that can escape from %being trapped into sub-optimal symbol groundings. Experiments with three representative neuro symbolic learning tasks demonstrate that, owining to its superior symbol grounding capability, our framework successfully solves problems well beyond the frontier of the existing proposals.
On the Data-Efficiency with Contrastive Image Transformation in Reinforcement Learning

Data-efficiency has always been an essential issue in pixel-based reinforcement learning (RL). As the agent not only learns decision-making but also meaningful representations from images. The line of reinforcement learning with data augmentation shows significant improvements in sample-efficiency. However, it is challenging to guarantee the optimality invariant transformation, that is, the augmented data are readily recognized as a completely different state by the agent. In the end, we propose a contrastive invariant transformation (CoIT), a simple yet promising learnable data augmentation combined with standard model-free algorithms to improve sample-efficiency. Concretely, the differentiable CoIT leverages original samples with augmented samples and hastens the state encoder for a contrastive invariant embedding. We evaluate our approach on DeepMind Control Suite and Atari100K. Empirical results verify advances using CoIT, enabling it to outperform the new state-of-the-art on various tasks. Source code is available at https://github.com/mooricAnna/CoIT.
LetsTalkShots: personalized vaccine risk communication

Introduction Vaccine hesitancy is a global health threat undermining control of many vaccine-preventable diseases. Patient-level education has largely been ineffective in reducing vaccine concerns and increasing vaccine uptake. We built and evaluated a personalized vaccine risk communication website called LetsTalkShots in English, Spanish and French (Canadian) for vaccines across the lifespan. LetsTalkShots tailors lived experiences, credible sources and informational animations to disseminate the right message from the right messenger to the right person, applying a broad range of behavioral theories. Methods We used mixed-methods research to test our animation and some aspects of credible sources and personal narratives. We conducted 67 discussion groups (n = 325 persons), stratified by race/ethnicity (African American, Hispanic, and White people) and population (e.g., parents, pregnant women, adolescents, younger adults, and older adults). Using a large Ipsos survey among English-speaking respondents (n = 2,272), we tested animations aligned with vaccine concerns and specific to population (e.g., parents of children, parents of adolescents, younger adults, older adults). Results Discussion groups provided robust feedback specific to each animation as well as areas for improvements across animations. Most respondents indicated that the information presented was interesting (85.5%), clear (96.0%), helpful (87.0%), and trustworthy (82.2%). Discussion Tailored vaccine risk communication can assist decision makers as they consider vaccination for themselves, their families, and their communities. LetsTalkShots presents a model for personalized communication in other areas of medicine and public health.
Using the behaviour change wheel and person-based approach to develop a digital self-management intervention for patients with adrenal insufficiency: the Support AI study protocol

Introduction Most patients with Adrenal insufficiency (AI) require lifelong glucocorticoid replacement. They need to increase glucocorticoids during physical illness or major stressful situations and require parenteral hydrocortisone in the event of an adrenal crisis. Patients with AI have impaired quality of life and high mortality; approximately 1 in 6-12 patients are hospitalised at least once/year from a potentially preventable adrenal crisis. Adoption of self-management behaviours are crucial; these include adherence to medication, following “sick day rules” and associated behaviours that aid prevention and treatment of adrenal crisis such as symptom monitoring, having extra tablets, carrying a medical-alert ID and injection kit, and self-injecting when necessary. Current patient education is ineffective at supporting self-management behaviour change or reducing adrenal crisis-related hospitalisations. This research study aims to gain an in-depth understanding of the barriers and enablers to self-management for patients with AI and to develop an evidence-based digital self-management behaviour change intervention. Methods The study is conducted in accordance with the MRC Framework for developing complex interventions. Underpinned by the Behaviour Change Wheel (BCW), the Theoretical Domains Framework (TDF), and the Person-Based Approach, this research will be conducted in two phases: Phase 1 will involve a sequential qualitative/quantitative mixed-methods study involving focus group interviews followed by a cross-sectional survey with patients with AI recruited from patient advocacy groups and endocrine clinics in the UK. Phase 2 will develop the Support AI, a website-based digital behaviour change intervention (DBCI) informed by Phase 1 findings to support self-management for patients with AI. The most appropriate behaviour change techniques (BCTs) will be selected utilising a nominal group technique with an Expert Panel of 10-15 key stakeholders. The design of the Support AI website will be guided by the Person-Based Approach using an Agile iterative “think-aloud” technique with 12-15 participants over 3 usability testing iterations. Conclusion A theory- and evidence-based digital behaviour change intervention will be developed which will be tested in a feasibility randomised trial following completion of this study. The projected benefit includes cost-effective health care service (reduced hospitalisations and demand for specialist services) and improved health outcomes and quality of life for patients with AI.
Influence of alpine meadow deterioration on soil microbial communities in the Yangtze River source region

Introduction: Although soil microbial populations are a good predictor of soil texture, little is known about how they react to alpine meadow deterioration. Methods: This study utilized Illumina HiSeq sequencing to investigate the effects of alpine meadow degradation on soil microbial communities in the Yangtze River source basin at five different degradation levels [i.e., non–degraded (ND), slightly degraded (LD), moderately degraded (MD), severely degraded (SD), and very severely degraded (VD)]. Results: The results indicated that bacterial and fungal α-diversity were not substantially different (p > 0.05) across the damaged alpine meadows, while β-diversity significantly differed (p < 0.01), indicating a higher variation in the microbial community due to alpine meadow degradation. Proteobacteria reduced considerably (p < 0.05) by 8.75%, 22.99%, and 24.65%, while Acidobacteria increased significantly (p < 0.05) by 41.67%, 85.20%, and 108.67%, in MD, SD, and VD compared with ND, respectively. Ascomycota declined significantly (p < 0.05) in the MD, whereas unclassified phyla rose significantly (p > 0.05) in the VD compared to the ND. The heatmaps of bacterial and fungal communities revealed two clusters: a ND, LD, and MD group and a SD and VD group, suggesting significant changes in soil microorganisms of alpine meadow in the SD and VD. Redundancy analysis (RDA) revealed that soil moisture, soil bulk density, soil organic carbon, total nitrogen, and plant biomass could explain 73.8% and 39.4% of the variance in bacterial and fungal community structure, respectively. Discussion: These findings imply that degradation of the alpine meadow impacts both plant and soil qualities, ultimately leading to changes in soil microbial populations in the Yangtze River’s source region.
Real-time simulation of the transplanted tooth using model order reduction

The biomechanics of transplanted teeth remain poorly understood due to a lack of models. In this context, finite element (FE) analysis has been used to evaluate the influence of occlusal morphology and root form on the biomechanical behavior of the transplanted tooth, but the construction of a FE model is extremely time-consuming. Model order reduction (MOR) techniques have been used in the medical field to reduce computing time, and the present study aimed to develop a reduced model of a transplanted tooth using the higher-order proper generalized decomposition method. The FE model of a previous study was used to learn von Mises root stress, and axial and lateral forces were used to simulate different occlusions between 75 and 175N. The error of the reduced model varied between 0.1% and 5.9% according to the subdomain, and was the highest for the highest lateral forces. The time for the FE simulation varied between 2.3 and 7.2 h. In comparison, the reduced model was built in 17s and interpolation of new results took approximately 2.10−2s. The use of MOR reduced the time for delivering the root stresses by a mean 5.9 h. The biomechanical behavior of a transplanted tooth simulated by FE models was accurately captured with a significant decrease of computing time. Future studies could include using jaw tracking devices for clinical use and the development of more realistic real-time simulations of tooth autotransplantation surgery.
Semi-supervised learning with a principled likelihood from a generative model of data curation

We currently do not have an understanding of semi-supervised learning (SSL) objectives such as pseudo-labelling and entropy minimization as log-likelihoods, which precludes the development of e.g. Bayesian SSL. Here, we note that benchmark image datasets such as CIFAR-10 are carefully curated, and we formulate SSL objectives as a log-likelihood in a generative model of data curation. We show that SSL objectives, from entropy minimization and pseudo-labelling, to state-of-the-art techniques similar to FixMatch can be understood as lower-bounds on our principled log-likelihood. We are thus able to introduce a Bayesian extension of SSL, which gives considerable improvements over standard SSL in the setting of 40 labelled points on CIFAR-10, with performance of 92 . 2 ± 0 . 3% vs 88 . 6% in the original FixMatch paper. Finally, our theory suggests that SSL is effective in part due to the statistical patterns induced by data curation. This provides an explanation of past results which show SSL performs better on clean datasets without any “out of distribution” examples. Confirming these results we find that SSL gave much larger performance improvements on curated than on uncurated data, using matched curated and uncurated datasets based on Galaxy Zoo 2. 1
An ecological and neurobiological perspective on the evolution of vocal learning

Vocal production learning (VPL) involves the use of auditory experience to guide the production of novel signals or to modify pre-existing signals. It allows animals to develop signals that are more complex and/or more flexible than innately developed signals. It has evolved rarely in vocal animals, widespread only in three avian and four mammalian taxa. The evolution of VPL was accompanied by innovations of the vocal motor neural circuitry. VPL is rare because of its various costs. Ecology, social spacing, and social fluidity can favor the evolution of VPL. It is striking that most taxa with VPL evolved in visually limited habitats, where sound is the only effective channel for communicating over distance from sender to receiver. Selective factors that favor the ability to produce complex and/or flexible signals would act predominantly on acoustic signals, and favor the evolution of VPL. Learning may be the only practical way to develop a signal complex enough to encode different types of information for assessment by receivers in animals that rely on acoustic communication, or to modify signals as local social factors dictate.
The fitness cost of horizontally transferred and mutational antimicrobial resistance in Escherichia coli

Antimicrobial resistance (AMR) in bacteria implies a tradeoff between the benefit of resistance under antimicrobial selection pressure and the incurred fitness cost in the absence of antimicrobials. The fitness cost of a resistance determinant is expected to depend on its genetic support, such as a chromosomal mutation or a plasmid acquisition, and on its impact on cell metabolism, such as an alteration in an essential metabolic pathway or the production of a new enzyme. To provide a global picture of the factors that influence AMR fitness cost, we conducted a systematic review and meta-analysis focused on a single species, Escherichia coli. By combining results from 46 high-quality studies in a multilevel meta-analysis framework, we find that the fitness cost of AMR is smaller when provided by horizontally transferable genes such as those encoding beta-lactamases, compared to mutations in core genes such as those involved in fluoroquinolone and rifampicin resistance. We observe that the accumulation of acquired AMR genes imposes a much smaller burden on the host cell than the accumulation of AMR mutations, and we provide quantitative estimates of the additional cost of a new gene or mutation. These findings highlight that gene acquisition is more efficient than the accumulation of mutations to evolve multidrug resistance, which can contribute to the observed dominance of horizontally transferred genes in the current AMR epidemic.
Liver immunopathogenesis in fatal cases of dengue in children: detection of viral antigen, cytokine profile and inflammatory mediators

Introduction Dengue virus (DENV), the etiologic agent of dengue fever illness, represents a global public health concern, mainly in tropical and subtropical areas across the globe. It is well known that this acute viral disease can progress to severe hemorrhagic stages in some individuals, however, the immunopathogenic basis of the development of more severe forms by these patients is yet to be fully understood. Objective In this context, we investigated and characterized the histopathological features as well as the cytokine profile and cell subpopulations present in liver tissues from three fatal cases of DENV in children. Methods Hematoxylin and Eosin, Periodic Acid Schiff and Picro Sirius Red staining were utilized for the histopathological analysis. Immunohistochemistry assay was performed to characterize the inflammatory response and cell expression patterns. Results Vascular dysfunctions such as hemorrhage, vascular congestion and edema associated with a mononuclear infiltrate were observedin all three cases. Liver tissues exhibited increased presence of CD68+ and TCD8+ cells as well as high expression of MMP-9, TNF-a, RANTES, VEGFR-2 mediators. Viral replication was confirmed by the detection of NS3 protein. Conclusion Taken together, these results evidenced key factors that may be involved in the development of severe alterations in liver tissues of children in response to DENV infection.
Mutual Partial Label Learning with Competitive Label Noise

Partial label learning (PLL) is an important weakly supervised learning problem, where each training instance is associated with a set of candidate labels that include both the true label and additional noisy labels. Most existing PLL methods assume the candidate noisy labels are randomly chosen, which hardly holds in real-world learning scenarios. In this paper, we consider a more realistic PLL scenario with competitive label noise that is more difﬁcult to distinguish from the true label than the random label noise. We propose a novel Mutual Learning based PLL approach named ML-PLL to address this challenging problem. ML-PLL learns a prediction network based classiﬁer and a class-prototype based classiﬁer cooperatively through interactive mutual learning and label correction. Moreover, we use a transformation network to model the association relationships between the true label and candidate labels, and learn it together with the prediction network to match the observed candidate labels in the training data and enhance label correction. Extensive experiments are conducted on several benchmark PLL datasets, and the proposed ML-PLL approach demonstrates state-of-the-art performance for partial label learning.
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting

Recently, Transformer-based methods have achieved surprising performance in the field of long-term series forecasting, but the attention mechanism for computing global correlations entails high complexity. And they do not allow for targeted modeling of local features as CNN structures do. To solve the above problems, we propose to combine local features and global correlations to capture the overall view of time series (e.g., fluctuations, trends). To fully exploit the underlying information in the time series, a multi-scale branch structure is adopted to model different potential patterns separately. Each pattern is extracted with down-sampled convolution and isometric convolution for local features and global correlations, respectively. In addition to being more effective, our proposed method, termed as Multi-scale Isometric Convolution Network (MICN), is more efficient with linear complexity about the sequence length with suitable convolution kernels. Our experiments on six benchmark datasets show that compared with state-of-the-art methods, MICN yields 17.2% and 21.6% relative improvements for multivariate and univariate time series, respectively. Code is available at https://github. com/wanghq21/MICN.