Miracle-dz/UniFAR-FTUs huggingface dataset:
Eyes of the world on a warmer, less frozen, and greener Arctic

Rapid atmospheric warming and sea-ice retreat are driving widespread changes in Arctic ecosystems, among the most pervasive of which is the "greening of the Arctic"-an increase in the cover and biomass of vegetation observed by satellites across much of the Arctic tundra biome. Determining the drivers, impacts, and feedbacks of Arctic greening requires continued investment in robust field, remote-sensing, and model-based capabilities, and improved integration of the knowledge base of Arctic peoples. These tools and approaches support the triangulation of complex problems and the development of improved projections for the warmer Arctic tundra biome of the future.
Sport in ischemic heart disease: Focus on primary and secondary prevention

Ischemic heart disease (IHD) is one of the leading causes of death and morbidity in the world. The role of primary prevention is particularly relevant since IHD can be for a long time asymptomatic until the occurrence of a condition that could lead to plaque instabilization or increased oxygen demand. Secondary prevention is also essential to improve patients' prognosis and quality of life. The aim of this review is to provide a detailed and updated description of the role of sport and physical activity both in primary prevention and secondary prevention. In primary prevention, sport and physical activity are effective through the control of the main cardiovascular risk factors, such as hypertension and dyslipidemia. In secondary prevention, sport and physical activity can lead to a reduction in subsequent coronary events. Every effort must be made to encourage the performance of physical and sports activity both in asymptomatic subjects at risk and those with a history of IHD.
Proportional multiple-integral observer design for descriptor systems with measurement output disturbances

A design approach for observers using multiple-integrals is presented for descriptor systems, which allows the authors to decouple or attenuate measurement output disturbances. Design schemes for both pure integral observers and proportional integral observers are investigated. Measurement noise and input disturbances are also simultaneously taken into account in the design. A systematic design procedure for a causal or normal multiple-integral observer is addressed, which can be implemented conveniently using Matlab software. The considered disturbance may be unbounded, which means that the presented design approach is suitable for more general cases. Moreover, the system state, together with the disturbance vector and its finite time derivatives can be simultaneously estimated. The proposed design has great potential for application to control problems, such as those in diagnosis or observer-based control. In addition, it also has good estimation robustness and can be directly applied to normal systems. Simulation results show a satisfactory tracking performance as compared to the results obtained for descriptor systems.
Ultraviolet B radiation induces oxidative stress and apoptosis in human lens epithelium cells by activating NF-κB signaling to down-regulate sodium vitamin C transporter 2 (SVCT2) expression

ABSTRACT Ultraviolet B (UVB) exposure is reported to cause cataract formation by inducing excessive reactive oxygen species (ROS) and apoptosis in human lens epithelial cells (HLECs). Sodium-dependent Vitamin C transports-2 (SVCT2) is a ascorbic acid (AsA) transporter for that can protect cells and tissues from oxidative stress. Here, we focus on the functional characterization and mechanism analysis of SVCT2 in UVB-treated HLECs. The results showed a significant reduction of SVCT2 expression in HLECs treated with UVB. SVCT2 abated apoptosis and Bax expression and increased Bcl-2 expression. Moreover, SVCT2 decreased ROS accumulation and MDA level, but increased the activities of antioxidant enzymes (SOD and GSH-PX). NF-κB inhibitor (PDTC) alleviated ROS production and apoptosis, and promoted SVCT2 expression in UVB-treated HLECs. Additionally, ROS inhibitor (NAC) suppressed oxidative stress, apoptosis, and induced SVCT2 expression in UVB-treated HLECs, while these effects were significantly abated due to the activation of NF-κB signaling. Furthermore, SVCT2 facilitated 14C-AsA absorption in UVB-treated HLECs. Together, our findings demonstrated that UVB exposure-induced ROS generation, which further activated NF-κB signaling to down-regulate SVCT2 expression in HLECs. Then, downregulated SVCT2 promoted ROS accumulation and induced apoptosis by decreasing AsA uptake. Our data reveal a novel NF-κB/SVCT2/AsA regulatory pathway and suggest the therapeutic potential of SVCT2 in UVB-induced cataract.
Electroencephalography microstates imbalance across the spectrum of early psychosis, autism, and mood disorders

Abstract Background Electroencephalography (EEG) microstates translate resting-state temporal dynamics of neuronal networks throughout the brain and could constitute possible markers of psychiatric disorders. We tested the hypothesis of an increased imbalance between a predominant self-referential mode (microstate C) and a decreased attentional mode (microstate D) in psychosis, mood, and autism spectrum disorders. Methods We retrospectively included 135 subjects from an early psychosis outpatient unit, with available eyes-closed resting-state 19 electrodes EEG. Individual-level then group-level modified K-means clustering in controls provided four microstate maps that were then backfitted to all groups. Differences between microstate parameters (occurrence, coverage, and mean duration) were computed between controls and each group, and between disease groups. Results Microstate class D parameters were systematically decreased in disease groups compared with controls, with an effect size increasing along the psychosis spectrum, but also in autism. There was no difference in class C. C/D ratios of mean duration were increased only in SCZ compared with controls. Conclusions The decrease in microstate class D may be a marker of stage of psychosis, but it is not specific to it and may rather reflect a shared dimension along the schizophrenia-autism spectrum. C/D microstate imbalance may be more specific to schizophrenia.
Multiplication-Free Transformer Training via Piecewise Affine Operations

Multiplications are responsible for most of the computational cost involved in neural network training and inference. Recent research has thus looked for ways to reduce the cost associated with them. Inspired by Mogami (2020), we replace multiplication with a cheap piecewise affine approximation that is achieved by adding the bit representation of the floating point numbers together as integers. We show that transformers can be trained with the resulting modified matrix multiplications on both vision and language tasks with little to no performance impact, and without changes to the training hyperparameters. We further replace all non-linearities in the networks making them fully and jointly piecewise affine in both inputs and weights. Finally, we show that we can eliminate all multiplications in the entire training process, including operations in the forward pass, backward pass and optimizer update, demonstrating the first successful training of modern neural network architectures in a fully multiplication-free fashion.
Learning Conditional Attributes for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute “wet” in “wet apple” and “wet cat” is different. As a solution, we provide analysis and argue that attributes are conditioned on the recognized object and input image and explore learning conditional attribute embeddings by a proposed attribute learning framework containing an attribute hyper learner and an attribute base learner. By encoding conditional attributes, our model enables to generate flexible attribute embeddings for generalization from seen to unseen compositions. Experiments on CZSL benchmarks, including the more challenging C-GQA dataset, demonstrate better performances compared with other state-of-the-art approaches and validate the importance of learning conditional attributes. Code‡1Gllee:https://gitee.com/wqshmzh/canet-czsl is available at https://github.com/wqshmzh/CANet-CZSL.
Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches

The growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architectures with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks. However, the lack of safety guarantees proves, which is one of the key challenges to be addressed, limit drastically the ambition to introduce more broadly AVs on our roads and restrict the use of AVs to very limited use cases. Therefore, the focus and the ambition of this paper is to survey research on autonomous vehicles while focusing on the important topic of safety guarantee of AVs. For this purpose, it is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs, with an emphasis on the safety assessment and decision-making systems composing these architectures. Moreover, it is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches. This is performed while emphasizing the strengths and weaknesses of each methodology and investigating the research that proposes a comprehensive multi-modal design that combines model-based and AI approaches. This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks.
Probing reaction channels via reinforcement learning

Chemical reactions are dynamical processes involving the correlated reorganization of atomic configurations, driving the conversion of an initial reactant into a result product. By virtue of the metastability of both the reactants and products, chemical reactions are rare events, proceeding fleetingly. Reaction pathways can be modelled probabilistically by using the notion of reactive density in the phase space of the molecular system. Such density is related to a function known as the committor function, which describes the likelihood of a configuration evolving to one of the nearby metastable regions. In theory, the committor function can be obtained by solving the backward Kolmogorov equation (BKE), which is a partial differential equation (PDE) defined in the full dimensional phase space. However, using traditional methods to solve this problem is not practical for high dimensional systems. In this work, we propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of states that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based PDE solver to obtain an approximation solution of a restricted BKE, even when the dimension of the problem is very high. The resulting solution provides an approximation for the committor function that encodes mechanistic information for the reaction, paving a new way for understanding of complex chemical reactions and evaluation of reaction rates.
Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark

Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario. This paper introduces a new dataset SURE (Multimodal Recommendation Dialog with SUbjective PREference), which contains 12K shopping dialogs in complex store scenes. The data is built in two phases with human annotations to ensure quality and diversity. SURE is well-annotated with subjective preferences and recommendation acts proposed by sales experts. A comprehensive analysis is given to reveal the distinguishing features of SURE. Three benchmark tasks are then proposed on the data to evaluate the capability of multimodal recommendation agents. Based on the SURE, we propose a baseline model, powered by a state-of-the-art multimodal model, for these tasks.
Nonreciprocal heat flux via synthetic fields in linear quantum systems

We study the heat transfer between N coupled quantum resonators with applied synthetic electric and magnetic fields realized by changing the resonators parameters by external drivings. To this end we develop two general methods, based on the quantum optical master equation and on the Langevin equation for $N$ coupled oscillators where all quantum oscillators can have their own heat baths. The synthetic electric and magnetic fields are generated by a dynamical modulation of the oscillator resonance with a given phase. Using Floquet theory we solve the dynamical equations with both methods which allow us to determine the heat flux spectra and the transferred power. With apply these methods to study the specific case of a linear tight-binding chain of four quantum coupled resonators. We find that in that case, in addition to a non-reciprocal heat flux spectrum already predicted in previous investigations, the synthetic fields induce here non-reciprocity in the total heat flux hence realizing a net heat flux rectification.
Beyond Confidence: Reliable Models Should Also Consider Atypicality

While most machine learning models can provide confidence in their predictions, confidence is insufficient to understand a prediction's reliability. For instance, the model may have a low confidence prediction if the input is not well-represented in the training dataset or if the input is inherently ambiguous. In this work, we investigate the relationship between how atypical(rare) a sample or a class is and the reliability of a model's predictions. We first demonstrate that atypicality is strongly related to miscalibration and accuracy. In particular, we empirically show that predictions for atypical inputs or atypical classes are more overconfident and have lower accuracy. Using these insights, we show incorporating atypicality improves uncertainty quantification and model performance for discriminative neural networks and large language models. In a case study, we show that using atypicality improves the performance of a skin lesion classifier across different skin tone groups without having access to the group attributes. Overall, we propose that models should use not only confidence but also atypicality to improve uncertainty quantification and performance. Our results demonstrate that simple post-hoc atypicality estimators can provide significant value.
CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation

Neural machine translation (NMT) systems exhibit limited robustness in handling source-side linguistic variations. Their performance tends to degrade when faced with even slight deviations in language usage, such as different domains or variations introduced by second-language speakers. It is intuitive to extend this observation to encompass dialectal variations as well, but the work allowing the community to evaluate MT systems on this dimension is limited. To alleviate this issue, we compile and release CODET, a contrastive dialectal benchmark encompassing 891 different variations from twelve different languages. We also quantitatively demonstrate the challenges large MT models face in effectively translating dialectal variants. All the data and code have been released.
Deep Variational Lesion-Deficit Mapping

Causal mapping of the functional organisation of the human brain requires evidence of \textit{necessity} available at adequate scale only from pathological lesions of natural origin. This demands inferential models with sufficient flexibility to capture both the observable distribution of pathological damage and the unobserved distribution of the neural substrate. Current model frameworks -- both mass-univariate and multivariate -- either ignore distributed lesion-deficit relations or do not model them explicitly, relying on featurization incidental to a predictive task. Here we initiate the application of deep generative neural network architectures to the task of lesion-deficit inference, formulating it as the estimation of an expressive hierarchical model of the joint lesion and deficit distributions conditioned on a latent neural substrate. We implement such deep lesion deficit inference with variational convolutional volumetric auto-encoders. We introduce a comprehensive framework for lesion-deficit model comparison, incorporating diverse candidate substrates, forms of substrate interactions, sample sizes, noise corruption, and population heterogeneity. Drawing on 5500 volume images of ischaemic stroke, we show that our model outperforms established methods by a substantial margin across all simulation scenarios, including comparatively small-scale and noisy data regimes. Our analysis justifies the widespread adoption of this approach, for which we provide an open source implementation: https://github.com/guilherme-pombo/vae_lesion_deficit
FORWARD-HALF-REFLECTED-PARTIAL INVERSE-BACKWARD SPLITTING ALGORITHM FOR SOLVING MONOTONE INCLUSIONS

In this article, we proposed a method for numerically solving monotone inclusions in real Hilbert spaces that involve the sum of a maximally monotone operator, a monotone-Lipschitzian operator, a cocoercive operator, and a normal cone to a vector subspace. Our algorithm splits and exploits the intrinsic properties of each operator involved in the inclusion. The proposed method is derived by combining partial inverse techniques and the forward-half-reﬂected-backward (FHRB) splitting method proposed by Malitsky and Tam (2020). Our method inherits the advantages of FHRB, requiring only one activation of the Lipschitzian operator, one activation of the cocoercive operator, two projections onto the closed vector subspace, and one calculation of the resolvent of the maximally monotone operator. Furthermore, we develop a method for solving primal-dual inclusions involving a mixture of sums, linear compositions, parallel sums, Lipschitzian operators, cocoercive operators, and normal cones. We apply our method to constrained composite convex optimization problems as a speciﬁc example. Finally, in order to compare our proposed method with existing methods in the literature, we provide numerical experiments on constrained total variation least-squares optimization problems. The numerical results are promising.
Few-shot Medical Image Segmentation via Cross-Reference Transformer

Deep learning models have become the mainstream method for medical image segmentation, but they require a large manually labeled dataset for training and are difficult to extend to unseen categories. Few-shot segmentation(FSS) has the potential to address these challenges by learning new categories from a small number of labeled samples. The majority of the current methods employ a prototype learning architecture, which involves expanding support prototype vectors and concatenating them with query features to conduct conditional segmentation. However, such framework potentially focuses more on query features while may neglect the correlation between support and query features. In this paper, we propose a novel self-supervised few shot medical image segmentation network with Cross-Reference Transformer, which addresses the lack of interaction between the support image and the query image. We first enhance the correlation features between the support set image and the query image using a bidirectional cross-attention module. Then, we employ a cross-reference mechanism to mine and enhance the similar parts of support features and query features in high-dimensional channels. Experimental results show that the proposed model achieves good results on both CT dataset and MRI dataset.
On the Value of Myopic Behavior in Policy Reuse

Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In reinforcement learning, rationally reusing the policies acquired from other tasks or human experts is critical for tackling problems that are difficult to learn from scratch. In this work, we present a framework called Selective Myopic bEhavior Control~(SMEC), which results from the insight that the short-term behaviors of prior policies are sharable across tasks. By evaluating the behaviors of prior policies via a hybrid value function architecture, SMEC adaptively aggregates the sharable short-term behaviors of prior policies and the long-term behaviors of the task policy, leading to coordinated decisions. Empirical results on a collection of manipulation and locomotion tasks demonstrate that SMEC outperforms existing methods, and validate the ability of SMEC to leverage related prior policies.
Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting

Mine planning is a complex task that involves many uncertainties. During early stage feasibility, available mineral resources can only be estimated based on limited sampling of ore grades from sparse drilling, leading to large uncertainty in under-sampled parts of the deposit. Planning the extraction schedule of ore over the life of a mine is crucial for its economic viability. We introduce a new approach for determining an “optimal schedule under uncertainty” that provides probabilistic bounds on the profits obtained in each period. This treatment of uncertainty within an economic framework reduces previously difficult-to-use models of variability into actionable insights. The new method discounts profits based on uncertainty within an evolutionary algorithm, sacrificing economic optimality of a single geological model for improving the downside risk over an ensemble of equally likely models. We provide experimental studies using Maptek's mine planning software Evolution. Our results show that our new approach is successful for effectively making use of uncertainty information in the mine planning process.
A schematic model for the direct cross-section in reactions induced by exotic and stable projectiles

A geometric model for the direct contribution of the reaction cross section induced by light ions on different targets is presented. The model separates the total reaction cross section into two components, one for total fusion and another for direct reactions. We show that the direct part scales as $2 \pi Ra$, where $R$ is related to the nuclear radius and $a$ is the width of a ring, which is related to the nuclear diffuseness. A simple expression is presented to calculate the radius $R$ and the width parameter $a$ in terms of the masses and charges of the system. The method is applied to experimental data of exotic, weakly bound, and strongly bound projectiles on several targets. Different diffuseness parameters were obtained for different types of projectiles: exotic n-rich, stable weakly bound, stable strongly bound and exotic p-rich exotic projectiles.