Miracle-dz/UniFAR-FTUs huggingface dataset:
Instance-wise Linearization of Neural Network for Model Interpretation

Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is still a major bottlenecks to deploy such technique into our daily life. The challenge can dive into the non-linear behavior of the neural network, which rises a critical question that how a model use input feature to make a decision. The classical approach to address this challenge is feature attribution, which assigns an important score to each input feature and reveal its importance of current prediction. However, current feature attribution approaches often indicate the importance of each input feature without detail of how they are actually processed by a model internally. These attribution approaches often raise a concern that whether they highlight correct features for a model prediction. For a neural network model, the non-linear behavior is often caused by non-linear activation units of a model. However, the computation behavior of a prediction from a neural network model is locally linear, because one prediction has only one activation pattern. Base on the observation, we propose an instance-wise linearization approach to reformulates the forward computation process of a neural network prediction. This approach reformulates different layers of convolution neural networks into linear matrix multiplication. Aggregating all layers' computation, a prediction complex convolution neural network operations can be described as a linear matrix multiplication $F(x) = W \cdot x + b$. This equation can not only provides a feature attribution map that highlights the important of the input features but also tells how each input feature contributes to a prediction exactly. Furthermore, we discuss the application of this technique in both supervise classification and unsupervised neural network learning parametric t-SNE dimension reduction.
XFEVER: Exploring Fact Verification across Languages

This paper introduces the Cross-lingual Fact Extraction and VERification (XFEVER) dataset designed for benchmarking the fact verification models across different languages. We constructed it by translating the claim and evidence texts of the Fact Extraction and VERification (FEVER) dataset into six languages. The training and development sets were translated using machine translation, whereas the test set includes texts translated by professional translators and machine-translated texts. Using the XFEVER dataset, two cross-lingual fact verification scenarios, zero-shot learning and translate-train learning, are defined, and baseline models for each scenario are also proposed in this paper. Experimental results show that the multilingual language model can be used to build fact verification models in different languages efficiently. However, the performance varies by language and is somewhat inferior to the English case. We also found that we can effectively mitigate model miscalibration by considering the prediction similarity between the English and target languages. The XFEVER dataset, code, and model checkpoints are available at https://github.com/nii-yamagishilab/xfever.
Pix2HDR - A pixel-wise acquisition and deep learning-based synthesis approach for high-speed HDR videos

Accurately capturing dynamic scenes with wide-ranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera's frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames. Yet, misaligned motion in these frames can still pose complications for HDR fusion algorithms, resulting in artifacts. Instead of frame-based exposures, we sample the videos using individual pixels at varying exposures and phase offsets. Implemented on a pixel-wise programmable image sensor, our sampling pattern simultaneously captures fast motion at a high dynamic range. We then transform pixel-wise outputs into an HDR video using end-to-end learned weights from deep neural networks, achieving high spatiotemporal resolution with minimized motion blurring. We demonstrate aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under low-light conditions and against bright backgrounds - both challenging conditions for conventional cameras. By combining the versatility of pixel-wise sampling patterns with the strength of deep neural networks at decoding complex scenes, our method greatly enhances the vision system's adaptability and performance in dynamic conditions.
Brainchop: Next Generation Web-Based Neuroimaging Application

Performing volumetric image processing directly within the browser, particularly with medical data, presents unprecedented challenges compared to conventional backend tools. These challenges arise from limitations inherent in browser environments, such as constrained computational resources and the availability of frontend machine learning libraries. Consequently, there is a shortage of neuroimaging frontend tools capable of providing comprehensive end-to-end solutions for whole brain preprocessing and segmentation while preserving end-user data privacy and residency. In light of this context, we introduce Brainchop (http://www.brainchop.org) as a groundbreaking in-browser neuroimaging tool that enables volumetric analysis of structural MRI using pre-trained full-brain deep learning models, all without requiring technical expertise or intricate setup procedures. Beyond its commitment to data privacy, this frontend tool offers multiple features, including scalability, low latency, user-friendly operation, cross-platform compatibility, and enhanced accessibility. This paper outlines the processing pipeline of Brainchop and evaluates the performance of models across various software and hardware configurations. The results demonstrate the practicality of client-side processing for volumetric data, owing to the robust MeshNet architecture, even within the resource-constrained environment of web browsers.
Evaluating, Understanding, and Improving Constrained Text Generation for Large Language Models

Advancements in natural language generation (NLG) and large language models (LLMs) have led to proficient text generation in various tasks. However, integrating intricate constraints into neural text generation, due to LLMs' opacity, remains challenging. This study investigates constrained text generation for LLMs, where predefined constraints are applied during LLM's generation process. Our research mainly focuses on mainstream open-source LLMs, categorizing constraints into lexical, structural, and relation-based types. We also present various benchmarks to facilitate fair evaluation. The study addresses some key research questions, including evaluating, understanding and improving constrained text generation for LLMs. Results illuminate LLMs' capacity and deficiency to incorporate constraints and provide insights for future developments in constrained text generation. Codes and datasets will be released upon acceptance.
The role of tropical and extra-tropical waves in the Hadley circulation

The tropical overturning circulation is examined in a moist aquaplanet general circulation model forced using a non-interactive sea surface temperature (SST) distribution that varies between a present-day Earth-like profile and one that is globally uniform. A traditional Hadley Cell (HC)-like flow is observed in all experiments along with the poleward transport of heat and angular momentum. In simulations with non-zero SST gradients, latent heat released from organized convection near the equator sets up a deep tropical cell; midlatitude baroclinic Rossby waves flux heat and angular momentum poleward, reinforcing the thermally direct circulation. As the imposed SST gradient is weakened, the HC transitions from a thermally and eddy-driven regime to one that's completely eddy-driven. When the SST is globally uniform, equatorial waves concentrate precipitation in the tropics and facilitate the lower-level convergence necessary for the ascending branch of the HC. Conventional midlatitude Rossby waves become very weak, but upper-level baroclinicity generates waves that cause equatorward transport of heat and poleward transport of momentum. Moreover, these upper-level waves induce a circulation that opposes the time-mean HC, thus highlighting the role of tropical waves in driving a traditional overturning flow for uniform SSTs. In all cases, anomalies associated with the tropical waves closely resemble those that sum to give the upper-level zonal mean divergent outflow. Through their ability to modulate tropical rainfall and the related latent heating, equatorial waves cause considerable hemispheric asymmetry in the HC and impart synoptic and intraseasonal variability to the tropical overturning circulation.
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits

We study the sample complexity of identifying the pure strategy Nash equilibrium (PSNE) in a two-player zero-sum matrix game with noise. Formally, we are given a stochastic model where any learner can sample an entry $(i,j)$ of the input matrix $A\in[-1,1]^{n\times m}$ and observe $A_{i,j}+\eta$ where $\eta$ is a zero-mean 1-sub-Gaussian noise. The aim of the learner is to identify the PSNE of $A$, whenever it exists, with high probability while taking as few samples as possible. Zhou et al. (2017) presents an instance-dependent sample complexity lower bound that depends only on the entries in the row and column in which the PSNE lies. We design a near-optimal algorithm whose sample complexity matches the lower bound, up to log factors. The problem of identifying the PSNE also generalizes the problem of pure exploration in stochastic multi-armed bandits and dueling bandits, and our result matches the optimal bounds, up to log factors, in both the settings.
A Causal Disentangled Multi-Granularity Graph Classification Method

Graph data widely exists in real life, with large amounts of data and complex structures. It is necessary to map graph data to low-dimensional embedding. Graph classification, a critical graph task, mainly relies on identifying the important substructures within the graph. At present, some graph classification methods do not combine the multi-granularity characteristics of graph data. This lack of granularity distinction in modeling leads to a conflation of key information and false correlations within the model. So, achieving the desired goal of a credible and interpretable model becomes challenging. This paper proposes a causal disentangled multi-granularity graph representation learning method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the important substructures and bias parts within the graph from a multi-granularity perspective. The disentanglement of the CDM-GNN model reveals important and bias parts, forming the foundation for its classification task, specifically, model interpretations. The CDM-GNN model exhibits strong classification performance and generates explanatory outcomes aligning with human cognitive patterns. In order to verify the effectiveness of the model, this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are employed for comparison purposes. Additionally, a qualitative analysis of the interpretation results is conducted.
TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer

Estimating the 6D object pose is an essential task in many applications. Due to the lack of depth information, existing RGB-based methods are sensitive to occlusion and illumination changes. How to extract and utilize the geometry features in depth information is crucial to achieve accurate predictions. To this end, we propose TransPose, a novel 6D pose framework that exploits Transformer Encoder with geometry-aware module to develop better learning of point cloud feature representations. Specifically, we first uniformly sample point cloud and extract local geometry features with the designed local feature extractor base on graph convolution network. To improve robustness to occlusion, we adopt Transformer to perform the exchange of global information, making each local feature contains global information. Finally, we introduce geometry-aware module in Transformer Encoder, which to form an effective constrain for point cloud feature learning and makes the global information exchange more tightly coupled with point cloud tasks. Extensive experiments indicate the effectiveness of TransPose, our pose estimation pipeline achieves competitive results on three benchmark datasets.
Artificial intelligence with temporal features outperforms machine learning in predicting diabetes

Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities.
Studying «too much»? A comparative and diachronic analysis of overeducation among tertiary graduates.

The aim of this dissertation is to assess if and how institutional features are likely to shape fields of study differentials in the overeducation risk. This issue is addressed with two different perspectives. First it is comparatively analysed how the differential incidence of overeducation among graduates from different fields of study is affected by labour market institutions. Then the effect of the university reform dictated by the guidelines of the Bologna Process on tertiary graduates’ risk of overeducation is evaluated. Italy is selected as a relevant case study for these analyses since, in comparison with other developed countries, it displays both low graduation rates and modest returns to tertiary education.
A comparison of phylogenetic and distance-based approaches for the distinction of genetically closed species, Draba rimarum (Rech.f.) A.R. Khosravi & A. Eslami-Farouji, and Draba aucheri Boiss. (Brassicaceae) as a case study

Circumscribing species boundries is necessary in systematic plant biology. Even a mistake in delimiting taxa may lead to incorrect scientific interpretations. Draba rimarum (Rech.f.) A.R. Khosravi & A. Eslami-Farouji is an endemic Iranian species with a narrow geographic distribution, and is genetically close to D. aucheri. The present study provided a phylogenetic review, time divergence, and planar network of both species to unravel the distinct position of both species along with the prediction of any conflicting or ambiguous signals. Regarding this purpose, here we represent that phylogenetic trees may fail to show reliable results toward the distinct position of genetically close species.
Promoting cardioprotection with fenugreek: Insights from CoCl2-induced hypoxia in neonatal rat cardiomyocytes

Objective(s): This study aimed to investigate the protective effects of fenugreek on CoCl2-induced hypoxia in neonatal rat cardiomyocytes. Materials and Methods: Primary cardiomyocytes were isolated from Sprague Dawley rats aged 0–2 days and incubated with various concentrations of fenugreek (10-320 µg/ml) and CoCl2-induced hypoxia for different durations (24, 48, and 72 hr). Cell viability, calcium signaling, beating rate, and gene expression were evaluated. Results: Fenugreek treatments did not cause any toxicity in cardiomyocytes. At a concentration of 160 µg/ml for 24 hr, fenugreek protected the heart against CoCl2-induced hypoxia, as evidenced by reduced expression of caspases (-3, -6, -8, and -9) and other functional genes markers, such as HIF-1α, Bcl-2, IP3R, ERK5, and GLP-1r. Calcium signaling and beating rate were also improved in fenugreek-treated cardiomyocytes. In contrast, CoCl2 treatment resulted in up-regulation of the hypoxia gene HIF-1α and apoptotic caspases gene (-3, -9, -8, -12), and down-regulation of Bcl-2 activity. Conclusion: Fenugreek treatment at a concentration of 160 µg/ml was not toxic to neonatal rat cardiomyocytes and protected against CoCl2-induced hypoxia. Furthermore, fenugreek improved calcium signaling and beating rate and altered gene expression. Fenugreek may be a potential therapeutic agent for promoting cardioprotection against hypoxia-induced injuries.
Haplotypic analysis of cox1 from Toxocara canis demonstrates five distinct clades that are not geographically defined

Background Toxocara canis is a cosmopolitan parasite of dogs that is transmitted transplacentally to puppies resulting in widespread shedding of eggs in the environment. However, it is not clear if there are dominant parasite genotypes that are more common, pathogenic, or likely to be zoonotic. Methods/principle findings Sequences of mitochondrial cox1 gene from adult worms were used to compare parasites from the United States with submitted sequences from parasites isolated from dogs in different countries. Our analysis revealed at least 55 haplotypes. While we expected the North American worms to form a distinct cluster, we found haplotypes of T. canis reported elsewhere existing in this population. Interestingly, combining the sequence data from our study with the available GenBank data, analysis of cox1 sequences results in five distinct clades that are not geographically defined. Conclusions The five clades of T. canis revealed in this study potentially have unique life histories, traits, or host preferences. Additional investigation is needed to see if these distinct clades represent cryptic species with clinically useful attributes or genotypes with taxonomic value. Evaluation of common mitochondrial genes may reveal distinct populations of zoonotic T. canis.
Enhanced causal effect of ecosystem photosynthesis on respiration during heatwaves

Because of global warming, Earth’s ecosystems have been experiencing more frequent and severe heatwaves. Heatwaves are expected to tip terrestrial carbon sequestration by elevating ecosystem respiration and suppressing gross primary productivity (GPP). Here, using the convergent cross-mapping technique, this study detected positive bidirectional causal effects between GPP and respiration in two unprecedented European heatwaves. Heatwaves enhanced the causal effect strength of GPP on respiration rather than respiration on GPP across 40 site-years of observations. Further analyses and global simulations revealed spatial heterogeneity in the heatwave response of the causal link strength between GPP and respiration, which was jointly driven by the local climate and vegetation properties. However, the causal effect strength of GPP on respiration showed considerable uncertainties in CMIP6 models. This study reveals an enhanced causal link strength between GPP and respiration during heatwaves, shedding light on improving projections for terrestrial carbon sink dynamics under future climate extremes.
Investigation of fructose consumption on hippocampal insulin and glucagon-like peptide-1 receptors, and metabolic effects in rats

Objective(s): The detrimental effects of high fructose consumption on metabolic health have been extensively studied. However, limited research has focused on the impact of fructose intake on neuroprotective mechanisms, specifically the expression of insulin receptor (INSR) and glucagon-like peptide-1 receptor (GLP-1R) in the hippocampus. Understanding the effects of fructose on these neuroprotective molecules can provide valuable insights into the potential role of fructose in hippocampal dysfunction. The goal of this study is to aim at the basal plasma levels of lipid profile, insulin, GLP-1, and HOMA-IR, as well as the mRNA and protein expression of neuroprotective molecules such as INSR and GLP-1R in Wistar rats fed a high fructose diet. Materials and Methods: Rats were separated into control (C) and high fructose (HF) groups. The HF group was given 20% fructose water to drink for 16 weeks. Results: Fructose ingestion significantly increased abdominal fat (C=1.24±0.08 g, HF=1.79±0.19 g, P<0.05) and plasma triglyceride levels (C=179.22±22.85 µg/ml, HF=242.45±14.45 µg/ml, P<0.05), but had no statistically significant effect on body weight and plasma HDL, LDL, total cholesterol, insulin, and GLP-1 levels (P>0.05). Although INSR mRNA expression in the hippocampus was significantly lower in the HF group compared to the control group (P<0.05), GLP-1R mRNA expression did not differ significantly across the groups (P>0.05). Furthermore, whereas INSR and GLP-1R protein levels in the experimental group were on a declining trend, this trend was not substantially different (P>0.05). Conclusion: These data suggest that fructose consumption may be harmful to the hippocampus by lowering the expression of INSR.
Demographic dynamics between 5500 and 3500 calBP (3550–1550 BCE) in selected study regions of Central Europe and the role of regional climate influences

With their rich Late Neolithic to Early Bronze Age archives, the Circumharz region, the Czech Republic/Lower Austria region, and the Northern Alpine Foreland are well-suited for research on potential links between human activities and climate fluctuations of this period with pronounced archaeological changes. In this paper, we reconstruct the rate and density of the available 14C data from 5500 to 3500 calBP (3550–1550 BCE). We ask to what extent population patterns varied over time and space, and whether fluctuations in human populations and their activities varied with local/regional climate changes. To answer these questions, we have compiled an extensive list of published radiocarbon dates and created 14C sum calibrations for each region. We also compare population dynamics with local and regional palaeoclimate records derived from high-resolution speleothems. At the regional scale, the results suggest a causal relationship between regional climate and population trends. Climate and associated environmental changes were thus at least partly responsible for demographic trends. These results also allow us to question the motivation for the construction of so-called “Early Bronze Age princely tombs” in the Circumharz region during a period of population decline. Among other things, it can be argued that the upper echelons of society may have benefited from trade relations. However, this process was accompanied by ecological stress, a cooling of the winter climate, a decline in the total population and an increase in social inequality.