Miracle-dz/UniFAR-FTUs huggingface dataset:
RiverText: A Python Library for Training and Evaluating Incremental Word Embeddings from Text Data Streams

Word embeddings have become essential components in various information retrieval and natural language processing tasks, such as ranking, document classification, and question answering. However, despite their widespread use, traditional word embedding models present a limitation in their static nature, which hampers their ability to adapt to the constantly evolving language patterns that emerge in sources such as social media and the web (e.g., new hashtags or brand names). To overcome this problem, incremental word embedding algorithms are introduced, capable of dynamically updating word representations in response to new language patterns and processing continuous data streams. This paper presents RiverText, a Python library for training and evaluating incremental word embeddings from text data streams. Our tool is a resource for the information retrieval and natural language processing communities that work with word embeddings in streaming scenarios, such as analyzing social media. The library implements different incremental word embedding techniques, such as Skip-gram, Continuous Bag of Words, and Word Context Matrix, in a standardized framework. In addition, it uses PyTorch as its backend for neural network training. We have implemented a module that adapts existing intrinsic static word embedding evaluation tasks for word similarity and word categorization to a streaming setting. Finally, we compare the implemented methods with different hyperparameter settings and discuss the results. Our open-source library is available at https://github.com/dccuchile/rivertext.
MR2: A Benchmark for Multimodal Retrieval-Augmented Rumor Detection in Social Media

As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Misinformation spreaders have recently targeted contextual connections between the modalities e.g., text and image. However, existing datasets for rumor detection mainly focus on a single modality i.e., text. To bridge this gap, we construct MR2, a multimodal multilingual retrieval-augmented dataset for rumor detection. The dataset covers rumors with images and texts, and provides evidence from both modalities that are retrieved from the Internet. Further, we develop established baselines and conduct a detailed analysis of the systems evaluated on the dataset. Extensive experiments show that MR2 will provide a challenging testbed for developing rumor detection systems designed to retrieve and reason over social media posts. Source code and data are available at: https://github.com/THU-BPM/MR2.
Momentum Flux of Convective Gravity Waves Derived from an Offline Gravity Wave Parameterization. Part II: Impacts on the Quasi-Biennial Oscillation

The characteristics of small-scale convective gravity waves (CGWs; horizontal wavelengths <100 km) and their contributions to the large-scale flow in the stratosphere, including the quasi-biennial oscillation (QBO), are investigated using an offline calculation of a source-dependent, physically based CGW parameterization with global reanalysis data from 1979 to 2010. The CGW momentum flux (CGWMF) and CGW drag (CGWD) are calculated from the cloud top (source level) to the upper stratosphere using a Lindzen-type wave propagation scheme. The 32-yr-mean CGWD exhibits large magnitudes in the tropical upper stratosphere and near the stratospheric polar night jet (~60°). The maximum positive drag is 0.1 (1.5) m s−1 day−1, and the maximum negative drag is −0.9 (−0.7) m s−1 day−1 in January (July) between 3 and 1 hPa. In the tropics, the momentum forcing by CGWs at 30 hPa associated with the QBO in the westerly shear zone is 3.5–6 m s−1 month−1, which is smaller than that by Kelvin waves, while that by CGWs in the easterly shear zone (3.1–6 m s−1 month−1) is greater than that by any other equatorial planetary waves or inertio-gravity waves (inertio-GWs). Composite analyses of the easterly QBO (EQBO) and westerly QBO (WQBO) phases reveal that the zonal CGWMF is concentrated near 10°N and that the negative (positive) CGWD extends latitudinally to ±20° (±10°) at 30 hPa. The strongest (weakest) negative CGWD is in March–May (September–November) during the EQBO, and the strongest (weakest) positive CGWD is in June–August (March–May) during the WQBO. The CGWMF and CGWD are generally stronger during El Niño than during La Niña in the equatorial region.
Role of trafficking protein particle complex 2 in medaka development

The skeletal dysplasia spondyloepiphyseal dysplasia tarda (SEDT) is caused by mutations in the TRAPPC2 gene, which encodes Sedlin, a component of the trafficking protein particle (TRAPP) complex that we have shown previously to be required for the export of type II collagen (Col2) from the endoplasmic reticulum. No vertebrate model for SEDT has been generated thus far. To address this gap, we generated a Sedlin knockout animal by mutating the orthologous TRAPPC2 gene (olSedl) of Oryzias latipes (medaka) fish. OlSedl deficiency leads to embryonic defects, short size, diminished skeletal ossification, and altered Col2 production and secretion, resembling human defects observed in SEDT patients. Moreover, SEDT knock-out animals display photoreceptor degeneration and gut morphogenesis defects, suggesting a key role for Sedlin in the development of these organs. Thus, by studying Sedlin function in vivo, we provide evidence for a mechanistic link between TRAPPC2-mediated membrane trafficking, Col2 export, and developmental disorders.
Estimating person-specific neural correlates of mental rotation: A machine learning approach

Using neurophysiological measures to model how the brain performs complex cognitive tasks such as mental rotation is a promising way towards precise predictions of behavioural responses. The mental rotation task has frequently been used to monitor progressive neurological disorders. Up until now, research on neural correlates of mental rotation have largely focused on group analyses yielding models with features common across individuals. Here, we propose an individually tailored machine learning approach to identify person-specific patterns of neural activity during mental rotation. We trained ridge regressions to predict the reaction time of correct responses in a mental rotation task using task-related, non-invasive electrocortical activity of the same person. When tested on independent data of the same person, the regression model predicted the reaction times significantly more accurately than when only the average reaction time was used for prediction (bootstrap mean difference of 0.02, 95% CI: 0.01–0.03, p < .001). When tested on another person’s data, the predictions were significantly less accurate compared to within-person predictions. Further analyses revealed that considering person-specific reaction times and topographical activity patterns substantially improved a model’s generalizability. Our results indicate that a more individualized approach towards neural correlates can improve their predictive performance of behavioural responses, particularly when combined with machine learning.
Brain network reconfiguration during prediction error processing

Learning from experience is driven by reward prediction errors—signals that reflect updates to our expectations of reward. Despite numerous studies on neural correlates of reward prediction errors, the question of how large-scale brain networks reconfigure in response to reward prediction error signalling remains open. Here we ask how functional networks change in response to reward prediction errors depending on the context. In our study participants performed the probabilistic reversal learning task in functional magnetic resonance imaging (fMRI) scanner in two experimental contexts: a reward-seeking setting and a punishment-avoiding. We found that the participants’ learning speed depended on the sign of the prediction error but not on the experimental context. Whole-brain network analysis revealed a multi-scale community structure with a separate striatal reward network emerging at a finer topological scale and a ventromedial prefrontal network emerging at a coarser scale. We also found that the integration between large-scale networks increased when switching from positive to negative prediction error events. This pattern of large-scale network reconfiguration aligns with the broad range of research showing increased network integration with increased cognitive demands. Our findings offer a first sketch of how processing reward prediction error affects the functional connectivity of brain-wide networks.
The Creation of a Vortex in Sea water through the MHD

The Magneto hydrodynamics equations, MHD, are discussed for weakly conductive fluids of electricity, and then an analytical solution is presented. An MHD vortex is generated using seawater in a cylindrical vessel, where external electromagnetic fields are conveniently applied. Some physical quantities of interest are discussed and measured by well-known techniques.
Reconfiguration of Behavioral Signals in the Anterior Cingulate Cortex based on Emotional State

Behaviours and their execution depend on the context and emotional state in which they are performed. The contextual modulation of behavior likely relies on regions such as the anterior cingulate cortex (ACC) that multiplex information about emotional/autonomic states and behaviours. The objective of the present study was to understand how the representations of behaviors by ACC neurons become modified when performed in different emotional states. A pipeline of machine learning techniques was developed to categorize and classify complex, spontaneous behaviors from video. This pipeline, termed HUB-DT, discovered a range of statistically separable behaviors during a task in which motivationally significant outcomes were delivered in blocks of trials that created 3 unique ‘emotional contexts’. HUB-DT was capable of detecting behaviors specific to each emotional context and was able to identify and segregate the portions of a neural signal related to a behaviour and to emotional context. Overall, ∼10x as many neurons responded to behaviors in a contextually dependent versus a fixed manner, highlighting the extreme impact of emotional state on representations of behaviors that were precisely defined based on detailed analyses of limb kinematics. This type of modulation may be a key mechanism that allows the ACC to modify behavioral output based on emotional states and contextual demands.
Clonal CD8 T cells in the leptomeninges are locally controlled and influence microglia in human neurodegeneration

Recent murine studies have highlighted a crucial role for the meninges in surveilling the central nervous system (CNS) and influencing CNS inflammation. However, how meningeal immunity is altered in human neurodegeneration and its potential effects on neuroinflammation is understudied. In the present study, we performed single-cell analysis of the transcriptomes and T cell receptor repertoire of 72,576 immune cells from 36 postmortem human brain and leptomeninges tissues from donors with neurodegenerative diseases including amyotrophic lateral sclerosis, Alzheimer’s disease, and Parkinson’s disease. We identified the meninges as an important site of antigen presentation and CD8 T cell activation and clonal expansion and found that T cell activation in the meninges is a requirement for infiltration into the CNS. We further found that natural killer cells have the potential to negatively regulate T cell activation locally in the meninges through direct killing and are one of many regulatory mechanisms that work to control excessive neuroinflammation.
Fundamental parameters governing the transmission of conjugative plasmids

Plasmid conjugation is a major route for the dissemination of antibiotic resistances and adaptive genes among bacterial populations. Obtaining precise conjugation rates is thus key to understanding how antibiotic resistances spread. Plasmid conjugation is typically modeled as a density-dependent process, where the formation of new transconjugants is proportional to the rate of encounters between donor and recipient bacteria. By analyzing conjugation dynamics at different cell concentrations, here we show that this assumption only holds at low bacterial densities. At higher concentrations, plasmid transmission switches from a density-dependent process to a frequency-dependent one. Mathematical modeling revealed that plasmid conjugation follows a Hollinǵs Type II functional response, characterized by two fundamental parameters: the searching rate and the handling time. These parameters represent, respectively, the density and frequency dependent limits for plasmid transmission. Our results demonstrate that these parameters are characteristic of the transfer machinery, rather than the entire plasmid genome, and that they are robust to environmental and transcriptional perturbation. Precise parameterization of plasmid conjugation will contribute to a better understanding of the propagation dynamics of antimicrobial resistances.
Pooled tagging and hydrophobic targeting of endogenous proteins for unbiased mapping of unfolded protein responses

System-level understanding of proteome organization and function requires methods for direct visualization and manipulation of proteins at scale. We developed an approach enabled by high-throughput gene tagging for the generation and analysis of complex cell pools with endogenously tagged proteins. Proteins are tagged with HaloTag to enable visualization or direct perturbation. Fluorescent labeling followed by in situ sequencing and deep learning-based image analysis identifies the localization pattern of each tag, providing a bird’s-eye-view of cellular organization. Next, we use a hydrophobic HaloTag ligand to misfold tagged proteins, inducing spatially restricted proteotoxic stress that is read out by single cell RNA sequencing. By integrating optical and perturbation data, we map compartment-specific responses to protein misfolding, revealing inter-compartment organization and direct crosstalk, and assigning proteostasis functions to uncharacterized genes. Altogether, we present a powerful and efficient method for large-scale studies of proteome dynamics, function, and homeostasis.
The common self-polar triangle of conics and its applications to computer vision

In projective geometry, the common self-polar triangle has often been used to discuss the location relationship of two planar conics. However, there are few researches on the properties of the common self-polar triangle, especially when the two planar conics are special conics. In this thesis, the properties of the common self-polar triangle of special conics are studied and their applications to computer vision are presented. Specifically, the applications focus on the two topics of the computer vision: camera calibration and homography estimation. This thesis first studies the common self-polar triangle of two sphere images and also that the common self-polar triangle of two concentric circles, and exploits its properties to solve the problem of camera calibration. For the sphere images, by recovering the constraints on the imaged absolute conic from the vertices of the common self-polar triangles, a novel method for estimating the intrinsic parameters of a camera from an image of three spheres has been developed. For the other case of concentric circles, it is shown in this thesis that the imaged circle center and the vanishing line of the support plane can be recovered simultaneously. Furthermore, many orthogonal vanishing points can be obtained from the common self-polar triangles. Consequently, two novel calibration methods have been developed. Based on our method, one of the state-of-the-art calibration methods has been well interpreted. This thesis then studies the common self-polar triangle of two separate ellipses, and applies it to planar homography estimation. For two images of the separate ellipses, by inducing four corresponding lines from the common self-polar triangle,
Transfer Learning on Kinyarwanda Tweets Sentiment Analysis

Pretrained models available on platforms such as Hugging Face have become a valuable resource for the machine learning community, particularly for nat-ural language processing tasks. In this study, we evaluated the performance of Kinyarwanda and English pretrained models for sentiment analysis of Kin-yarwanda tweets through transfer learning using Hugging Face pretrained models and Trainer for implementation. We have found that the fine tuned English pre-trained models for translated Kinyarwanda tweets dataset using Google translate outperformed Kinyarwanda fine tuned pretrained models
Relationship between convective bursts and the rapid intensification of Typhoon Mujigae (2015)

Typhoon Mujigae was the strongest typhoon in 2015, and underwent an extreme rapid intensification (RI). The RI of Mujigae was reasonably reproduced by a 72‐hr control simulation. By enhancing two updrafts in the eye wall, connective bursts (CBs) were found to have played an important role in the formation of the two warming cores in the middle and upper troposphere, respectively, which triggered and maintained the RI of Typhoon Mujigae. Meanwhile, CBs induced the upper‐tropospheric potential vorticity anomaly and caused the downward transfer of momentum from the upper to the middle troposphere which was also conducive to the RI of the typhoon. Sensitivity tests confirmed our conclusion.
3D pose estimation using low resolution time-of-flight (ToF) cameras

Time-of-Flight(ToF) cameras developed rapidly in recent years, but its low resolution limits its application. In this paper, we propose a new method to estimate the object’s 3D pose using a single ToF camera. We use the Iterative Closest Points(ICP) algorithm based on the Normal Aligned Radial Feature(NARF) to achieve the pose estimation of the target. The experimental results show that this method can accurately estimate the 3D pose of an object using a low-resolution ToF camera.
Subpixel displacement measurement method based on the combination of particle swarm optimization and gradient algorithm

Abstract. A subpixel displacement measurement method based on the combination of particle swarm optimization (PSO) and gradient algorithm (GA) was proposed for accuracy and speed optimization in GA, which is a subpixel displacement measurement method better applied in engineering practice. An initial integer-pixel value was obtained according to the global searching ability of PSO, and then gradient operators were adopted for a subpixel displacement search. A comparison was made between this method and GA by simulated speckle images and rigid-body displacement in metal specimens. The results showed that the computational accuracy of the combination of PSO and GA method reached 0.1 pixel in the simulated speckle images, or even 0.01 pixels in the metal specimen. Also, computational efficiency and the antinoise performance of the improved method were markedly enhanced.
Improved Enspart for DNA Motif Prediction

In our previous work we proposed ENSPART-an ensemble method for DNA motif discovery which partitions input dataset into several equal size subsets runs by several distinct tools for candidate motif prediction. The candidate motifs obtained from different data subsets are merged to obtain the final motifs. Nevertheless, the original ENSPART has several limitations: (1) the same background sequences are used for the calculation of Receiver Operating Cost (ROC) of motifs obtained from different datasets. This causes bias because different datasets might have different background distribution; (2) it does not consider the duplication of a motif and its reverse complement. This causes many redundant motifs in the result set which requires filtering. In this work, we extended the original ENSPART to solve those two issues. For the first issue, we employed background sequences that is based on the distribution of bases in the input sequences. As for the second issue, we employ a "triple" merging strategy to reduce redundant motifs. Our evaluation results indicate that the two improvements obtain better AUC values in comparison to the original implementation.
PRECIS‐projected increases in temperature and precipitation over Canada

In this study, high‐resolution projections of temperature and precipitation changes over Canada were developed through the Providing Regional Climates for Impact Studies (PRECIS) model under Representative Concentration Pathways (RCPs). In detail, the PRECIS model was employed to conduct simulations for the historical period over the entirety of Canada, driven by the boundary conditions from both ERA‐Interim (1979–2011) and HadGEM2‐ES (1959–2005). The performance of PRECIS simulations in reproducing historical climatology of Canada was then validated through comparison with observed temperature and precipitation over the baseline period (1986–2005). The boundary conditions from HadGEM2‐ES under RCP4.5 and RCP8.5 was used to drive PRECIS for simulating climatic variables over Canada for the period of 2006–2099. Future climate projections of temperature and precipitation as well as their extreme indices over two time‐slices (i.e. 2046–2065 and 2076–2095) were extracted and analysed. The results could help investigate how the regional climate over Canada will respond to global warming as well as the spatio‐temporal characteristics of plausible climate changes in the Canadian context. The validation results demonstrate that the PRECIS model is effective in reproducing the historical climatological patterns of annual mean temperature and total precipitation across Canada. Projections of temperature and precipitation for the two future periods indicate that there will be an apparent increasing pattern over Canada. The projected changes derived in this study can provide decision‐makers with valuable information to evaluate possible impacts on economic, social and environmental sectors at regional and local scales.
Edit at your own risk: evaluating the robustness of edited models to distribution shifts

The current trend toward ever-larger models makes standard retraining procedures an ever-more expensive burden. For this reason, there is growing interest in model editing, which enables computationally inexpensive, interpretable, post-hoc model modifications. While many model editing techniques are promising, research on the properties of edited models is largely limited to evaluation of validation accuracy. The robustness of edited models is an important and yet mostly unexplored topic. In this paper, we employ recently developed techniques from the field of deep learning robustness to investigate both how model editing affects the general robustness of a model, as well as the robustness of the specific behavior targeted by the edit. We find that edits tend to reduce general robustness, but that the degree of degradation depends on the editing algorithm and layers chosen. Motivated by these observations we introduce a new model editing algorithm, 1-layer interpolation (1-LI), which uses weight-space interpolation to navigate the trade-off between editing task accuracy and general robustness.