Miracle-dz/UniFAR-FTUs huggingface dataset:
DSDF: Coordinated look-ahead strategy in multi-agent reinforcement learning with noisy agents

Existing methods of Multi-Agent Reinforcement learning, involving Centralized Training and Decentralized execution, attempt to train the agents towards learning a pattern of coordinated actions to arrive at optimal joint policy. However, during the execution phase, if some of the agents degrade and perform noisy actions (not the same actions suggested by policy) to varying degrees, the above methods provide poor coordination. In this paper, we show how such random noise in agents, which could be a result of the degradation or aging of robots, can add to the uncertainty in coordination and thereby contribute to unsatisfactory global rewards. In such a scenario, the agents which are in accordance with the policy have to understand the behavior and limitations of the noisy agents while the noisy agents have to plan in cognizance of their limitations. In our proposed method, Deep Stochastic Discount Factor (DSDF), based on the degree of degradation the algorithm tunes the discount factor for each agent uniquely, thereby altering the global planning of the agents. Moreover, given the degree of degradation in some agents is expected to change over time, our method provides a framework under which such changes can be incrementally addressed without extensive retraining. Results on benchmark environments show the efficacy of the DSDF approach when compared with existing approaches.
SCE: Shared Concept Extractor to Explain a CNN's Classification Dynamics

To better understand how accurate opaque black box models work, it is necessary to explain their internal workings in terms of human-interpretable image sub-regions known as concepts. This explanation will provide insights into how these models perceive the sharedness of concepts across related classes, as frequently observed in the real world. With this objective in mind, the proposed work aims to leverage an incremental Non-negative Matrix Factorization technique to extract shared concepts in a memory-efficient manner, thereby reflecting the sharedness of concepts across classes. The relevance of the extracted concepts towards prediction, as well as the encoding of primitive image aspects such as color, texture, and shape by the concept, will be estimated after training the concept extractor. This approach reduces training overhead and simplifies the explanation pipeline, enabling the elucidation of various concepts - some genuine, some spurious - on which different black box architectures trained on the Imagenet dataset group and distinguish related classes.
Objects’ perceived meaningfulness predicts both subjective memorability judgments and actual memory performance

ABSTRACT Memorability studies have revealed a limitation in our ability to accurately judge which images are memorable. Conversely, metacognitive research suggests that individuals can utilize cues to reliably assess their memory performance. Here, we investigated two important stimulus cues potentially underlying subjective memorability, and their relation to actual memory performance. Participants encoded 200 real-world object images while providing Judgements of Learning (JOLs), in which they estimated the likelihood of remembering each image. Subsequently, they completed an old/new memory recognition test on these stimuli. All stimuli were priorly rated by an independent group of participants for their perceived meaningfulness and visual complexity. Results indicated that participants' metacognitive judgements exhibited a relatively good resolution, allowing them to distinguish well-remembered from less-remembered images. Furthermore, analyses conducted at the image level demonstrated that JOLs significantly predicted memory performance, with the meaningfulness of the images emerging as a crucial factor associated with JOLs of both participants and their actual memory performance. The visual complexity factor was correlated only with the actual memory performance. Collectively, these findings suggest that memory performance is closely associated with the meaning of objects. Critically, individuals possess a subjective sense of images' memorability, which is at least partially mediated by perceived meaningfulness.
Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a “meta-matching” framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants (“meta-matching finetune” and “meta-matching stacking”) from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.
Viral proteins activate PARIS-mediated tRNA degradation and viral tRNAs rescue infection

Viruses compete with each other for limited cellular resources, and some viruses deliver defense mechanisms that protect the host from competing genetic parasites. PARIS is a defense system, often encoded in viral genomes, that is composed of a 53 kDa ABC ATPase (AriA) and a 35 kDa TOPRIM nuclease (AriB). Here we show that AriA and AriB assemble into a 425 kDa supramolecular immune complex. We use cryo-EM to determine the structure of this complex which explains how six molecules of AriA assemble into a propeller-shaped scaffold that coordinates three subunits of AriB. ATP-dependent detection of foreign proteins triggers the release of AriB, which assembles into a homodimeric nuclease that blocks infection by cleaving the host tRNALys. Phage T5 subverts PARIS immunity through expression of a tRNALys variant that prevents PARIS-mediated cleavage, and thereby restores viral infection. Collectively, these data explain how AriA functions as an ATP-dependent sensor that detects viral proteins and activates the AriB toxin. PARIS is one of an emerging set of immune systems that form macromolecular complexes for the recognition of foreign proteins, rather than foreign nucleic acids.
FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.
Hybrid cancer stem cells utilise vascular tracks for collective streaming invasion in a metastasis-on-a-chip device

Cancer stem cells (CSCs) drive cancer metastatic dissemination. They do not do so in a vacuum, and the important influence of the tumour microenvironment (TME) on metastatic dissemination is becoming increasingly recognised. Therapeutic targeting of CSC-TME interactions may be a promising route to suppression of tumour metastasis. However, we must first understand how interactions with the TME influence CSC metastatic dissemination. To achieve this understanding, there is a need for experimental models that enable the analysis of dynamic interactions at single cell resolution within a complex environment. To this end, we utilise a metastasis-on-a-chip device to produce a 3D in vitro model of CSC interaction with a developing microvasculature, that is amenable to precise imaging and real time studies at single cell resolution. We show that the invasive phenotype of oral squamous cell carcinoma (OSCC) cells is markedly altered when in proximity to a microvasculature, with a switch to a hybrid CSC phenotype that undergoes collective streaming invasion. Mechanistically, ECM compression by the developing vasculature creates an environment that is refractory to cancer invasion, whilst leaving abandoned vascular tracks that can be utilised by hybrid CSCs for collective streaming invasion. Human tissue studies identify streaming invasion in association with vascularised regions in OSCC specimens. These findings elucidate the influence of the vasculature on CSC metastatic dissemination in OSCC, and the role of hybrid CSC invasion plasticity in overcoming this TME constraint.
BCI Toolbox: An Open-Source Python Package for the Bayesian Causal Inference Model

Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.
ExoJ: an ImageJ2/Fiji plugin for automated spatiotemporal detection and analysis of exocytosis

Exocytosis is a dynamic physiological process that enables the release of biomolecules to the surrounding environment via the fusion of membrane compartments to the plasma membrane. Understanding its mechanisms is crucial, as defects can compromise essential biological functions. The development of pH-sensitive optical reporters alongside fluorescence microscopy enables the assessment of individual vesicle exocytosis events at the cellular level. Manual annotation represents, however, a time-consuming task, prone to selection biases and human operational errors. Here, we introduce ExoJ, an automated plugin based on ImageJ2/Fiji. ExoJ identifies user-defined genuine populations of exocytic events, recording quantitative features including intensity, apparent size and duration. We designed ExoJ to be fully user-configurable, making it suitable to study distinct forms of vesicle exocytosis regardless of the imaging quality. Our plugin demonstrates its capabilities by showcasing distinct exocytic dynamics among tetraspanins and vesicular SNAREs protein reporters. Assessment of performance on synthetic data demonstrated ExoJ is a robust tool, capable to correctly identify exocytosis events independently of signal-to-noise ratio conditions. We propose ExoJ as a standard solution for future comparative and quantitative studies of exocytosis.
Predictive coding in the auditory brainstem

Predictive coding is an influential concept in sensory and cognitive neuroscience. It is often understood as involving top-down prediction of bottom-up sensory patterns, but the term also applies to feed-forward predictive mechanisms, for example in the retina. Here, I discuss a recent model of low-level predictive processing in the auditory brainstem that is of the feed-forward flavor. Past sensory input from the cochlea is delayed and compared with the current input, via a predictive model that is tuned with the objective of minimizing prediction error. This operation is performed independently within each peripheral channel, with parameters determined from information within that channel. The result is a sensory representation that is invariant to a certain class of interfering sounds (harmonic, quasi-harmonic, or spectrally sparse), thus contributing to Auditory Scene Analysis. The purpose of this paper is to discuss that model in the light of predictive coding, and examine how it might fit within a wider hierarchical model that supports the perceptual representation of objects and events in the world.
Rare genetic variation in Fibronectin 1 (FN1) protects against APOEe4 in Alzheimer’s disease

The risk of developing Alzheimer’s disease (AD) significantly increases in individuals carrying the APOEε4 allele. Elderly cognitively healthy individuals with APOEε4 also exist, suggesting the presence of cellular mechanisms that counteract the pathological effects of APOEε4; however, these mechanisms are unknown. We hypothesized that APOEε4 carriers without dementia might carry genetic variations that could protect them from developing APOEε4-mediated AD pathology. To test this, we leveraged whole genome sequencing (WGS) data in National Institute on Aging Alzheimer’s Disease Family Based Study (NIA-AD FBS), Washington Heights/Inwood Columbia Aging Project (WHICAP), and Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA) cohorts and identified potentially protective variants segregating exclusively among unaffected APOEε4 carriers. In homozygous unaffected carriers above 70 years old, we identified 510 rare coding variants. Pathway analysis of the genes harboring these variants showed significant enrichment in extracellular matrix (ECM)-related processes, suggesting protective effects of functional modifications in ECM proteins. We prioritized two genes that were highly represented in the ECM-related gene ontology terms, (FN1) and collagen type VI alpha 2 chain (COL6A2) and are known to be expressed at the blood-brain barrier (BBB), for postmortem validation and in vivo functional studies. The FN1 and COL6A2 protein levels were increased at the BBB in APOEε4 carriers with AD. Brain expression of cognitively unaffected homozygous APOEε4 carriers had significantly lower FN1 deposition and less reactive gliosis compared to homozygous APOEε4 carriers with AD, suggesting that FN1 might be a downstream driver of APOEε4-mediated AD-related pathology and cognitive decline. To validate our findings, we used zebrafish models with loss-of-function (LOF) mutations in fn1b – the ortholog for human FN1. We found that fibronectin LOF reduced gliosis, enhanced gliovascular remodeling and potentiated the microglial response, suggesting that pathological accumulation of FN1 could impair toxic protein clearance, which is ameliorated with FN1 LOF. Our study suggests vascular deposition of FN1 is related to the pathogenicity of APOEε4, LOF variants in FN1 may reduce APOEε4-related AD risk, providing novel clues to potential therapeutic interventions targeting the ECM to mitigate AD risk.
Spatio-temporal variation of the source mechanism of the 2008 great Wenchuan earthquake

A linear inversion technique based on assumption of constant rupture velocity and fixed source time functions of sub-events is proposed in this paper for inverting teleseismic waveform data to image spatio-temporal variation of source mechanism of large earthquakes, and applied to the 2008 Wenchuan M_S8. 0 earthquake, obtaining its image of the spatio-temporal variation of source mechanism by inverting 48 worldwide stations of long period waveform data. The image shows that the mainly thrust mechanism appeared in the southwest segment of the fault, and the strike-slip component was getting more and more with the fault extending northeastwards, with the turning point at which the strike-slip component became larger than the thrust-slip component appearing around 190 km northeast to the epicenter. In order to inspect the validity of the used method and reliability of the inverted results a specially designed numerical test was performed. The inspection indicated that the inversion method proposed in this paper was valid and the inverted results regarding the great Wenchuan earthquake were trustable (except those on one segment where the long-period component is weak). A comparison shows that the inverted results were coincident with the field-investigation results.
Towards understanding the glacial impact on sedimentary basins using numerical methods

The topographic relief of the southern Barents Sea shelf was subjected to major changes during the past 1.5 million years (Ma) mostly due to sediment redistribution driven by glacial activity. This paper addresses the problem of the Pleistocene bathymetry evolution by using a numerical modelling approach that considers an aspect of regional isostasy influence on the relief development. The results show that most of the prominent bathymetrical features were initiated prior to the first documented shelf edge glaciations at around 1.5 Ma. During the Early Pleistocene the Barents Sea shelf was close to sea level with some areas elevated to about 300 m. Most of the shelf experienced up to 200 m topography reduction during the Early to Middle Pleistocene (1.5 0.7 Ma). Later during the Middle Pleistocene–Present (0.7 – 0.0 Ma) the relief deepened by approximately 0 to 250 m. The results suggest that the present-day topography of the southern Barents Sea is the result of glacial activity affected by the regional isostatic component developed in response to selective trough erosion and significant deposition at the Barents Sea margins.
Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery

ABSTRACT This study focuses on the statistical characterization of ice conditions (extent, sea ice occurrence probability (SIOP), and length of ice season) in the Gulf of Riga, Baltic Sea, using remote-sensing data. The optical remote-sensing data with 250 m resolution acquired by a Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002–2011 were used for statistical characterization of sea ice. A method based on bimodal histogram analysis of remote-sensing reflectance data was developed to discriminate ice from water. In general, ice extent information obtained from MODIS data agrees with the official ice chart data (synthetic aperture radar (SAR) and in situ measurements) and multi-sensor product containing data from microwave and infrared instruments (R2 >0.83). However, in case of severe winters and extremely mild winters there are differences in the dates when maximum ice extent is registered. MODIS data can be used for detailed analysis of ice extent in specific basins of Baltic Sea. Depending on the year, the ice season length in the Gulf of Riga ranged from 68 to 146 days, and the maximum ice extent varied greatly from 329 to 15,350 km2. SIOP and number of ice days increased significantly in areas where the depth is less than 15 m. Based on negative-degree days and ice cover characteristics (SIOP and ice season length), three winter scenarios were defined: severe (2003, 2006, 2010, and 2011), medium (2004 and 2005), and mild (2007, 2008, and 2009).
Estimation of Shallow Water Depth Using HJ-1C S-band SAR Data

This paper presents a shallow water depth estimation methodology using S-band Synthetic Aperture Radar (SAR) data from the HJ-1C satellite. It is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. A two-scale Bragg scattering model is used to describe the imaging process of long waves by SAR. By computing the Fast Fourier Transformation (FFT) for the selected sub image, wavelength and direction of the long wave can be retrieved from the two-dimensional (2D) spectrawith wave tracking technol-ogy. Shallow water depths are then obtained from the linear dispersion relation with the calculated angular wave frequency obtained from other sources or first guesses of initial water depths or wave periods. Applicability and effectiveness are tested in the near-shore area of the Fujian province, China. Comparison between the derived results and water depths from an Electronic Navigational Chart (ENC) indicates that HJ-1C SAR is capable of higher resolution underwater topography detection, and the methodology can be used for shallow water depth estimation with good accuracy. The average absolute error and average relative error of the estimated results is 0·86 m and 11·05%, respectively.
Engaging Minds - How Gamified Chatbots Can Support and Motivate Learners in Digital Education

Blended and online learning is growing, and self-regulated learning is becoming more relevant. Most often, students struggle with organizing their own learning processes, lose focus or procrastinate. Keeping learners motivated and engaged can be a real challenge. Therefore, we present gamified chatbots as a potential solution. On the one hand, chatbots can provide a more engaging learning experience. On the other hand, gamification can provide motivational incentives to keep learners engaged and motivated. So far, not many studies have elaborated on how gamification can be effectively used to make a chatbot interaction more engaging or improve the learning experience. This study uses an experimental approach to distinguish how a combination of badges and a progress bar can support and motivate learners to stay engaged with their learning activities. We elaborate on the effects of gamified chatbots and support practitioners with guidance on how to design gamified chatbots in education