Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusing Conditional Submodular GAN and Programmatic Weak Supervision

Programmatic Weak Supervision (PWS) and generative models serve as crucial tools that enable researchers to maximize the utility of existing datasets without resorting to laborious data gathering and manual annotation processes. PWS uses various weak supervision techniques to estimate the underlying class labels of data, while generative models primarily concentrate on sampling from the underlying distribution of the given dataset. Although these methods have the potential to complement each other, they have mostly been studied independently. Recently, WSGAN proposed a mechanism to fuse these two models. Their approach utilizes the discrete latent factors of InfoGAN for the training of the label models and leverages the class-dependent information of the label models to generate images of specific classes. However, the disentangled latent factor learned by the InfoGAN may not necessarily be class specific and hence could potentially affect the label model's accuracy. Moreover, the prediction of the label model is often noisy in nature and can have a detrimental impact on the quality of images generated by GAN. In our work, we address these challenges by (i) implementing a noise-aware classifier using the pseudo labels generated by the label model, (ii) utilizing the prediction of the noise-aware classifier for training the label model as well as generation of class-conditioned images. Additionally, We also investigate the effect of training the classifier with a subset of the dataset within a defined uncertainty budget on pseudo labels. We accomplish this by formalizing the subset selection problem as submodular maximization with a knapsack constraint on the entropy of pseudo labels. We conduct experiments on multiple datasets and demonstrate the efficacy of our methods on several tasks vis-a-vis the current state-of-the-art methods. Our implementation is available at https://github.com/kyrs/subpws-gan
Finite GK-dimensional Nichols algebras over the quaternion group $Q_8$

We contribute to the classification of Hopf algebras with finite Gelfand-Kirillov dimension, GK-dimension for short, through the study of Nichols algebras over Q 8 ,the quaternion group . We find all the irreducible Yetter-Drinfeld modules V over Q 8 , and determine which Nichols algebras B(V ) of V are finite GK-dimensional, and which Nichols algebras B(V ) of V are finite dimensional.
Multi-agent Reinforcement Learning: A Comprehensive Survey

The prevalence of multi-agent applications pervades various interconnected systems in our everyday lives. Despite their ubiquity, the integration and development of intelligent decision-making agents in a shared environment pose challenges to their effective implementation. This survey delves into the domain of multi-agent systems (MAS), placing a specific emphasis on unraveling the intricacies of learning optimal control within the MAS framework, commonly known as multi-agent reinforcement learning (MARL). The objective of this survey is to provide comprehensive insights into various dimensions of MAS, shedding light on myriad opportunities while highlighting the inherent challenges that accompany multi-agent applications. We hope not only to contribute to a deeper understanding of the MAS landscape but also to provide valuable perspectives for both researchers and practitioners. By doing so, we aim to facilitate informed exploration and foster development within the dynamic realm of MAS, recognizing the need for adaptive strategies and continuous evolution in addressing emerging complexities in MARL.
Reliability of the Maximal Lactate Accumulation Rate in Rowers

Abstract The maximal lactate accumulation rate (VLamax) has been linked to lactic anaerobic performance. Hence, accurate and reliable assessment is crucial in sport-specific performance testing. Thus, between-day reliability data of rowing-specific VLamax assessment was examined. Seventeen trained rowers (eight females and nine males; 19.5±5.2 yrs; 1.76±0.08 m; 70.2±8.9 kg; V̇O 2 max: 54±13 ml/min/kg) performed 20-s sprint tests on two separate days (one week apart) on a rowing ergometer. VLamax, peak lactate concentration, time to peak lactate, and mean rowing power were measured. Good to excellent intraclass correlation coefficients (ICCs), low standard error of measurement (SEM), and acceptable levels of agreement (LoAs; 90% confidence interval) for VLamax (ICC=0.85; SEM=0.02 mmol/L/s; LoA±0.09 mmol/L/s), peak lactate (ICC=0.88; SEM=0.3 mmol/L; LoA±1.4 mmol/l), time to peak lactate (ICC=0.92; SEM=0.1 min; LoA±0.5 min), and mean rowing power (ICC=0.98; SEM=3 W; LoA±39 W) were observed. In addition, VLamax was highly correlated (r=0.96; p ≤0.001) to rowing power. Thus, VLamax and sprint performance parameters can be measured highly reliably using this sport-specific sprint test in rowing.
Neuroanatomical changes observed over the course of a human pregnancy

Pregnancy is a period of profound hormonal and physiological change experienced by millions of women annually, yet the neural changes unfolding in the maternal brain throughout gestation have not been studied in humans. Leveraging precision imaging, we mapped neuroanatomical changes in an individual from preconception through two years postpartum. Pronounced decreases in gray matter volume and cortical thickness paired with increases in white matter microstructure were evident across the brain, with few regions untouched by the transition to motherhood.
A Repetition-Based Triplet Mining Approach for Music Segmentation

Contrastive learning has recently appeared as a well-suited method to find representations of music audio signals that are suitable for structural segmentation. However, most existing unsupervised training strategies omit the notion of repetition and therefore fail at encompassing this essential aspect of music structure. This work introduces a triplet mining method which explicitly considers repeating sequences occurring inside a music track by leveraging common audio descriptors. We study its impact on the learned representations through downstream music segmentation. Because musical repetitions can be of different natures, we give further insight on the role of the audio descriptors employed at the triplet mining stage as well as the trade-off existing between the quality of the triplets mined and the quantity of unlabelled data used for training. We observe that our method requires less non-annotated data while remaining competitive against other unsupervised methods trained on a larger corpus.
Small molecule inhibitor binds to NLRP3 and prevents inflammasome activation

Despite recent advances in the mechanism of oxidized DNA activating NLRP3, the molecular mechanism and consequence of oxidized DNA associating with NLRP3 remains unknown. Cytosolic NLRP3 binds oxidized DNA which has been released from the mitochondria, which subsequently triggers inflammasome activation. Human glycosylase (hOGG1) repairs oxidized DNA damage which inhibits inflammasome activation. The fold of NLRP3 pyrin domain contains amino acids and a protein fold similar to hOGG1. Amino acids that enable hOGG1 to bind and cleave oxidized DNA are conserved in NLRP3. We found NLRP3 could bind and cleave oxidized guanine within mitochondrial DNA. The binding of oxidized DNA to NLRP3 was prevented by small molecule drugs which also inhibit hOGG1. These same drugs also inhibited inflammasome activation. Elucidating this mechanism will enable design of drug memetics that treat inflammasome pathologies, illustrated herein by NLRP3 pyrin domain inhibitors which suppressed interleukin-1β (IL-1β) production in macrophages. One-Sentence Summary NLRP3 cleaves oxidized DNA and small molecule drug binding inhibits inflammasome activation.
Memory reactivation during sleep does not act holistically on object memory

Memory reactivation during sleep is thought to facilitate memory consolidation. Most sleep reactivation research has examined how reactivation of specific facts, objects, and associations benefits their overall retention. However, our memories are not unitary, and not all features of a memory persist in tandem over time. Instead, our memories are transformed, with some features strengthened and others weakened. Does sleep reactivation drive memory transformation? We leveraged the Targeted Memory Reactivation technique in an object category learning paradigm to examine this question. Participants (20 female, 14 male) learned three categories of novel objects, where each object had unique, distinguishing features as well as features shared with other members of its category. We used a real-time EEG protocol to cue the reactivation of these objects during sleep at moments optimized to generate reactivation events. We found that reactivation improved memory for distinguishing features while worsening memory for shared features, suggesting a differentiation process. The results indicate that sleep reactivation does not act holistically on object memories, instead supporting a transformation process where some features are enhanced over others.
Structural basis of a regulatory switch in mammalian complex I

Respiratory complex I powers oxidative phosphorylation in mammalian mitochondria by using the reducing potential of NADH to reduce ubiquinone-10 and drive protons across the inner mitochondrial membrane. High-resolution cryoEM structures have provided a molecular framework for complex I catalysis, but controversies about how to assign functional properties to the states identified in single-particle analyses are preventing progress on its energy-converting mechanism. Here, we combine precise biochemical definition with high-resolution cryoEM structures in the phospholipid bilayer of coupled vesicles and show that the closed and open states observed in mammalian complex I preparations are components of the deactive transition that occurs during ischaemia. Populations of the cryoEM open state and biochemical deactive state match exactly. Deactivation switches the enzyme off, converting the closed state that is capable of rapid, reversible catalysis into an open, dormant state that is unable to start up in reverse. The deactive state is switched back on by slow priming reactions with NADH and ubiquinone-10. Thus, by developing a versatile membrane system to unite structure and function, we define the role of large-scale conformational transitions in complex I and establish a new gold standard for structure-based investigations of catalysis by energy-coupled proteins.
CCT3 drives Sorafenib resistance by inhibiting TFRC-mediated iron uptake in HCC

Sorafenib is commonly utilized in the management of advanced hepatocellular carcinoma (HCC). However, its efficacy in extending patients’ survival is hindered by the development of drug resistance. By employing protein posttranslational modification (PTM) omics, including acetylome, phosphoproteome, and ubiquitinome, in conjunction with genome-wide CRISPR/Cas9 knockout library screening, we have successfully identified chaperonin containing TCP1 subunit 3 (CCT3) as a key factor contributing to Sorafenib resistance. Furthermore, we observed a reduction in the ubiquitination of CCT3 at lysine 21 (K21) subsequent to Sorafenib treatment. This study provides evidence that CCT3 hinders the recycling of transferrin receptor protein 1 (TFRC) by interacting with alpha-actinin-4 (ACTN4), which is influenced by K6-linked ubiquitination on K21. Depleting CCT3 increased the susceptibility of cells to Sorafenib-induced ferroptosis, while reintroducing CCT3 through transfection restored resistance to ferroptosis. Additionally, impairing ACTN4 or TFRC depletion compromised CCT3’s ability to inhibit Sorafenib-induced ferroptosis. In summary, targeting CCT3 presents a potential strategy for overcoming Sorafenib resistance in HCC.
Choice anticipation as gated accumulation of sensory expectations

Expectations are combined with sensory information when making choices. Some models of the choice process have conceptualized expectations as trial by trial updates to baseline evidence in an accumulator framework. These models have been successful in explaining the influence of choice history across trials on reaction times and choice probabilities, however they do not account for variability in the delay interval within trials. Here, we derive a gated accumulator that models the onset of evidence accumulation as a combination of delayed sensory information and an expectation of sensory timing that changes within trials. To test how the delay intervals interact with trial by trial expectations, we designed a free choice saccade task where participants directed eye movements, as fast as possible, to either of two targets that appeared with variable delays and asynchronies. Despite being instructed to not to anticipate, participants responded prior to target onset on a subset of trials. We reasoned that anticipatory responses may reflect a trade-off between gating the onset of evidence accumulation prior to target onset and releasing this gate as target appearance became more likely. Using a choice history analysis, we found that anticipatory responses were more likely following repeated choices, despite task randomization, suggesting that the balance between anticipatory and sensory responses was driven by an expectation of sensory timing. By fitting the gated accumulator model to reaction time data, we determined that within-trial fluctuations in baseline evidence occurring prior to the onset of the target could explain the joint increase of anticipatory responses and faster sensory-guided responses as the delay period increased. Thus, we conclude that an expectation of sensory timing can shape how the accumulation of choice evidence is gated, allowing us to balance the costs of anticipation with lowering the amount of evidence required to trigger movement.
Inactivation of the Niemann Pick C1 cholesterol transporter 1 (NPC1) restricts SARS-CoV-2 infection

The Niemann Pick C1 (NPC1) protein is an intracellular cholesterol transporter located in the late endosome/lysosome (LE/Ly) and is involved in cholesterol mobilization. Loss-of-function mutations of the NPC1 gene lead to accumulation of cholesterol and sphingolipids in LE/Ly, resulting in severe fatal NPC1 disease. Cellular alterations associated with NPC1 inactivation affect both the integrity of lipid rafts and the endocytic pathway. Because the angiotensin-converting enzyme 2 (ACE2) and type 2 serine transmembrane protease (TMPRSS2) of the SARS-CoV-2 Spike (S) protein also localize to lipid rafts, we sought to investigate the hypothesis that NPC1 inactivation would generate an intrinsically unfavorable barrier to SARS-CoV-2 entry. In this study, we demonstrate that NPC1 pharmacological inactivation or CRISP/R-Cas mediated ablation of NPC1 dramatically reduced SARS-CoV-2 infectivity. More specifically, our findings demonstrate that pharmacological inactivation of NPC1 results in massive accumulation of ACE2 in the autophagosomal/lysosomal compartment. A >40-fold decrease in virus titer indicates that this effectively prevents VSV-Spike-GFP infection by impeding virus binding and entry. A similarly marked decrease in viral infectivity is observed in cells that had NPC1 expression genetically abrogated. These observations were further confirmed in a de novo SARS-CoV-2 infection paradigm, where cells were infected with the naturally pathogenic SARS-CoV-2. Overall, this work offers strong evidence that NPC1 function is essential for successful SARS-CoV-2 infection, thus implicating NPC1 as a potential therapeutic target in COVID-19 management. Significance A significant convergence exists between the cellular alterations associated with NPC1 inactivation and the mechanistic processes of SARS-CoV-2 infectivity. These alterations affect the integrity of lipid-enriched plasma membrane microdomains and the endocytic pathway. Furthermore, the cholesterol-regulated ACE2 receptor protein that facilitates SARS-Cov-2 viral binding and entry is targeted to the autophagolysosomal compartment upon NPC1 inactivation, thus hindering virus-host cell interaction. To our knowledge, this study provides the first evidence that NPC1 function represents a crucial factor for SARS-CoV-2 infection and suggests therapeutic opportunities.
Prenatal exposure to valproic acid reduces synaptic δ-catenin levels and disrupts ultrasonic vocalization in neonates

Valproic acid (VPA) is an effective and commonly prescribed drug for epilepsy and bipolar disorder. However, children born from mothers treated with VPA during pregnancy exhibit an increased incidence of autism spectrum disorder (ASD). Although VPA may impair brain development at the cellular level, the mechanism of VPA-induced ASD has not been completely addressed. A previous study has found that VPA treatment strongly reduces δ-catenin mRNA levels in cultured human neurons. δ-catenin is important for the control of glutamatergic synapses and is strongly associated with ASD. VPA inhibits dendritic morphogenesis in developing neurons, an effect that is also found in neurons lacking δ-catenin expression. We thus hypothesize that prenatal exposure to VPA significantly reduces δ-catenin levels in the brain, which impairs glutamatergic synapses to cause ASD. Here, we found that prenatal exposure to VPA markedly reduced δ-catenin levels in the brain of mouse pups. VPA treatment also impaired dendritic branching in developing mouse cortical neurons, which was reversed by elevating δ-catenin expression. Prenatal VPA exposure significantly reduced synaptic AMPA receptor levels and postsynaptic density 95 (PSD95) in the brain of mouse pups, indicating dysfunctions in glutamatergic synaptic transmission. VPA exposure also significantly altered ultrasonic vocalization (USV) in newly born pups when they were isolated from their nest. Moreover, VPA-exposed pups show impaired hypothalamic response to isolation, which is required to produce animals’ USVs following isolation from the nest. Therefore, these results suggest that VPA-induced ASD pathology can be mediated by the loss of δ-catenin functions. Highlights Prenatal exposure of valproic acid (VPA) in mice significantly reduces synaptic δ-catenin protein and AMPA receptor levels in the pups’ brains. VPA treatment significantly impairs dendritic branching in cultured cortical neurons, which is reversed by increased δ-catenin expression. VPA exposed pups exhibit impaired communication such as ultrasonic vocalization. Neuronal activation linked to ultrasonic vocalization is absent in VPA-exposed pups. The loss of δ-catenin functions underlies VPA-induced autism spectrum disorder (ASD) in early childhood.
Sounds Out of Pläce? Score-Independent Detection of Conspicuous Mistakes in Piano Performances

In piano performance, some mistakes stand out to listeners, whereas others may go unnoticed. Former research concluded that the salience of mistakes depended on factors including their contextual appropriateness and a listener’s degree of familiarity to what is being performed. A conspicuous error is considered to be an area where there is something obviously wrong with the performance, which a listener can detect regardless of their degree of knowledge of what is being performed. Analogously, this paper attempts to build a score-independent conspicuous error detector for standard piano repertoire of beginner to intermediate students. We gather three qualitatively different piano playing MIDI data: (1) 103 sight-reading sessions for beginning and intermediate adult pianists with formal music training, (2) 245 performances by presumably late-beginner to early-advanced pianists on a digital piano, and (3) 50 etude performances by an advanced pianist. The data was annotated at the regions considered to contain conspicuous mistakes. Then, we use a Temporal Convolutional Network to detect the sites of such mistakes from the piano roll. We investigate the use of two pre-training methods to overcome data scarcity: (1) synthetic data with procedurally-generated mistakes, and (2) training a part of the model as a piano roll auto-encoder. Experimental evaluation shows that the TCN performs at an F-measure of 0.78 without pretraining for sight-reading data, but the proposed pretraining steps improve the F-measure on performance and etude data, approaching the agreement between human raters on conspicuous error labels. Importantly, we report on the lessons learned from this pilot study, and what should be addressed to continue this research direction.
Identifying pure urban image spectra using a learning urban image spectral archive (LUISA)

In this study a learning urban image spectral archive (LUISA) has been developed, that overcomes the issue of an incomplete spectral library and can be used to derive scene-specific pure material spectra. It consists of a well described starting spectral library (LUISA-A) and a tool to derive scene-based pure surface material spectra (LUISA-T). The concept is based on a three-stage approach: (1) Comparing hyperspectral image spectra with LUISA-A spectra to identify scene-specific pure materials, (2) extracting unknown pure spectra based on spatial and spectral metrics and (3) provides the framework to implement new surface material spectra into LUISA-A. The spectral comparison is based on several similarity measures, followed by an object- and spectral-based ruleset to optimize and categorize potentially new pure spectra. The results show that the majority of pure surface materials could be identified using LUISA-A. Unknown spectra are composed of mixed pixels and real pure surface materials which could be distinguished by LUISA-T.
Context Neural Nets Semantic Similarities Knowledge Feature Level Model Level Motivation for PDP system : features + models Knowledge Enhanced Embeddings Texts KBs

Solving Winograd Schema Problems Quan Liu†, Hui Jiang‡, Zhen-Hua Ling†, Xiaodan Zhu, Si Wei§, Yu Hu†§ † National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China, Hefei, Anhui, China ‡ Department of Electrical Engineering and Computer Science, York University, Canada ` National Research Council Canada, Ottawa, Canada § iFLYTEK Research, Hefei, China emails: quanliu@mail.ustc.edu.cn, hj@cse.yorku. ca, zhling@ustc.edu.cn, zhu2048@gmail.com siwei@iflytek.com, yuhu@iflytek.com Abstract
Effective seed sterilization methods require optimization across maize genotypes

Studies of plant-microbe interactions using synthetic microbial communities (SynComs) often require the removal of seed-associated microbes by seed sterilization prior to inoculation to provide gnotobiotic growth conditions. A diversity of seed sterilization protocols have been developed in the past and have been used on different plant species with various amounts of validation. From these studies it has become clear that each plant species requires its own optimized sterilization protocol. It has, however, so far not been tested if the same protocol works equally well for different varieties and seed sources of one plant species. We evaluated six seed sterilization protocols on two different varieties (Sugar Bun & B73) of maize. All unsterilized maize seeds showed fungal growth upon germination on filter paper, highlighting the need for a sterilization protocol. A short sterilization protocol with hypochlorite and ethanol was sufficient to prevent fungal growth on Sugar Bun germinants, however a longer protocol with heat treatment and germination in fungicide was needed to obtain clean B73 germinants. This difference may have arisen from the effect of either genotype or seed source. We then tested the protocol that performed best for B73 on three additional maize genotypes from four sources. Seed germination rates and fungal contamination levels varied widely by genotype and geographic source of seeds. Our study shows that consideration of both variety and seed source is important when optimizing sterilization protocols and highlights the importance of including seed source information in plant-microbe interaction studies that use sterilized seeds.