Miracle-dz/UniFAR-FTUs huggingface dataset:
IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation

Recent research has made significant progress in designing fusion modules for audio-visual speech separation. However, they predominantly focus on multi-modal fusion at a single temporal scale of auditory and visual features without employing selective attention mechanisms, which is in sharp contrast with the brain. To address this issue, We propose a novel model called Intra- and Inter-Attention Network (IIANet), which leverages the attention mechanism for efficient audio-visual feature fusion. IIANet consists of two types of attention blocks: intra-attention (IntraA) and inter-attention (InterA) blocks, where the InterA blocks are distributed at the top, middle and bottom of IIANet. Heavily inspired by the way how human brain selectively focuses on relevant content at various temporal scales, these blocks maintain the ability to learn modality-specific features and enable the extraction of different semantics from audio-visual features. Comprehensive experiments on three standard audio-visual separation benchmarks (LRS2, LRS3, and VoxCeleb2) demonstrate the effectiveness of IIANet, outperforming previous state-of-the-art methods while maintaining comparable inference time. In particular, the fast version of IIANet (IIANet-fast) has only 7% of CTCNet's MACs and is 40% faster than CTCNet on CPUs while achieving better separation quality, showing the great potential of attention mechanism for efficient and effective multimodal fusion.
Metriplectic Heavy Top: An Example of Geometrical Dissipation

Recently, Morrison and Updike showed that many dissipative systems are naturally described as possessing a Riemann curvature-like bracket, which similar to the Poisson bracket, generates the dissipative equations of motion once suitable generators are chosen. In this paper, we use geometry to construct and explore the dynamics of these new brackets. Specifically, we consider the dynamics of a heavy top with dissipation imposed by a Euclidian contravariant curvature. We find that the equations of motion, despite their rather formal motivation, naturally generalize the energy-conserving dissipation considered by Matterasi and Morrison. In particular, with suitable initial conditions, we find that the geometrically motivated equations of motion cause the top to relax to rotation about a principal axis.
Temporal Interest Network for User Response Prediction

User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are among the most critical. Many works have revealed that a user's behavior reflects her interest in the candidate item, owing to the semantic or temporal correlation between behaviors and the candidate. While the literature has individually examined each of these correlations, researchers have yet to analyze them in combination, that is, the semantic-temporal correlation. We empirically measure this correlation and observe intuitive yet robust patterns. We then examine several popular user interest models and find that, surprisingly, none of them learn such correlation well. To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic encoding, to represent behaviors and the target. Furthermore, we conduct explicit 4-way interaction by deploying target-aware attention and target-aware representation to capture both semantic and temporal correlation. We conduct comprehensive evaluations on two popular public datasets, and our proposed TIN outperforms the best-performing baselines by 0.43% and 0.29% on GAUC, respectively. During online A/B testing in Tencent's advertising platform, TIN achieves 1.65% cost lift and 1.93% GMV lift over the base model. It has been successfully deployed in production since October 2023, serving the WeChat Moments traffic. We have released our code at https://github.com/zhouxy1003/TIN.
Galactic Archaeology: Tracing the Milky Way’s Formation and Evolution through Stellar Populations

Galactic archaeology represents a multidisciplinary approach aimed at unraveling the intricate history of the Milky Way galaxy through the study of its stellar populations. This abstract delves into the significance of galactic archaeology as a vital tool for understanding the formation and evolution of the Milky Way. By examining the distribution, kinematics, chemical compositions, and ages of stars within the galaxy, researchers gain insights into the dynamic processes that have shaped its structure over billions of years. Stellar populations serve as invaluable relics of past epochs, preserving clues about the conditions prevalent during their formation. The utilization of spectroscopic and photometric data has enabled the classification and analysis of stars, allowing astronomers to discern distinct populations and unveil their origin stories. Through these studies, the emergence of the Milky Way's various components, such as the thin and thick disk, halo, and bulge, becomes discernible.
Comment on"Origin of correlated isolated flat bands in copper-substituted lead phosphate apatite"

In this comment on"Origin of correlated isolated flat bands in copper-substituted lead phosphate apatite"(arXiv:2307.16892 [cond-mat.supr-con]), we discuss the flat half-occupied two-band manifold that appears in $\mathrm{Pb}_9\mathrm{Cu}(\mathrm{PO}_4)_6(\mathrm{OH})_2$ when using a semilocal DFT functional. We argue that the flat band is an artifact of the functional's overestimation of the energy of the oxygen p states in the valence band. When using the HSE hybrid functional, the energy of the oxygen p states is reduced, and the copper-derived manifold splits into one fully occupied and one empty band. While these results do not rule out the possibility of superconductivity in doped LK-99, they do predict that stoichiometric $\mathrm{Pb}_9\mathrm{Cu}(\mathrm{PO}_4)_6(\mathrm{OH})_2$ is an insulator, not a superconductor. Furthermore, we have shown that future first-principles studies of these materials should employ hybrid functionals or other advanced methods to ensure that the oxygen-derived valence-band energies are correctly described.
Nonresponse Bias Analysis in Longitudinal Studies: A Comparative Review with an Application to the Early Childhood Longitudinal Study

Longitudinal studies are subject to nonresponse when individuals fail to provide data for entire waves or particular questions of the survey. We compare approaches to nonresponse bias analysis (NRBA) in longitudinal studies and illustrate them on the Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011 (ECLS‐K:2011). Wave nonresponse with attrition often yields a monotone missingness pattern, and the missingness mechanism can be missing at random (MAR) or missing not at random (MNAR). We discuss weighting, multiple imputation (MI), incomplete data modelling and Bayesian approaches to NRBA for monotone patterns. Weighting adjustments can be effective when the constructed weights are correlated with the survey outcome of interest. MI allows for variables with missing values to be included in the imputation model, yielding potentially less biased and more efficient estimates. We add offsets in the MAR results to provide sensitivity analyses to assess MNAR deviations. We conduct NRBA for descriptive summaries and analytic model estimates in the ECLS‐K:2011 application. The strength of evidence about our NRBA depends on the strength of the relationship between the fully observed variables and the key survey outcomes, so the key to a successful NRBA is to include strong predictors.
SAMD1 suppresses epithelial-mesenchymal transition (EMT) pathways in pancreatic ductal adenocarcinoma

SAMD1 (SAM-domain containing protein 1), a CpG island-binding protein, plays a pivotal role in the repression of its target genes. Despite its significant correlation with outcomes in various tumor types, the role of SAMD1 in cancer has remained largely unexplored. In this study we focused on pancreatic ductal adenocarcinoma (PDAC) and revealed that SAMD1 acts as a repressor of genes associated with epithelial-mesenchymal transition (EMT). Upon deletion of SAMD1 in PDAC cells, we observed significantly increased migration rates. SAMD1 exerts its effects by binding to specific genomic targets, including CDH2, encoding N-cadherin, which emerged as a driver of enhanced migration upon SAMD1 knockout. Furthermore, we discovered the FBXO11-containing E3 ubiquitin ligase complex as an interactor of SAMD1. FBXO11 ubiquitinates SAMD1 within its DNA-binding winged helix domain and inhibits SAMD1 chromatin binding genome-wide. High FBXO11 expression in PDAC is associated with poor prognosis and increased expression of EMT-related genes, underlining an antagonistic relationship between SAMD1 and FBXO11. In summary, our findings provide new insights into the regulation of EMT-related genes in PDAC, shedding light on the intricate role of SAMD1 and its interplay with FBXO11 in this cancer type.
Fiber Recognition Algorithm Based on Improved Mask RCNN

In response to the application requirements of identifying and classifying multiple types of fibers, this paper proposes a fiber recognition algorithm based on improved Mask RCNN to achieve recognition and classification of multiple types of fibers, reduce the labor cost of fiber inspection, and improve inspection efficiency and quality. Firstly, a data augmentation strategy is adopted, which combines three data augmentation methods: RandomFlip, RandomCrop, and Cutout to achieve the best increase in network performance; Subsequently, a multi-scale training strategy is introduced to improve the model's training efficiency while also enhancing its robustness to scale; Finally, the attention mechanism module of convolutional blocks is added to solve the problem of low recognition and classification accuracy caused by small differences in fine-grained granularity between certain fiber classes. The experimental results show that the algorithm achieves a recognition and classification accuracy of 97.87% on the test set by introducing techniques such as data augmentation, multi-scale training, and CBAM, significantly improving the recognition and classification accuracy of various fiber targets.
An online sensate focus application to treat sexual desire discrepancy in intimate relationships: contrasting case studies

Abstract The present study provided an initial evaluation of an online, therapist-free sex therapy application for people struggling with sexual desire discrepancy in their relationship. In the mixed-methods case study, we provided an account of four users (two who improved and two who did not) who engaged in the intervention for eight weeks; the users answered weekly questions about their progress and completed an interview at the end of the study. We found that the intervention was very effective for some users with hope and confidence being related to higher improvement. Also seeing the problem as shared rather than one person’s issue helped make improvements. Communication (or lack thereof) was an important contributor to both the problem and the progress. The application can be used as a standalone treatment for sexual desire discrepancy or in combination with a therapist. LAY SUMMARY The manuscript describes the experience of four individuals who used an online sex therapy app for eight weeks to help with mismatched sexual desire in their relationship. Having hope things would get better, seeing the problem as shared, and beginning to communicate about sex helped improve outcomes.
Acute or chronic depletion of macrophages in the dorsal root ganglia induces neuropathic pain after unilateral cervical spinal cord injury

The inflammatory response at the spinal cord injury (SCI) epicenter and heightened macrophage presence in the dorsal root ganglia (DRG) has been well characterized after SCI and correlates with neuropathic pain. CCL2, a chemokine that acts as a macrophage chemoattractant and neuromodulator, is implicated in pain development, however, the role of the CCL2-CCR2 axis in the development of pain after SCI has not been explored. Here, we examined the role of CCL2-CCR2 signaling in macrophage recruitment to the DRG as well as the prolonged presence of macrophages in the DRG on the development and persistence of pain after SCI. Adult female Sprague-Dawley rats received a moderate, unilateral C5 contusion. Sandwich ELISA revealed that CCL2 is upregulated in the ipsilesional C7 and C8 DRGs in the first 24 hours post injury (hpi) and returns to naïve levels by 72 hpi. To prevent monocyte-derived macrophage recruitment to the DRG, additional SCI rats received vehicle or INCB3344, a CCR2 antagonist, intravenously at the time of SCI and at 24 and 48 hpi. INCB3344 administration induced transient forepaw allodynia at 7dpi in nearly all rats (88%) compared to only 33% in vehicle controls that resolves partially by 28 dpi, as measured by von Frey and mechanical conflict avoidance paradigms. As expected, qPCR analyses of whole DRG revealed that INCB3344 reduced macrophage markers and inflammatory cytokines in the ipsilesional C7 and C8 DRGs at 7 dpi compared to vehicle treated rats. By 28 dpi, there were no significant differences between INCB3344 or vehicle-treated groups, indicating that SCI-induced macrophage presence in the DRG is delayed by INCB3344 treatment. Moreover, gene expression of markers of macrophage polarity and cytokines suggest a pro-inflammatory environment in the DRG at 28dpi. DRG macrophage ablation via liposomal clodronate at 21dpi did not ameliorate hypersensitive pain behavior, though their ablation did reduce paw withdrawal thresholds in SCI rats that did not previously demonstrate pain behavior. Collectively, these data suggest that driving macrophages to a pro-reparative phenotype may be a viable and effective analgesic strategy that acts by modulating both the immune response and the experience of pain.
Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning

Resprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The prediction of resprouting patterns on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree’s resprouting after pruning purely based on their empirical knowledge and a visual check of the tree’s geometry. In this study, we explore in how far such predictions can also be made by algorithms, especially using machine learning. Table-topped annually pruned Platanus × hispanica trees at a nursery were documented with terrestrial LiDAR scanners in two consecutive years. Topological structures for these trees were abstracted from point clouds by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts. The accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with GaussianNB model is 82.1% but its balanced accuracy is reduced to 42.9%. The results were validated with a separate evaluation dataset. It proves a feasibility in predicting resprouting patterns after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research.
Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization

Domain randomization (DR) is widely used in reinforcement learning (RL) to bridge the gap be-tween simulation and reality by maximizing its average returns under the perturbation of environmental parameters. However, even the most complex simulators cannot capture all details in reality due to ﬁnite domain parameters and sim-pliﬁed physical models. Additionally, the existing methods often assume that the distribution of domain parameters belongs to a speciﬁc family of probability functions, such as normal distributions, which may not be correct. To overcome these limitations, we propose a new approach to DR by rethinking it from the perspective of adversarial state perturbation , without the need for reconﬁguring the simulator or relying on prior knowledge about the environment. We also address the issue of over-conservatism that can occur when perturbing agents to the worst states during training by introducing a Relaxed State-Adversarial Algorithm that simultaneously maximizes the average-case and worst-case returns. We evaluate our method by comparing it to state-of-the-art methods, providing experimental results and theoretical proofs to verify its effectiveness. Our source code and appendix are available at https://github.com/sophialien/RAPPO.
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization

Mean-field theory is widely used in theoretical studies of neural networks. In this paper, we analyze the role of depth in the concentration of mean-field predictions for Gram matrices of hidden representations in deep multilayer perceptron (MLP) with batch normalization (BN) at initialization. It is postulated that the mean-field predictions suffer from layer-wise errors that amplify with depth. We demonstrate that BN avoids this error amplification with depth. When the chain of hidden representations is rapidly mixing, we establish a concentration bound for a mean-field model of Gram matrices. To our knowledge, this is the first concentration bound that does not become vacuous with depth for standard MLPs with a finite width.
Unveiling nesting strategies: Investigating visual concealment and nest-site selection in Yellow-wattled Lapwings, Vanellus malabaricus (Boddaert, 1783)

Ground-nesting birds such as Yellow-wattled Lapwing, Vanellus malabaricus (Boddaert, 1783) employ several nesting and behavioral strategies to mitigate nest predation, which greatly influences their reproductive success and survival. Yet the breeding ecology of Yellow-wattled Lapwing has received relatively little research attention despite the species’ widespread presence in the Indian subcontinent. The objective of this study was to investigate the significance and impact of two visual concealment strategies commonly utilized by open ground-nesting birds: visual concealment through vegetative characteristics and camouflage using Yellow-wattled Lapwing as the model organism. We also assessed the nest-site selection of Yellow-wattled Lapwings in relation to vegetation cover and food availability, as well as their choice of nest substrate by using behavioral sampling, quadrat sampling, and digital image analysis techniques. We found that variation in hatching success could not be attributed to the analyzed camouflage mechanisms (disruptive markings and pattern complexity matching). Furthermore, the nesting pairs exhibited a preference for areas with high vegetation cover and low food availability while selecting nesting sites. We also showed that this species might exhibit an active behavioral preference for selecting nest substrates. It is important to note that this study was conducted during a single nesting season for the Yellow-wattled Lapwings, and further temporal replicates are necessary to validate and establish the findings over the long term.
DiPPI: A curated dataset for drug-like molecules in protein-protein interfaces

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large dataset for drug-like molecules in protein interfaces. We further present DiPPI (Drugs in Protein-Protein Interfaces), a two-module website to facilitate the search for such molecules and their properties by exploiting our dataset in drug repurposing studies. In the interface module of the website, we extracted several properties of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we curated a list of drug-like small molecules and FDA-approved drugs from various databases and extracted those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski’s rules and various molecular descriptors, are also calculated and made available on the website to guide the selection of drug molecules. Our dataset contains 534,203 interfaces for 98,632 proteins, of which 55,135 are detected to bind to a drug-like molecule. 2,214 drug-like molecules are deposited on our website, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data; and is freely available at http://interactome.ku. edu.tr:8501.
Chromosome-level genome assembly of navel orange cv. Gannanzao (Citrus sinensis Osbeck cv. Gannanzao)

Navel orange cv. Gannanzao is a variant of the navel orange cv. Newhall (C. sinensis Osbeck cv. Newhall) that exhibits an earlier maturation, making it commercially valuable. However, the underlying mechanism underneath its early maturation remains unknown. To address this question, we conducted genome sequencing and de novo assembly of navel orange cv. Gannanzao. The assembled genome sequence is 334.57 Mb in length with a GC content of 31.48%. It comprises 318 contigs (N50 = 3.23 Mb) and 187 scaffolds (N50 = 31.86 Mb). The BUSCO test demonstrates 94.6% completeness. The annotation revealed 23,037 gene models, 164.95 Mb of repetitive sequences, and 2,554 ncRNA. Comparative analysis identified 323 fruit ripening-related genes in navel orange cv. Gannanzao genome, while navel orange cv. Newhall genome contained 345 such genes. These genes were organized into 320 orthologous gene families, with 30.3% of them exhibiting differences in gene copy numbers between the two genomes. Additionally, we identified 15 fruit ripening-related genes that have undergone adaptive evolution, suggesting their potential role in advancing fruit maturation in navel orange cv. Gannanzao. Whole genome sequencing and annotation of navel orange cv. Gannanzao provides a valuable resource to unravel the early maturation mechanism of citrus and enriches the genomic resources for citrus research.
Functional Structure Recognition of Scientific Documents in Information Science

The recognition of the functional structure can help to understand the logical structure and content structure of the text, understand the text from a deep level, and further realize the academic big data analysis and data mining. In this study, the academic full text is taken as the research object, and the papers published in JASIST journals from 2010 to 2020 are selected to conduct a comparison experiment on the four models of Prompt, SciBERT, LSTM and TextCNN. The results showed that Prompt model had the best recognition effect.