Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS: Explorable and Explainable Feature Extraction Framework for Multivariate Time-Series Classification

We demonstrate EFFECTS, an automated system for explorable and explainable feature extraction for multivariate time series classification. EFFECTS has a twofold contribution: (1) It significantly facilitates the exploration of MTSC data, and (2) it generates informative yet intuitive and explainable features to be used by the classification model. EFFECTS first mines the MTS data and extracts a set of interpretable features using an optimized transform-slice-aggregate process. To evaluate the quality of EFFECTS features, we gauge how well each feature distinguishes between every two classes, and how well they characterize each single class. Users can then explore the MTS data via the EFFECTS Explorer, which facilitates the visual inspection of important features, dimensions, and time slices. Last, the user can use the top features for each class when building a classification pipeline. We demonstrate EFFECTS on several real-world MTSC datasets, inviting the audience to investigate the data via EFFECTS Explorer and obtain initial insights on the time series data. Then, we will show how EFFECTS features are used in an ML model, and obtain accuracy that is on par with state-of-the-art MTSC models that do not optimize on explainability.
A Multi-Modality Framework for Drug-Drug Interaction Prediction by Harnessing Multi-source Data

Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to adverse physiological reactions and increasing mortality rates of patients. Therefore, predicting potential DDI has always been an important and challenging issue in medical health applications. Owing to the extensive pharmacological research, we can get access to various drug-related features for DDI predictions; however, most of the existing works on DDI prediction do not incorporate comprehensive features to analyze the DDI patterns. Despite the high performance that the existing works have achieved, the incomplete and noisy information generated from limited sources usually leads to sub-optimal performance and poor generalization ability on the unknown DDI pairs. In this work, we propose a holistic framework, namely Multi-modality Feature Optimal Fusion for Drug-Drug Interaction Prediction (MOF-DDI), that incorporates the features from multiple data sources to resolve the DDI predictions. Specifically, the proposed model jointly considers DDIs literature descriptions, biomedical knowledge graphs, and drug molecular structures to make the prediction. To overcome the issue induced by directly aggregating features in different modalities, we bring a new insight by mapping the representations learned from different sources to a unified hidden space before the combination. The empirical results show that MOF-DDI achieves a large performance gain on different DDI datasets compared with multiple state-of-the-art baselines, especially under the inductive setting.
Contrastive Keyword Extraction from Versioned Documents

Versioned documents are common in many situations and play a vital part in numerous applications enabling an overview of the revisions made to a document or document collection. However, as documents increase in size, it gets difficult to summarize and comprehend all the changes made to versioned documents. In this paper, we propose a novel research problem of contrastive keyword extraction from versioned documents, and introduce an unsupervised approach that extracts keywords to reflect the key changes made to an earlier document version. In order to provide an easy-to-use comparison and summarization tool, an open-source demonstration is made available which can be found at https://contrastive-keyword-extraction.streamlit. app/
XCreation: A Graph-based Crossmodal Generative Creativity Support Tool

Creativity Support Tools (CSTs) aid in the efficient and effective composition of creative content, such as picture books. However, many existing CSTs only allow for mono-modal creation, whereas previous studies have become theoretically and technically mature to support multi-modal innovative creations. To overcome this limitation, we introduce XCreation, a novel CST that leverages generative AI to support cross-modal storybook creation. Nevertheless, directly deploying AI models to CSTs can still be problematic as they are mostly black-box architectures that are not comprehensible to human users. Therefore, we integrate an interpretable entity-relation graph to intuitively represent picture elements and their relations, improving the usability of the underlying generative structures. Our between-subject user study demonstrates that XCreation supports continuous plot creation with increased creativity, controllability, usability, and interpretability. XCreation is applicable to various scenarios, including interactive storytelling and picture book creation, thanks to its multimodal nature.
Fair&Share: Fast and Fair Multi-Criteria Selections

Traditional multi-criteria selection methods are the leading approach for selecting a set of candidates when multiple criteria determine selection relevancy. For instance, hiring platforms combine candidates' proximity, skills, and years of experience to build shortlists for recruiters. While these methods succeed in efficiently selecting candidates, their chosen set may unfairly affect marginalized candidate groups (e.g., race or gender). Bridging the gap between traditional fairness-unaware multi-criteria selection and contemporary fairness interventions, we characterize the open problem of fair multi-criteria selection. We design Fair&Share the first efficient fairness-tunable multi-criteria selection method. Fair&Share supports several fair representation notions. The key to Fair&Share is the design of its group-aware utility objective. Fair&Share uses a novel fairness calibration component to provide a user-friendly tuning mechanism for controlling the balance between selection relevancy (utility) and representation fairness. Our fairness-focused selection policy iteratively builds the result set by prioritizing candidates as aiding either the fair representation or the shared overall utility goals. We prove the optimality of Fair&Share, meaning that Fair&Share selects the best possible candidates such that the desired fair representation is achieved. Our experimental study demonstrates that Fair&Share achieves the best fairness and utility performance of state-of-the-art alternatives adapted to this new problem while taking a fraction of the time.
'Choose your Data Wisely': Active Learning based Selection with Multi-Objective Optimisation for Mitigating Stereotypes

Data-driven (deep) learning methods has led to parameterised abstractions of the data, often leading to stereotype societal biases in their predictions, e.g., predicting more frequently that women are weaker than men, or that African Americans are more likely to commit crimes than Caucasians. Standard approaches of mitigating such stereotypical biases from deep neural models include modifying the training dataset (pre-processing), or adjusting the model parameters with a bias-specific objective (in-processing). In our work, we approach this bias mitigation from a different perspective - that of an active learning-based selection of a subset of data instances towards training a model optimised for both effectiveness and fairness. Specifically speaking, the imbalances in the attribute value priors can be alleviated by constructing a balanced subset of the data instances with two selection objectives - first, of improving the model confidence of the primary task itself (a standard practice in active learning), and the second, of taking into account the parity of the model predictions with respect to the sensitive attributes, such as gender and race etc. We demonstrate that our proposed selection function achieves better results in terms of both the primary task effectiveness and fairness. The results are further shown to improve when this active learning-based data selection is combined with an in-process method of multi-objective training.
BioWeave: Weaving Thread-Based Sweat-Sensing On-Skin Interfaces

There has been a growing interest in developing and fabricating wearable sweat sensors in recent years, as sweat contains various analytes that can provide non-invasive indications of various conditions in the body. Although recent HCI research has been looking into wearable sensors for understanding health conditions, textile-based wearable sweat sensors remain underexplored. We present BioWeave, a woven thread-based sweat-sensing on-skin interface. Through weaving single-layer and multi-layer structures, we combine sweat-sensing threads with versatile fiber materials. We identified a design space consisting of colorimetric and electrochemical sensing approaches, targeting biomarkers including pH, glucose, and electrolytes. We explored 2D and 3D weaving structures for underexplored body locations to seamlessly integrate sweat-sensing thread into soft wearable interfaces. We developed five example applications to demonstrate the design capability offered. The BioWeave sensing interface can provide seamless integration into everyday textile-based wearables and offers the unobtrusive analysis of health conditions.
DRL4IR: 4th Workshop on Deep Reinforcement Learning for Information Retrieval

\AcIR is one of the most important fields to help users find relevant information. The interaction between IR systems and users can be naturally formulated as a decision-making problem. In the last decade, deep reinforcement learning (DRL) has become a promising direction to utilize the high model capacity of deep learning to improve long-term gains. On the one hand, there have been emerging research works focusing on leveraging DRL for IR tasks while the fundamental information theory under DRL settings, the principle of RL methods for IR tasks, or the experimental evaluation protocols of DRL-based IR systems, has not been deeply investigated. On the other hand, the emerging ChatGPT also provides new insights and challenges for DRL-based IR. Therefore, we propose the fourth DRL4IR workshop1 at CIKM 2023, which provides a venue for both academia researchers and industry practitioners to present the recent advances of DRL-based IR system, to foster novel research, interesting findings, and new applications of DRL for IR. We will pay more attention to fundamental research topics and recent application advances such as ChatGPT, with an expectation of over 300 workshop participants.
BiverWordle: Visualizing Stock Market Sentiment with Financial Text Data and Trends

While financial forums are increasingly significant in financial analysis, current visualization tools do not properly exploit their text data. To address this, we present BiverWordle, a novel tool that reveals the relationship between market sentiment and firm trends. BiverWordle integrates candlestick chart, ThemeRiver, and Wordle with text classification and sentiment analysis techniques to decode market dynamics from textual sources, such as shareholder opinions and firm announcements. With the application of a Voting model to the manually labeled data, we achieved an accuracy of approximately 64%. BiverWordle facilitates the extraction of shareholder insights from sparse comments and provides a visual method for historical stock trend analysis, which we validated with three distinct stock trends. Resources are accessible at https://github.com/Brian-Lei-XIA/BiverWordle.
Structural covariation between cerebellum and cerebral cortex is atypically modulated by thalamus in autism spectrum disorder

Despite its strong interconnectivity with the cerebral cortex, the influence of the human cerebellum on neocortical structure and its role in the development of neuropsychiatric disorders is unclear. Because cerebellar damage in early postnatal life creates a high risk for autism spectrum disorder (ASD), we investigated inter-relationships in cerebello-cerebral morphology. Leveraging a large structural brain MRI dataset in neurotypical children (n=375) and those diagnosed with ASD (n=373), we examined whether volumetric variation in cerebellar structure across individuals was correlated with neocortical variation during development, modeling the thalamus as a moderating coupling factor. We found negative covariation between cerebellar cortical regions and thalamic/sensorimotor neocortical regions, and positive covariation between thalamic and sensorimotor neocortical regions. This pattern aligned with the major disynaptic path of cerebellar inhibition to thalamocortical excitation. Examining the dependence of this structural covariation on ASD diagnosis, we found that neurotypical and ASD children displayed inverted hemispheric biases. In ASD, the thalamus moderated structural associations between the left cerebellum and right sensorimotor cortex. For neurotypical children, right cerebellum and left sensorimotor cortex were coupled. Notably, structural coupling between cerebellum, thalamus, and neocortex was strongest in younger childhood and waned by early adolescence, a time during which behavioral differences were smallest between typically developing and autistic children. In addition to the sensorimotor cortex, cerebellar dentate nuclei in ASD displayed greater coupling to broad neocortical regions, and these nuclei related to cognitive function differently such that greater dentate nuclear volume was associated with greater behavioral impairment in ASD but not in controls. Graph analyses demonstrated that the cerebello-thalamocortical network was more densely and less prolifically interconnected in ASD than in typical development. Taken as a whole, our study reveals a developmental interplay between the cerebellum, thalamus, and neocortex which differs in ASD from neurotypical children. This pattern is consistent with a model of ASD in which early developmental influences of cerebellar output on brain maturation are specifically moderated by cerebello-thalamocortical pathways.
Effects of intensified training with insufficient recovery on joint level and single muscle fibre mechanical function: The role of myofibrillar Ca2+ sensitivity

Intense exercise training with insufficient recovery is associated with reductions in neuromuscular performance. However, it is unclear how single muscle fibre mechanical function and myofibrillar Ca2+ sensitivity contribute to these impairments. We investigated the effects of overload training on joint-level neuromuscular performance and cellular-level mechanical function. Fourteen athletes (4 female, 10 male) underwent a 3-week intensified training protocol consisting of ∼140% of their regular training hours with three additional high-intensity training sessions per week. Neuromuscular performance of the knee extensors was assessed via maximum voluntary contraction (MVC) force, electrically evoked twitch contractions, and a force-frequency relationship. Muscle biopsies were taken from the vastus lateralis to assess single fibre mechanical function. Neither MVC force nor twitch parameters were altered following intensified training (all p>0.05), but a rightward shift in the force-frequency curve was observed with a 6-27% reduction in force at low-frequencies (5-20Hz, all p<0.05). In single fibres, maximal force output was not reduced following intensified training, but there was a rightward shift in the force-pCa curve driven by a 6% reduction in Ca2+ sensitivity as indicated by a lower pCa50 value (i.e., higher [Ca2+]) across fibre types (Pre=6.477±0.157, Post=6.088±0.480, p<0.05). These data indicate intensified training leads to impaired Ca2+ sensitivity at the single fibre level, which in part explains impaired neuromuscular function at the joint level during lower frequencies of activation. This is an important consideration for athletes, as performance is often assessed at maximal levels of activation, and these underlying impairments in force generation may be less obvious. New & Noteworthy Intense exercise training with insufficient recovery leads to impaired muscle contractile performance. These impairments often manifest at lower frequencies of muscle stimulation, termed prolonged low-frequency force depression. Impaired myofibrillar calcium sensitivity has been suggested as a potential mechanism of prolonged low-frequency force depression. Our work shows that impaired calcium sensitivity of single muscle fibres coincided with joint level prolonged low-frequency force depression after intense exercise training with insufficient recovery.
Microglial depletion worsens lesion in female but not male C57BL/6J mice after P10 hypoxia-ischemia

Background Rodent models for perinatal hypoxic ischemic (HI) encephalopathy have reported sex differences such as males having larger brain lesions than females after the same injury. Microglia, the resident immune cells of the brain that have distinct developmental trajectories and gene expression patterns by sex, likely play a different role in males and females following HI. However, there is conflicting literature on whether depletion of microglia worsens or improves HI-induced lesions, and whether this differs by sex. We tested the effect of pharmacologic microglial depletion on lesion size and developmental milestones. Methods C57BL/6J mouse pups received daily intraperitoneal injections from postnatal day 7 (P7) to P12 of either 25 mg/kg PLX3397 (a CSF1R inhibitor) or vehicle (Veh). At P10, pups either underwent Hypoxic-Ischemic (HI) insult using a modified Vannucci procedure, or a Sham insult. This resulted in four groups tested (Veh-Sham, Veh-HI, PLX-Sham, PLX-HI); all groups included both male and female mice. Behavioral testing was performed both pre-HI (forelimb grasping [P8, P9]) and post-HI (open field traversal [P12], gross behavior and appearance in homecage and new environment [P13]). Brains were collected at P13, fixed, and sectioned, then either immunolabeled for Iba1 or stained with cresyl violet for injury scoring. Results Immunohistochemistry for Iba1 demonstrated >95% depletion of microglia at either P10 or P13 in all PLX3397-treated mice; depletion did not differ by sex. In the hippocampus, Female PLX-HI mice had worse cresyl violet lesion scores than Female Veh-HI mice; this was not true in male mice, where there was a trend in the opposite direction. Female PLX-HI mice also had worse lesion scores than Male PLX-HI mice. There were no differences among groups in forelimb grasp, open field traversal times, or weight gain. Conclusion Microglial depletion worsens HI-induced injury in female mice but not in male mice.
Comparative analyses of disease-linked missense mutations in the RNA exosome modeled in budding yeast reveal distinct functional consequences in translation

The RNA exosome is an evolutionarily conserved exoribonuclease complex that consists of a 3- subunit cap, a 6-subunit barrel-shaped core, and a catalytic base subunit. Missense mutations in genes encoding structural subunits of the RNA exosome cause a growing family of diseases with diverse pathologies, collectively termed RNA exosomopathies. The disease symptoms vary and can manifest as neurological defects or developmental disorders. The diversity of the RNA exosomopathy pathologies suggests that the different missense mutations in structural genes result in distinct in vivo consequences. To investigate these functional consequences and distinguish whether they are unique to each RNA exosomopathy mutation, we generated a collection of in vivo models using budding yeast by introducing pathogenic missense mutations in orthologous S. cerevisiae genes. We then performed a comparative RNA-seq analysis to assess broad transcriptomic changes in each mutant model. Three of the mutant models rrp4-G226D, rrp40-W195R and rrp46-L191H, which model mutations in the genes encoding structural subunits of the RNA exosome, EXOSC2, EXOSC3 and EXOSC5 showed the largest transcriptomic differences. Further analyses revealed shared increased transcripts enriched in translation or ribosomal RNA modification/processing pathways across the three mutant models. Studies of the impact of the mutations on translation revealed shared defects in ribosome biogenesis but distinct impacts on translation. Collectively, our results provide the first comparative analysis of several RNA exosomopathy mutant models and suggest that different RNA exosomopathy mutations result in in vivo consequences that are both unique and shared across each variant, providing more insight into the biology underlying each distinct pathology.
The Aspergillus fumigatus maiA gene contributes to cell wall homeostasis and fungal virulence

In this study, two distinct in vitro infection models of Aspergillus fumigatus, using murine macrophages (RAW264.7) and human lung epithelial cells (A549), were employed to identify the genes important for fungal adaptation during infection. Transcriptomic analyses of co-incubated Aspergillus uncovered 140 fungal genes up-regulated in common between both models that, when compared with a previously published in vivo transcriptomic study, allowed the identification of 13 genes consistently up-regulated in all three infection conditions. Among them, the maiA gene, responsible for a critical step in the L-phenylalanine degradation pathway, was identified. Disruption of maiA resulted in a mutant strain unable to complete the Phe degradation pathway, leading to an excessive production of pyomelanin when this amino acid served as the sole carbon source. Moreover, the disruption mutant exhibited noticeable cell wall abnormalities, with reduced levels of β-glucans within the cell wall. the maiA-1 mutant strain induced reduced inflammation in primary macrophages and displayed significantly lower virulence in a neutropenic mouse model of infection. This is the first study linking the A. fumigatus maiA gene to fungal cell wall homeostasis and virulence.
Aging differentially alters the transcriptome and landscape of chromatin accessibility in the male and female mouse hippocampus

Aging-related memory impairment and pathological memory disorders such as Alzheimer’s disease differ between males and females, and yet little is known about how aging-related changes in the transcriptome and chromatin environment differ between sexes in the hippocampus. To investigate this question, we compared the chromatin accessibility landscape and gene expression/alternative splicing pattern of young adult and aged mouse hippocampus in both males and females using ATAC-seq and RNA-seq. We detected significant aging-dependent changes in the expression of genes involved in immune response and synaptic function and aging-dependent changes in the alternative splicing of myelin sheath genes. We found significant sex-bias in the expression and alternative splicing of hundreds of genes, including aging-dependent female-biased expression of myelin sheath genes and aging-dependent male-biased expression of genes involved in synaptic function. Aging was associated with increased chromatin accessibility in both male and female hippocampus, especially in repetitive elements, and with an increase in LINE-1 transcription. We detected significant sex-bias in chromatin accessibility in both autosomes and the X chromosome, with male-biased accessibility enriched at promoters and CpG-rich regions. Sex differences in gene expression and chromatin accessibility were amplified with aging, findings that may shed light on sex differences in aging-related and pathological memory loss.