Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic-Motivated Sensor Management: Exploring with Physical Surprise

In modern complex physical systems, advanced sensing technologies extend the sensor coverage but also increase the difficulties of improving system monitoring capabilities based on real-time data availability. Traditional model-based methods of sensor management are limited to specific systems/settings, which can be challenged when system knowledge is intractable. Fortunately, the large amount of data collected in real-time allows machine learning methods to be a complement. Especially, reinforcement learning-based control is recognized for its capability to dynamically interact with systems. However, the direct implementation of learning methods easily overfits and results in inaccurate physics modeling for sensor management. Although physical regularization is a popular direction to bridge the gap, learning-based sensor control still suffers from convergence failure under highly complex and uncertain scenarios. This paper develops physics-embedded and self-supervised reinforcement learning for sensor management using an intrinsic reward. Specifically, the intrinsic-motivated sensor management (IMSM) constructs the local surprise information from the physical latent features, which captures hidden states in observations, and thus intrinsically motivates the agent to speed-up exploration. We show that the designs can not only relieve the lack of consistency with underlying physics/physical dynamics, but also adapt the global objective of maximizing monitoring capabilities to local environment changes. We demonstrate its effectiveness by experiments on physical system sensor control. The proposed model is implemented for the sensor management of unmanned vehicles and sensor rescheduling in complex/settled power systems, with or without observability constraints. Numerical results show that our model provides consistently higher threat detection accuracy and better observability recovery, as compared to existing methods.
Analyzing Online Transaction Networks with Network Motifs

Network motif is a kind of frequently occurring subgraph that reflects local topology in graphs. Although network motif has been studied in graph analytics, e.g., social network and biological network, it is yet unclear whether network motif is useful for analyzing online transaction network that is generated in applications such as instant messaging and e-commerce. In this work, we analyze online transaction networks from the perspective of network motif. We define vertex features based on size-2 and size-3 motifs, and introduce motif-based centrality measurements. We further design motif-based vertex embedding that integrates weighted motif counts and centrality measurements. Afterward, we implement a distributed framework for motif detection in large-scale online transaction networks. To understand the effectiveness of motif for analyzing online transaction network, we study the statistical distribution of motifs in various kinds of graphs in Tencent and assess the benefit of motif-based embedding in a range of downstream graph analytical tasks. Empirical results show that our proposed method can efficiently find motifs in large-scale graphs, help interpretability, and benefit downstream tasks.
Low-rank Nonnegative Tensor Decomposition in Hyperbolic Space

Tensor decomposition aims to factorize an input tensor into a number of latent factors. Due to the low-rank nature of tensor in real applications, the latent factors can be used to perform tensor completion in numerous tasks, such as knowledge graph completion and timely recommendation. However, existing works solve the problem in Euclidean space, where the tensor is decomposed into Euclidean vectors. Recent studies show that hyperbolic space is roomier than Euclidean space. With the same dimension, a hyperbolic vector can represent richer information (e.g., hierarchical structure) than a Euclidean vector. In this paper, we propose to decompose tensor in hyperbolic space. Considering that the most popular optimization tools (e.g, SGD, Adam) have not been generalized in hyperbolic space, we design an adaptive optimization algorithm according to the distinctive property of hyperbolic manifold. To address the non-convex property of the problem, we adopt gradient ascent in our optimization algorithm to avoid getting trapped in local optimal landscapes. We conduct experiments on various tensor completion tasks and the result validates the superiority of our method over these baselines that solve the problem in Euclidean space.
Learning Supplementary NLP Features for CTR Prediction in Sponsored Search

In sponsored search engines, pre-trained language models have shown promising performance improvements on Click-Through-Rate (CTR) prediction. A widely used approach for utilizing pre-trained language models in CTR prediction consists of fine-tuning the language models with click labels and early stopping on peak value of the obtained Area Under the ROC Curve (AUC). Thereafter the output of these fine-tuned models, i.e., the final score or intermediate embedding generated by language model, is used as a new Natural Language Processing (NLP) feature into CTR prediction baseline. This cascade approach avoids complicating the CTR prediction baseline, while keeping flexibility and agility. However, we show in this work that calibrating separately the language model based on the peak single model AUC does not always yield NLP features that give the best performance in CTR prediction model ultimately. Our analysis reveals that the misalignment is due to overlap and redundancy between the new NLP features and the existing features in CTR prediction baseline. In other words, the NLP features can improve CTR prediction better if such overlap can be reduced. For this purpose, we introduce a simple and general joint-training framework for fine-tuning of language models, combined with the already existing features in CTR prediction baseline, to extract supplementary knowledge for NLP feature. Moreover, we develop an efficient Supplementary Knowledge Distillation (SuKD) that transfers the supplementary knowledge learned by a heavy language model to a light and serviceable model. Comprehensive experiments on both public data and commercial data presented in this work demonstrate that the new NLP features resulting from the joint-training framework can outperform significantly the ones from the independent fine-tuning based on click labels. we also show that the light model distilled with SuKD can provide obvious AUC improvement in CTR prediction over the traditional feature-based knowledge distillation.
A Graph Learning Based Framework for Billion-Scale Offline User Identification

Offline user identification is a scenario that users use their bio-information like faces as identification in offline venues, which has been applied in many offline scenarios such as verification in banks, check-in in hotels and making a purchase in offline merchants. In such a scenario, designing an identification approach to do extremely accurate offline user identification is critical. Most scenarios use faces to identify users and previous algorithms are mainly based on visual features and computer-vision models. However, due to the large variations such as pose, illumination and occlusions in offline scenarios, it remains a challenging problem for existing computer-vision algorithms to get a satisfying accuracy in real-world scenarios. Furthermore, billion-scale candidate users also require high efficiency and high accuracy for the approach. In offline scenarios, users, venues and their different kinds of interactions can form a heterogeneous graph. Mining the graph can tell much information about users' offline habits and behaviors, which can be regarded as a great information supplement for user identification. In this paper, we elaborately design an offline identification framework considering two aspects. First, given a scanning face, we propose a 'local-global' retrieval mechanism to find one user from billion-scale candidate users. Second and most importantly, to make the verification between the scanning face, the retrieved user and the venue, we propose a novel Wide & Deep Based Graph Convolution Network to model both the visual information and the heterogeneous graph. Extensive offline and online A/B experimental results on a real-world industrial dataset demonstrate the effectiveness of our proposed approach. Nowadays, the whole approach has been deployed to serve billion-scale users to do offline identification in the industrial production environment.
Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks

Graph neural networks (GNN) are powerful tools in many web research problems. However, existing GNNs are not fully suitable for many real-world web applications. For example, over-smoothing may affect personalized recommendations and the lack of an explanation for the GNN prediction hind the understanding of many business scenarios. To address these problems, in this paper, we propose a new second-order continuous GNN which naturally avoids over-smoothing and enjoys better interpretability. There is some research interest in continuous graph neural networks inspired by the recent success of neural ordinary differential equations (ODEs). However, there are some remaining problems w.r. t. the prevailing first-order continuous GNN frameworks. Firstly, augmenting node features is an essential, however heuristic step for the numerical stability of current frameworks; secondly, first-order methods characterize a diffusion process, in which the over-smoothing effect w.r. t. node representations are intrinsic; and thirdly, there are some difficulties to integrate the topology of graphs into the ODEs. Therefore, we propose a framework employing second-order graph neural networks, which usually learn a less stiff transformation than the first-order counterpart. Our method can also be viewed as a coupled first-order model, which is easy to implement. We propose a semi-model-agnostic method based on our model to enhance the prediction explanation using high-order information. We construct an analog between continuous GNNs and some famous partial differential equations and discuss some properties of the first and second-order models. Extensive experiments demonstrate the effectiveness of our proposed method, and the results outperform related baselines.
Exploring the relationship between dispositional mindfulness and hoarding behavior: A moderated multi-mediation model

Hoarding behavior may not only interfere with hoarders’ daily lives but may also endanger the community. However, few studies have investigated the role of personality characteristics in hoarding behavior. We hypothesized that dispositional mindfulness would be negatively associated with hoarding behavior, and tested mechanisms and gender differences in this association. An online survey was conducted in a sample of 533 Chinese adults (262 women, Mage = 26.82; SD = 6.30). Regression-based analyses showed that mindfulness was associated with less hoarding behavior through higher self-esteem and lower emotion dysregulation. Moreover, gender moderated the mediating effect of emotion dysregulation in the association between mindfulness and hoarding behavior. Specifically, the indirect association was only significant for women. These findings provide a deeper understanding of how, why, and for whom dispositional mindfulness is negatively associated with hoarding behavior, they provide support for self-completion theory and the cognitive-behavioral model of hoarding, and they have heuristic value for future research.
Benefits of group compassion-focused therapy for treatment-resistant depression: A pilot randomized controlled trial

Major depression is one of the most common mental health problems worldwide. More than one-third of patients suffer from treatment-resistant depression (TRD). In this study, we explored the feasibility of group compassion-focused therapy (CFT) for TRD using a randomized controlled trial with two parallel groups. Eighteen participants were randomly allocated to the intervention group (CFT and usual care) and control group (usual care alone) and a participant in each group withdrew. Participants in the intervention group received a 1.5-h session every week for 12 weeks. The effects of the intervention on the participants’ scores were calculated using a linear mixed model. There was a larger reduction in their depressive symptoms and fears of compassion for self and a greater increase in their compassion for self compared to the control group participants. The reliable clinical indices showed that in the CFT (intervention) group, three of nine participants recovered (33%), two improved (22%), two recovered but non-reliably (22%), and the condition of two remained unchanged (22%). These findings indicate adequate feasibility of group CFT for TRD in Japanese clinical settings. Clinical trial registration [https://clinicaltrials.gov/], identifier [UMIN 000028698].
Secondary damage and neuroinflammation in the spinal dorsal horn mediate post-thalamic hemorrhagic stroke pain hypersensitivity: SDF1-CXCR4 signaling mediation

Central post-stroke pain (CPSP) is an intractable neuropathic pain, which can be caused by primary lesion of central somatosensory system. It is also a common sequelae of the thalamic hemorrhagic stroke (THS). So far, the underlying mechanisms of CPSP remain largely unknown. Our previous studies have demonstrated that SDF1-CXCR4 signaling in the hemorrhagic region contributes to the maintenance of the THS pain hypersensitivity via mediation of the thalamic neuroinflammation. But whether the spinal dorsal horn, an initial point of spinothalamic tract (STT), suffers from retrograde axonal degeneration from the THS region is still unknown. In this study, neuronal degeneration and loss in the spinal dorsal horn were detected 7 days after the THS caused by intra-thalamic collagenase (ITC) injection by immunohistochemistry, TUNEL staining, electron microscopy, and extracellular multi-electrode array (MEA) recordings, suggesting the occurrence of secondary apoptosis and death of the STT projecting neuronal cell bodies following primary THS via retrograde axonal degeneration. This retrograde degeneration was accompanied by secondary neuroinflammation characterized by an activation of microglial and astrocytic cells and upregulation of SDF1-CXCR4 signaling in the spinal dorsal horn. As a consequence, central sensitization was detected by extracellular MEA recordings of the spinal dorsal horn neurons, characterized by hyperexcitability of both wide dynamic range and nociceptive specific neurons to suprathreshold mechanical stimuli. Finally, it was shown that suppression of spinal neuroinflammation by intrathecal administration of inhibitors of microglia (minocycline) and astrocytes (fluorocitrate) and antagonist of CXCR4 (AMD3100) could block the increase in expression levels of Iba-1, GFAP, SDF1, and CXCR4 proteins in the dorsal spinal cord and ameliorate the THS-induced bilateral mechanical pain hypersensitivity, implicating that, besides the primary damage at the thalamus, spinal secondary damage and neuroinflammation also play the important roles in maintaining the central post-THS pain hypersensitivity. In conclusion, secondary neuronal death and neuroinflammation in the spinal dorsal horn can be induced by primary thalamic neural damage via retrograde axonal degeneration process. SDF1-CXCR4 signaling is involved in the mediation of secondary spinal neuroinflammation and THS pain hypersensitivity. This finding would provide a new therapeutic target for treatment of CPSP at the spinal level.
Effect of skill proficiency on motor imagery ability between amateur dancers and non-dancers

Evidence has shown that athletes with high motor skill proficiency possess higher motor imagery ability than those with low motor skill proficiency. However, less is known whether this superiority in motor imagery ability emerges over amateur athletes. To address the issue, the present study aimed to investigate the individual differences in motor imagery ability between amateur dancers and non-dancers. Forty participants completed a novel dance movement reproduction task and measures of the vividness of visual imagery questionnaire (VVIQ) and the vividness of motor imagery questionnaire (VMIQ). The results showed that, relative to non-dancers, amateur dancers had higher ability of motor imagery to reproduce the lower-limb and upper-limb dance movements during the dance movement reproduction task. Besides, amateur dancers displayed higher abilities of the visual motor imagery and the kinesthetic imagery, but comparable visual imagery ability as the non-dancers. These findings suggest that the mental representation of motors but not the visual is affected by the motor skill levels, due to the motor imagery practice in sports amateurs.
Comparison of brain microstructure alterations on diffusion kurtosis imaging among Alzheimer’s disease, mild cognitive impairment, and cognitively normal individuals

Objective Our study aimed to explore the differences in brain microstructure in patients with Alzheimer’s disease (AD) and with mild cognitive impairment (MCI) and in individuals with normal cognition using diffusion kurtosis imaging (DKI) to identify a potential non-invasive biomarker of AD. Materials and methods A total of 61 subjects were included in our study, including 20 subjects diagnosed with AD, 21 patients diagnosed with amnestic MCI, and 20 cognitively normal individuals. We acquired magnetic resonance imaging (MRI) scans, and DKI images were processed. Twelve regions of interest were drawn, and various parameters were measured and analyzed using SPSS version 11.0 software. Results Comparative analysis showed that differences in brain regions in terms of mean diffusion (MD) and mean kurtosis (MK) between groups were the most marked. Precuneus MD, temporal MK, precuneus MK, and hippocampal MK were significantly correlated with neuropsychological test scores. Hippocampal MK showed the strongest correlation with the medial temporal lobe atrophy score (r = −0.510), and precuneus MD had the strongest correlation with the Koedam score (r = 0.463). The receiver operating curve analysis revealed that hippocampal MK exhibited better diagnostic efficacy than precuneus MD for comparisons between any group pair. Conclusion DKI is capable of detecting differences in brain microstructure between patients with AD, patients with MCI, and cognitively normal individuals. Moreover, it compensates for the deficiencies of conventional MRI in detecting pathological changes in microstructure before the appearance of macroscopic atrophy. Hippocampus MK was the most sensitive single parameter map for differentiating patients with AD, patients with MCI, and cognitively normal individuals.
Justification of Argumentation Schemes

Argumentation schemes are forms of argument that capture stereotypical patterns of human reasoning, especially defeasible ones like argument from expert opinion, that have proved troublesome to view deductively or inductively. Much practical work has already been done on argumentation schemes, proving their worth in A1 [19], but more precise investigations are needed to formalize their structures. The problem posed in this paper is what form justification of a given scheme, as having a certain precise structure of inference, should take. It is argued that defeasible argumentation schemes require both a systematic and a pragmatic justification, of a kind that can only be provided by the case study method of collecting key examples of arguments of the types traditionally classified as fallacies, and subjecting them to comparative examination and analysis. By this method, postulated structures for schemes can be formulated as hypotheses to solve three kinds of problems: (1) how to classify such arguments into different types, (2) how to identify their premises and conclusions, and (3) how to formulate the critical questions used to evaluate each type of argument.
Fluorescence-based thermometry for precise estimation of nanoparticle laser-induced heating in cancerous cells at nanoscale

Abstract Photothermal therapy (PTT) has attracted increasing interest as a complementary method to be used alongside conventional therapies. Despite a great number of studies in this field, only a few have explored how temperatures affect the outcome of the PTT at nanoscale. In this work, we study the necrosis/apoptosis process of cancerous cells that occurs during PTT, using a combination of local laser heating and nanoscale fluorescence thermometry techniques. The temperature distribution within a whole cell was evaluated using fluorescence lifetime imaging microscopy during laser-induced hyperthermia. For this, gold nanorods were utilized as nanoheaters. The local near-infrared laser illumination produces a temperature gradient across the cells, which is precisely measured by nanoscale thermometry. This allows one to optimize the PTT conditions by varying concentration of gold nanorods associated with cells and laser power density. During the PTT procedure, such an approach enables an accurate determination of the percentages of apoptotic and necrotic cells using 2D and 3D models. According to the performed cell experiments, the influence of temperature increase during the PTT on cell death mechanisms has been verified and determined. Our investigations can improve the understanding of the PTT mechanisms and increase its therapeutic efficiency while avoiding any side effects.
Measuring the Video-driven Discussion Engagement Using IRT model

In this study, we introduced a general methodology, three parameter Poisson (3PP) model, for online video-driven discussion (VDD) engagement measurement. To make the model more robust and apply the model in small sample size case, we improved Duplicate, Erase and Replace (DupER) Augmentation in 3PP model. Finally, we conducted behavioral analyses to gain insights into patterns of students comment interaction on a video-driven discussion platform, Vialogues, in a real class environment