Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximizing Influence-based Group Shapley Centrality

One key problem in network analysis is the so-called influence maximization problem, which consists in finding a set $S$ of at most $k$ seed users, in a social network, maximizing the spread of information from $S$. This paper studies a related but slightly different problem: We want to find a set $S$ of at most $k$ seed users that maximizes the spread of information, when $S$ is added to an already pre-existing - but unknown - set of seed users $T$. We consider such scenario to be very realistic. Assume a central entity wants to spread a piece of news, while having a budget to influence $k$ users. This central authority may know that some users are already aware of the information and are going to spread it anyhow. The identity of these users being however completely unknown. We model this optimization problem using the Group Shapley value, a well-founded concept from cooperative game theory. While the standard influence maximization problem is easy to approximate within a factor $1-1/e-\epsilon$ for any $\epsilon>0$, assuming common computational complexity conjectures, we obtain strong hardness of approximation results for the problem at hand in this paper. Maybe most prominently, we show that it cannot be approximated within $1/n^{o(1)}$ under the Gap Exponential Time Hypothesis. Hence, it is unlikely to achieve anything better than a polynomial factor approximation. Nevertheless, we show that a greedy algorithm can achieve a factor of $\frac{1-1/e}{k}-\epsilon$ for any $\epsilon>0$, showing that not all is lost in settings where $k$ is bounded.
Simulated Annealing-Based Heuristic for Multiple Agile Satellites Scheduling Under Cloud Coverage Uncertainty

Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments cannot see through the cloud, the cloud coverage brings a significant influence on the satellite observation missions. The scheduling of multiple agile EOSs (AEOSs) is already complicated due to strong satellite maneuverability and onboard satellite energy constraints. Moreover, introducing cloud coverage uncertainty further increases scheduling complexity. Motivated by these challenges, we address the multiple AEOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. A chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via a sample approximation method. To overcome the solving difficulty, an improved simulated annealing (ISA)-based heuristic including a fast insertion strategy is proposed for large-scale observation missions. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method. Experimental results show that the ISA-based heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which validates the proposed algorithm.
Quantifying the Relationships between Everyday Objects and Emotional States through Deep Learning Based Image Analysis Using Smartphones

There has been an increasing interest in the problem of inferring emotional states of individuals using sensor and user-generated information as diverse as GPS traces, social media data and smartphone interaction patterns. One aspect that has received little attention is the use of visual context information extracted from the surroundings of individuals and how they relate to it. In this paper, we present an observational study of the relationships between the emotional states of individuals and objects present in their visual environment automatically extracted from smartphone images using deep learning techniques. We developed MyMood, a smartphone application that allows users to periodically log their emotional state together with pictures from their everyday lives, while passively gathering sensor measurements. We conducted an in-the-wild study with 22 participants and collected 3,305 mood reports with photos. Our findings show context-dependent associations between objects surrounding individuals and self-reported emotional state intensities. The applications of this work are potentially many, from the design of interior and outdoor spaces to the development of intelligent applications for positive behavioral intervention, and more generally for supporting computational psychology studies.
Designing and Evaluating Hand-to-Hand Gestures with Dual Commodity Wrist-Worn Devices

Hand gestures provide a natural and easy-to-use way to input commands. However, few works have studied the design space of bimanual hand gestures or attempted to infer gestures that involve devices on both hands. We explore the design space of hand-to-hand gestures, a group of gestures that are performed by touching one hand with the other hand. Hand-to-hand gestures are easy to perform and provide haptic feedback on both hands. Moreover, hand-to-hand gestures generate simultaneous vibration on two hands that can be sensed by dual off-the-shelf wrist-worn devices. In this work, we derive a hand-to-hand gesture vocabulary with subjective ratings from users and select gesture sets for real-life scenarios. We also take advantage of devices on both wrists to demonstrate their gesture-sensing capability. Our results show that the recognition accuracy for fourteen gestures is 94.6% when the user is stationary, and the accuracy for five gestures is 98.4% or 96.3% when the user is walking or running, respectively. This is significantly more accurate than a single device worn on either wrist. Our further evaluation also validates that users can easily remember hand-to-hand gestures and use our technique to invoke commands in real-life contexts.
A Survey on Deep Learning for Multimodal Data Fusion

With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. These data, referred to multimodal big data, contain abundant intermodality and cross-modality information and pose vast challenges on traditional data fusion methods. In this review, we present some pioneering deep learning models to fuse these multimodal big data. With the increasing exploration of the multimodal big data, there are still some challenges to be addressed. Thus, this review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multimodal deep learning fusion method and to motivate new multimodal data fusion techniques of deep learning. Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. Then the current pioneering multimodal data fusion deep learning models are summarized. Finally, some challenges and future topics of multimodal data fusion deep learning models are described.
The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise

Multilayer neural networks have led to remarkable performance on many kinds of benchmark tasks in text, speech, and image processing. Nonlinear parameter estimation in hierarchical models is known to be subject to overfitting and misspecification. One approach to these estimation and related problems (e.g., saddle points, colinearity, feature discovery) is called Dropout. The Dropout algorithm removes hidden units according to a binomial random variable with probability p prior to each update, creating random “shocks” to the network that are averaged over updates (thus creating weight sharing). In this letter, we reestablish an older parameter search method and show that Dropout is a special case of this more general model, stochastic delta rule (SDR), published originally in 1990. Unlike Dropout, SDR redefines each weight in the network as a random variable with mean μwij and standard deviation σwij. Each weight random variable is sampled on each forward activation, consequently creating an exponential number of potential networks with shared weights (accumulated in the mean values). Both parameters are updated according to prediction error, thus resulting in weight noise injections that reflect a local history of prediction error and local model averaging. SDR therefore implements a more sensitive local gradient-dependent simulated annealing per weight converging in the limit to a Bayes optimal network. We run tests on standard benchmarks (CIFAR and ImageNet) using a modified version of DenseNet and show that SDR outperforms standard Dropout in top-5 validation error by approximately 13% with DenseNet-BC 121 on ImageNet and find various validation error improvements in smaller networks. We also show that SDR reaches the same accuracy that Dropout attains in 100 epochs in as few as 40 epochs, as well as improvements in training error by as much as 80%.
Chondroinduction of Mesenchymal Stem Cells on Cellulose-Silk Composite Nanofibrous Substrates: The Role of Substrate Elasticity

Smart biomaterials with an inherent capacity to elicit specific behaviors in lieu of biological prompts would be advantageous for regenerative medicine applications. In this work, we employ an electrospinning technique to model the in vivo nanofibrous extracellular matrix (ECM) of cartilage using a chondroinductive cellulose and silk polymer blend (75:25 ratio). This natural polymer composite is directly electrospun for the first time, into nanofibers without post-spun treatment, using a trifluoroacetic acid and acetic acid cosolvent system. Biocompatibility of the composite nanofibres with human mesenchymal stem cells (hMSCs) is demonstrated and its inherent capacity to direct chondrogenic stem cell differentiation, in the absence of stimulating growth factors, is confirmed. This chondrogenic stimulation could be countered biochemically using fibroblast growth factor-2, a growth factor used to enhance the proliferation of hMSCs. Furthermore, the potential mechanisms driving this chondroinduction at the cell-biomaterial interface is investigated. Composite substrates are fabricated as two-dimensional film surfaces and cultured with hMSCs in the presence of chemicals that interfere with their biochemical and mechanical signaling pathways. Preventing substrate surface elasticity transmission resulted in a significant downregulation of chondrogenic gene expression. Interference with the classical chondrogenic Smad2/3 phosphorylation pathway did not impact chondrogenesis. The results highlight the importance of substrate mechanical elasticity on hMSCs chondroinduction and its independence to known chondrogenic biochemical pathways. The newly fabricated scaffolds provide the foundation for designing a robust, self-inductive, and cost-effective biomimetic biomaterial for cartilage tissue engineering.
A Brief Review of Current Maturation Methods for Human Induced Pluripotent Stem Cells-Derived Cardiomyocytes

Cardiovascular diseases are the leading cause of death worldwide. Therefore, the discovery of induced pluripotent stem cells (iPSCs) and the subsequent generation of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) was a pivotal point in regenerative medicine and cardiovascular research. They constituted an appealing tool for replacing dead and dysfunctional cardiac tissue, screening cardiac drugs and toxins, and studying inherited cardiac diseases. The problem is that these cells remain largely immature, and in order to utilize them, they must reach a functional degree of maturity. To attempt to mimic in vivo environment, various methods including prolonging culture time, co-culture and modulations of chemical, electrical, mechanical culture conditions have been tried. In addition to that, changing the topology of the culture made huge progress with the introduction of the 3D culture that closely resembles the in vivo cardiac topology and overcomes many of the limitations of the conventionally used 2D models. Nonetheless, 3D culture alone is not enough, and using a combination of these methods is being explored. In this review, we summarize the main differences between immature, fetal-like hiPSC-CMs and adult cardiomyocytes, then glance at the current approaches used to promote hiPSC-CMs maturation. In the second part, we focus on the evolving 3D culture model – it’s structure, the effect on hiPSC-CMs maturation, incorporation with different maturation methods, limitations and future prospects.
Smart Seismic Control System for High-Rise Buildings Using Large-Stroke Viscous Dampers Through Connection to Strong-Back Core Frame

An innovative structural control system is proposed for high-rise buildings. A damping layer is provided between a stiff upper core frame suspended from the top of the main building and a stiff lower core frame connected to the building foundation. As the ratio of stiffness of both core frames to that of the main building becomes larger, the relative displacement in the damping layer (damper deformation) approaches to the top floor displacement of the main building. The large displacement of the top floor displacement of the main building is taken full advantage in the proposed control system as most of the total displacement of the main building results from the damper deformation instead of interstory drift. Transformation of the multi-degree-of-freedom (MDOF) model into the single-degree-of-freedom (SDOF) model enables a simplified but rather accurate response evaluation for pulse-type and long-duration earthquake ground motions. The results of the time history response analysis of buildings including this control system are presented for various recorded ground motions. Finally, the effectiveness of the proposed structural control system is discussed from the viewpoint of earthquake input energy.
Flexible and Electroactive Textile Actuator Enabled by PEDOT:PSS/MOF-Derivative Electrode Ink

Smart fabrics that integrate electronic devices with textiles are emerging as potential candidate for apparel and electronics industries. Soft actuators based on conducting polymers are promising for smart fabrics because of light weight, flexibility, and large deformation under low voltage. However, due to the distinct characteristics of textile and electronic components, the connection between textiles and electronic devices still keeps a challenge in development of smart fabrics. Here, we report an new strategy to prepare a flexible and electroactive textile actuator. The fabric electrolyte was directly coated with an electrode ink, which is composed of Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonic acid) doped with carbonized carbon nanotubes wired zeolite imidazolate framework-8 composite. A pre-treatment of the fabric was made by soaking hydrophobic poly(vinylidene fluoride-co-hexafluoropropylene) to increase the ionic conductivity (6.72 mS cm–1) and prevent the electrode ink from penetrating through the fabric. It was found that the textile actuator could work in air stably under a low voltage of 3 V and operate at frequencies from 0.1 to 10 Hz with large strain difference (0.28% at 0.1 Hz), fast strain rate (2.8% s–1 at 10 Hz) and good blocking force (0.62 mN at 0.1 Hz). The key to high performance originates from high ionic conductivity of fabric electrolyte and large specific surface area, good mechanical properties of the metal-organic framework derivative-based composite electrodes, which present insights into preparing other smart fabrics such as textiles sensors, flexible displays, and textile energy storage devices.
Vertical Beta-Diversity of Bacterial Communities Depending on Water Stratification

Many studies indicate that variation of marine bacterial beta diversity in the horizontal dimension is mainly attributable to environmental and spatial effects. However, whether and how these two effects drive bacterial beta diversity in the vertical dimension remains unclear, especially when considering seasonal variation in the strength of water stratification. Here, we used 78 paired bacterioplankton community samples from surface and deep chlorophyll maximum (DCM) layers along a transect in the Kuroshio region east of Taiwan across multiple seasons. Variance partitioning was used to evaluate the mechanisms driving the vertical beta diversity between surface-DCM bacterioplankton communities during weak stratification periods (i.e., spring and fall) versus strong stratification periods (i.e., summer). During strong periods of stratification, vertical beta diversity was shaped by both environmental and spatial effects; notably, the strength of stratification played an important role in enhancing environmental dissimilarity and creating a barrier to dispersal. In contrast, during periods of weak stratification, environmental effects dominate, with a non-significant spatial effect due to mixing. Variation of vertical beta diversity for bacterioplankton communities in the Kuroshio region east of Taiwan was structured by different mechanisms across seasons, and was further dependent on stratification strength of the water column.
Influence of Physical Activity Interventions on Body Representation: A Systematic Review

Distorted representation of one's own body is a diagnostic criterion and corepsychopathology of disorders such as anorexia nervosa and body dysmorphic disorder. Previousliterature has raised the possibility of utilising physical activity intervention (PI) as atreatment option for individuals suffering from poor body satisfaction, which is traditionallyregarded as a systematic distortion in “body image. ” In this systematic review,conducted according to the PRISMA statement, the evidence on effectiveness of PI on body representation outcomes is synthesised. We provide an update of 34 longitudinal studies evaluating the effectiveness of different types of PIs on body representation. No systematic risk of bias within or across studies were identified. The reviewed studies show that the implementation of structured PIs may be efficacious in increasing individuals’ satisfaction of their own body, and thus improving their subjective body image related assessments. However, there is no clear evidence regarding an additional or interactive effect of PI when implemented in conjunction with established treatments for clinical populations. We argue for theoretically sound, mechanism-oriented, multimethod approaches to future investigations on body image disturbance. Specifically, we highlight the need to consider expanding the theoretical framework for the investigation of body representation disturbances to include further body representations besides body image.
The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility

Many proteins realize their special functions by binding with specific metal ion ligands during a cell’s life cycle. The ability to correctly identify metal ion ligand-binding residues is valuable for the human health and the design of molecular drug. Precisely identifying these residues, however, remains challenging work. We have presented an improved computational approach for predicting the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Ca2+, Mg2+, Mn2+, Na+, and K+) by adding reclassified relative solvent accessibility (RSA). The best accuracy of fivefold cross-validation was higher than 77.9%, which was about 16% higher than the previous result on the same dataset. It was found that different reclassification of the RSA information can make different contributions to the identification of specific ligand binding residues. Our study has provided an additional understanding of the effect of the RSA on the identification of metal ion ligand binding residues.
Impact of Post Partum Due Care on Fertility in Farm Animals

The transition from a pregnant non-lactating state to a non-pregnant lactating state in farm animals is associated with numerous physiological challenges and stressors related to parturition and the onset of lactation. The postpartum period is defined as the time between parturition and completion of uterine involution. The postpartum care of domestic animals starts before giving birth. To avoid reproductive tract infections, animals need to be served and give birth under hygienic conditions, otherwise animals will suffer from prolongation in the interval to next parturition and great economic losses. The current article aims to threw lights on the proper postpatrum hygienic precautions to maintain fertility in farm animals. These precautions are mainly directed towards appropriate balanced nutrition; improved health status and good management. Balanced feeding with vitamins, mineral and traces supplementation must be available to face the increased nutrient demands and the onset of lactation. Application of the suitable proper vaccine program against prevailing local diseases is required to protect these animals, as well as their offspring. Dropping of fetal membranes must be monitored to ensure its separation in the proper time otherwise veterinary medical interference must be taken. The uterus must be checked for the progress and completion of post partum involution either thought rectal palpation or ultrasonography. A plenty of room and clean site of parturition should be available. Bedding should be changed after each parturition with the use only disinfected instruments and disinfect both the operator and animal before and after conducting any obstetrical manipulations. If a birth is difficult, or otherwise abnormal, intra-uterine application of a broad-spectrum antibiotic will help prevent infection. In conclusion, the peripartum period should not be neglected to maintain animal health and fertility and decrease economic losses.
CellPred

Cellular data usage consumption prediction is an important topic in cellular networks related researches. Accurately predicting future data usage can benefit both the cellular operators and the users, which can further enable a wide range of applications. Different from previous work focusing on statistical approaches, in this paper, we propose a scheme called CellPred to predict cellular data usage from an individual user perspective considering user behavior patterns. Specifically, we utilize explicit user behavioral tags collected from subscription data to function as an external aid to enhance the user's mobility and usage prediction. Then we aggregate individual user data usage to cell tower level to obtain the final prediction results. To our knowledge, this is the first work studying cellular data usage prediction from an individual user behavior-aware perspective based on large-scale cellular signaling and behavior tags from the subscription data. The results show that our method improves the data usage prediction accuracy compared to the state-of-the-art methods; we also comprehensively demonstrate the impacts of contextual factors on CellPred performance. Our work can shed light on broad cellular networks researches related to human mobility and data usage. Finally, we discuss issues such as limitations, applications of our approach, and insights from our work.