Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring network properties from time series using transfer entropy and mutual information: Validation of multivariate versus bivariate approaches

Abstract Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting properties of these networks requires inferred network models to reflect key underlying structural features. However, even a few spurious links can severely distort network measures, posing a challenge for functional connectomes. We study the extent to which micro- and macroscopic properties of underlying networks can be inferred by algorithms based on mutual information and bivariate/multivariate transfer entropy. The validation is performed on two macaque connectomes and on synthetic networks with various topologies (regular lattice, small-world, random, scale-free, modular). Simulations are based on a neural mass model and on autoregressive dynamics (employing Gaussian estimators for direct comparison to functional connectivity and Granger causality). We find that multivariate transfer entropy captures key properties of all network structures for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series but are unable to control false positives (lower specificity) as available data increases. This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models.
Heat Flux Measurement of Boiling Heat Transfer in a Helically Coiled Tube Heated by Oil Bath

This study deals with an experimental investigation of the flow boiling heat transfer characteristics of water in a helically coiled tube heated in a high temperature oil bath in relation to the steam generator of LMFBR. Time variations of the external and internal tube wall temperatures were measured under low quality conditions and the heat fluxes across the tube were calculated from the difference between the external and internal temperatures. A simple numerical analysis was made to evaluate the heat transfer coefficients at the external and internal tube walls and their time variations. The results show that the heat flux change is caused by the heat capacity of the tube and that its temporal behavior is quite different from the wall temperatures. This suggests that the deterioration of heat transfer caused by local dry-out on the wall surface results in peculiar changes of the wall temperatures and the heat fluxes.
Entropy dissipation via Information Gamma calculus: Non-reversible stochastic differential equations.

We formulate explicit bounds to guarantee the exponential dissipation for some non-gradient stochastic differential equations towards their invariant distributions. Our method extends the connection between Gamma calculus and Hessian operators in $L^2$--Wasserstein space. In details, we apply Lyapunov methods in the space of probabilities, where the Lyapunov functional is chosen as the relative Fisher information. We derive the Fisher information induced Gamma calculus to handle non-gradient drift vector fields. We obtain the explicit dissipation bound in terms of $L_1$ distance and formulate the non-reversible Poincar{\'e} inequality. An analytical example is provided for a non-reversible Langevin dynamic.
The duality structure gradient descent algorithm: analysis and applications to neural networks.

The training of deep neural networks is typically carried out using some form of gradient descent, often with great success. However, existing non-asymptotic analyses of ﬁrst-order optimization algorithms typically employ a gradient smoothness assumption that is too strong to be applicable in the case of deep neural networks. To address this, we propose an algorithm named duality structure gradient descent ( dsgd ) that is amenable to non-asymptotic performance analysis, under mild assumptions on the training set and network architecture. The algorithm can be viewed as a form of layer-wise coordinate descent, where at each iteration the algorithm chooses one layer of the network to update. The decision of what layer to update is done in a greedy fashion, based on a rigorous lower bound on the improvement of the objective function for each choice of layer. In the analysis, we bound the time required to reach approximate stationary points, in both the deterministic and stochastic settings. The convergence is measured in terms of a parameter-dependent family of norms that is derived from the network architecture and designed to conﬁrm a smoothness-like property on the gradient of the training loss function. We empirically demonstrate the eﬀectiveness of dsgd in several neural network training scenarios.
A Robust Feature-aware Sparse Mesh Representation

The sparse representation of signals defined on Euclidean domains has been successfully applied in signal processing. Bringing the power of sparse representations to non-regular domains is still a challenge, but promising approaches have started emerging recently. In this paper, we investigate the problem of sparsely representing discrete surfaces and propose a new representation that is capable of providing tools for solving different geometry processing problems. The sparse discrete surface representation is obtained by combining innovative approaches into an integrated method. First, to deal with irregular mesh domains, we devised a new way to subdivide discrete meshes into a set of patches using a feature-aware seed sampling. Second, we achieve good surface approximation with over-fitting control by combining the power of a continuous global dictionary representation with a modified Orthogonal Marching Pursuit. The discrete surface approximation results produced were able to preserve the shape features while being robust to over-fitting. Our results show that the method is quite promising for applications like surface re-sampling and mesh compression.
Prevalence of Sarcopenic Obesity and its Association with Functionality, Lifestyle, Biomarkers and Morbidities in Older Adults: the FIBRA-RJ Study of Frailty in Older Brazilian Adults

OBJECTIVES: To assess the prevalence of sarcopenic obesity and its association with functionality, lifestyle, biomarkers, and morbidities in older adults. METHODS: The study analyzed cross-sectional data from 270 older adults who participated in phase III of the Frailty in Brazilian Older People Study (Fragilidade em Idosos Brasileiros—Rio de Janeiro, FIBRA-RJ study-2013). They took part in a home interview surveying socioeconomic, demographic, lifestyle, morbidities, and functional data. Blood was collected for biochemical marker analysis and participants’ body composition was determined by dual-energy X-ray absorptiometry. For women, the diagnosis of sarcopenic obesity was defined at a body fat percentage ≥38% and appendicular skeletal muscle mass index (ASMMI) <5.45 kg/m2. For men, a fat percentage ≥27% and ASMMI <7.26 kg/m2 was defined as sarcopenic obesity. Multivariate analysis was performed using a multinomial regression model (95% confidence intervals), with sarcopenic obesity as the outcome. RESULTS: The prevalence of sarcopenic obesity was 29.3%. In the final fitted model, the variables that displayed statistically significant association with sarcopenic obesity were lower gait speed, self-reported medical diagnosis of arthrosis or arthritis, and high levels of glycemia. CONCLUSION: The study showed a high prevalence of sarcopenic obesity in non-institutionalized older adults in Brazil. The finding that this condition was associated with modifiable risk factors may provide insights into measures directed at prevention and reduction of the risk of sarcopenic obesity in this population subgroup.
Carath\'eodory Sampling for Stochastic Gradient Descent

Many problems require to optimize empirical risk functions over large data sets. Gradient descent methods that calculate the full gradient in every descent step do not scale to such datasets. Various flavours of Stochastic Gradient Descent (SGD) replace the expensive summation that computes the full gradient by approximating it with a small sum over a randomly selected subsample of the data set that in turn suffers from a high variance. We present a different approach that is inspired by classical results of Tchakaloff and Carath\'eodory about measure reduction. These results allow to replace an empirical measure with another, carefully constructed probability measure that has a much smaller support, but can preserve certain statistics such as the expected gradient. To turn this into scalable algorithms we firstly, adaptively select the descent steps where the measure reduction is carried out; secondly, we combine this with Block Coordinate Descent so that measure reduction can be done very cheaply. This makes the resulting methods scalable to high-dimensional spaces. Finally, we provide an experimental validation and comparison.
Intracellular pH and Its Measurement

The Concept of pH, Hydrolysis, and Buffers. pH in Biological Systems. Methods of Determining Intracellular pH with Electrodes. Assays of Intracellular pH Using Chemical Probes: Principles of pH Indicator Response. Assays of Intracellular pH Using Chemical Probes: Distribution Techniques. Assays of Intracellular pH Using Chemical Probes: Absorption Spectroscopy. Assays of Intracellular pH Using Chemical Probes: Fluorescence Spectroscopy. Assays of Intracellular pH Using Chemical Probes: Nuclear Magnetic Resonance Spectroscopy. The Choice of the Assay Method. Heterogeneity of Intracellular pH. Index.
Survey on model-based biped motion control for humanoid robots

ABSTRACT Studies of biped control including standing, walking, hopping and running on humanoid robots are reviewed in this survey paper. Model-based approaches standing upon reduced-order dynamics are focused. It begins with revisiting the centroidal dynamics and the zero-moment point, which leads to the linear inverted pendulum (LIP) model and some of its variations. Then, some representative standing and walking control techniques based on the LIP model are reviewed, where the concept of capturability is also discussed. It is followed by consideration of height variation of the center of mass. Enhancement to hopping and running motions, which include aerial phases, is also addressed. Ideas to deal with complex nature as a hybrid system underlying discontinuously varying mechanics mainly due to unilateral constraints on contact forces are concisely explained. GRAPHICAL ABSTRACT
Dynamic goal processes: an agenda for interpersonal communication research

ABSTRACT A longstanding tradition in communication research is that people have goals for communicative encounters. Communication research has evolved to better acknowledge that goals are malleable and can change during interactions. Drawing upon the history of communication goals theorizing and research, we elaborate three properties emerging from the juxtaposition of goals and time. We then explore theoretical accounts of goal change and options for data acquisition. We end with speculation on how goal dynamics might stimulate new questions about interpersonal communication and, perhaps, move the field into a new phase of inquiry.
Predicting Users’ Movie Preference and Rating Behavior from Personality and Values

In this article, we propose novel techniques to predict a user’s movie genre preference and rating behavior from her psycholinguistic attributes obtained from the social media interactions. The motivation of this work comes from various psychological studies that demonstrate that psychological attributes such as personality and values can influence one’s decision or choice in real life. In this work, we integrate user interactions in Twitter and IMDb to derive interesting relations between human psychological attributes and their movie preferences. In particular, we first predict a user’s movie genre preferences from the personality and value scores of the user derived from her tweets. Second, we also develop models to predict user movie rating behavior from her tweets in Twitter and movie genre and storyline preferences from IMDb. We further strengthen the movie rating model by incorporating the user reviews. In the above models, we investigate the role of personality and values independently and combinedly while predicting movie genre preferences and movie rating behaviors. We find that our combined models significantly improve the accuracy than that of a single model that is built by using personality or values independently. We also compare our technique with the traditional movie genre and rating prediction techniques. The experimental results show that our models are effective in recommending movies to users.
‘This circle of joy’: meaningful musicians’ work and the benefits of facilitating singing groups

ABSTRACT The complex realities of musicians’ portfolio careers highlight the need for research on musicians’ wellbeing. Musicians may include community work within their portfolio such as the facilitation of singing groups for health and wellbeing. Until recently, research on these groups has focused primarily on health and wellbeing outcomes for group participants with little discussion of these factors for the facilitators as professional musicians. We recruited eleven facilitators for semi-structured interviews to investigate their work experiences. Informed by positive psychology and the PERMA model for wellbeing, reflexive thematic analysis of interview data produced themes indicating positive wellbeing outcomes for facilitators. Facilitation was experienced as a unique relationship and mode of performance, and as work ideally suited to facilitators’ skills and interests. Facilitators’ experiences exhibited all five PERMA elements constitutive of wellbeing: positive emotions (P), engagement (E), relationships (R), meaning (M), and accomplishment (A). These findings align with other research on the positive experiences of professional musicians, music educators and singing group participants, while contributing new understanding that facilitation of these groups can offer wellbeing benefits that compliment musicians’ livelihoods. This is important knowledge for the facilitators themselves and for university educators who seek to inform the career choices of their students.
Skeleton Based Dynamic Hand Gesture Recognition using LSTM and CNN

Dynamic Hand Gestures offer a natural, non-verbal way of communication that can substitute other communication modalities like verbal speech and script writing. Not only for the voice command, hand gestures also play significant role in Augmented Reality (AR), Virtual Reality (VR) and games. There are some factors like computational cost, flexibility and recognition accuracy that can impact the incorporation of hand gestures in these fields. In this paper, a Dynamic Hand Gesture Recognition (DHGR) approach is propose that is based on Convolutional Neural Network (CNN) and long-short term memory (LSTM). This system is trained to execute the sequence of 3D input data along with the velocities and positions information learned from Leap Motion Controller (LMC). When evaluated and compared with state-of-art DHGR methods, this architecture shows relative high accuracy of 98%.
Characterizing Measurement Error in Dietary Sodium in Longitudinal Intervention Studies

Background: Previous measurement error work that investigates the relationship between a nutritional biomarker and self-reported intake levels has typically been at a single time point, in a single treatment group, or with respect to basic patient demographics. Few studies have examined the measurement error structure in longitudinal randomized trials, and whether the error varies across time or group. This structure is crucial to understand, however, in order to correct for measurement error in self-reported outcomes and properly interpret the longitudinal effects of dietary interventions. Methods: Using two longitudinal randomized controlled trials with internal longitudinal validation data (urinary biomarkers and self-reported values), we examine the relationship between urinary sodium and self-reported sodium and whether this relationship changes as a function of time and/or treatment condition. We do this by building a mixed effects regression model, allowing for a flexible error variance-covariance structure, and testing all possible interactions between time, treatment condition, and self-reported intake. Results: Using a backward selection approach, we arrived at the same final model for both validation data sets. We found no evidence that measurement error changes as a function of self-reported sodium. However, we did find evidence that urinary sodium can differ by time or treatment condition even when conditioning on self-reported values. Conclusion: In longitudinal nutritional intervention trials it is possible that measurement error differs across time and treatment groups. It is important for researchers to consider this possibility and not just assume non-differential measurement error. Future studies should consider data collection strategies to account for the potential dynamic nature of measurement error, such as collecting internal validation data across time and treatment groups when possible.
CROSS-AGE FACE VERIFICATION USING FACE AGING

Faces are inherently associated with a person’s identity. By extracting the relevant features from facial images, we can differentiate between people. Most of these features are age-invariant and can be used to determine the identity of the person even after a long span of time. In this paper, we propose a face verification model that can unambiguously verify a person’s identity across the age-progression. For the task of face verification, we firstly use a Generative Adversarial Network (GAN), which preserves the distinguishing facial features and ages the person’s image. We then compare the aged image with a ground truth old image to verify the identity. We apply three metrics namely, SSIM, AbsDiff, and Face++ Online API to achieve this task. Qualitative results on CACD and FG-NET databases show that aging the image improves the accuracy of face verification. Quantitative experiments on both the databases demonstrate the high applicability of our model to existing biometric systems. We hope that the proposed work can be used to tackle impersonation frauds and for finding lost children.
Bottom-up: A SGX-based Blockchain Trusted Startup and Verification Scheme

The rapid development of blockchain technology has aroused people's attention to its security issues. The immutability and non-repudiation of the blockchain ensure the security of upper-level data and networks, but the existing mechanisms of the blockchain have always been difficult to guarantee its underlying Security. This paper proposes a method for verifying the trusted launch of a blockchain, which is used for trusted verification before the launch of a blockchain network. This method mainly uses a solution that combines trusted computing and blockchain networks in cloud scenarios to solve the integrity and security issues at the start of the blockchain to prevent potential problems. The architecture of this article is composed of a blockchain network, SGX Application and cloud database. We build a trust chain based on key services and files when the blockchain is started, and use integrity verification to transfer trust. Hashing is performed with the support of SGX to ensure the security of the verification process. Under the condition that the performance at the time of loss is about 50% to 62%, the underlying security is fully guaranteed.
A group key agreement based encrypted traffic detection scheme for Internet of Things

In CCS 2019, the privacy-preserving deep package inspection (PrivDPI) was proposed to detect anomalies and suspicious activities in encrypted network traffic, which is a provably secure and highly efficient approach for the end-to-end communication model. However, PrivDPI cannot be applied directly on the scenarios of the Internet of Things (IoT) due to its one/many-to-many communication model in which key agreement will bring giant power consumption. In this paper, we propose a group key agreement based encrypted traffic detection scheme for the Internet of Things (GKA_DPI) to solve it. In GKA_DPI, we still use BlindBox for traffic detection, which was used in PrivDPI and Sherry's scheme. The difference is that we use a dynamic group key agreement to replace the original key agreement protocol to reduce power consumption. Then we can perform deep traffic detection over encrypted packages on the widely used protocol Message Queuing Telemetry Transport (MQTT) of IoT. GKA_DPI can detect encrypted traffic without decrypting transmitted messages and find out malicious traffic to ensure the security of sensor network communication. Finally, we prove the forward and backward secrecy of proposed GKA_DPI.
Support of Image-Based Quality Assessment in Discrete Production Scenarios through AI-Based Decision Support

This paper is based on results obtained during the research project IQ-Control. The main objective of the project targets the enrichment of a conventional perception-based 3D line scanning sensor data with methods of deep learning, in order to identify quality defects in discrete production scenarios in real-time. In the coming years, this process should help to preserve resources and further reduce material consumption, especially for large quantity production scenarios.
Bots in Social and Interaction Networks

The rise of bots and their influence on social networks is a hot topic that has aroused the interest of many researchers. Despite the efforts to detect social bots, it is still difficult to distinguish them from legitimate users. Here, we propose a simple yet effective semi-supervised method that allows distinguishing between bots and legitimate users with high accuracy. The method learns a joint representation of social connections and interactions between users by leveraging graph-based representation learning. Then, on the proximity graph derived from user embeddings, a sample of bots is used as seeds for a label propagation algorithm. We demonstrate that when the label propagation is done according to pairwise account proximity, our method achieves F1 = 0.93, whereas other state-of-the-art techniques achieve F1 ≤ 0.87. By applying our method to a large dataset of retweets, we uncover the presence of different clusters of bots in the network of Twitter interactions. Interestingly, such clusters feature different degrees of integration with legitimate users. By analyzing the interactions produced by the different clusters of bots, our results suggest that a significant group of users was systematically exposed to content produced by bots and to interactions with bots, indicating the presence of a selective exposure phenomenon.
Ecg Classification using Machine Learning Techniques and Smote Oversampling Technique

In this paper, automatic classification of Atrial Fibrillation (AF) based on single lead ECG signal was proposed using three different classification algorithm AdaBoost, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). SMOTE technique was applied as data oversampling techniques. Many features were extracted and Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select relevant features. 5834 records were selected from the Physionet Challenge 2017 dataset for this experiment. Classification using oversampling technique yields best results for all classifiers involved. AdaBoost on oversampling data yields the best accuracy of 98.8%.