Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery Mechanism of Large-scale Damaged Edge Computing Network in Industrial Internet of Things

: Given the limited resources at the early stage of recovery, a failure recovery mechanism of edge computing networks considering both computational demands and repair costs was proposed, which intends to tackle the problem of the high probability of large-scale cascading failure caused by the interdependence between edge computing networks and other subnetworks in industrial Internet of Things (IIoT). Considering the network structure (topology and link capacities) and system dynamics (computational demands), a joint link recovery selection and computation migration optimization problem was formulated under the conservation of node computing requirements. By leveraging the Benders decomposition algorithm, the NP-hard problem was transformed into a main problem and a subproblem, which were interdependent and could be solved in polynomial time through the approximation of cutting planes. A local branching method was further introduced to guarantee the non-increasing nature of the Benders upper bound, thus accelerating the convergence of Benders decomposition. Simulation results demonstrate that the proposed mechanism outperforms the conventional topology-based recovery mechanism in system cost, and can perform well in multiple scenarios.
Cross-modal constraints in multimodal vocalizations in Siamang ( Syndactylus symphalangus )

Gibbons ( Hylobatidae ) sing loudly, reaching over a hundred Decibels - about the sound level of a rock concert. Qualitative observations report that, during song climaxes, individuals move in a coordinated way with their singing. We hypothesize that vigorous thorax-loading movements such as brachiation induce physical constraints on the respiratory-vocal system. This coordination and possibly underlying biomechanics have never been studied, leaving a blind spot for a possible shared variance between vocal and locomotor repertoires that would indicate a co-evolution of two systems. Here, we recorded over a hundred stereotypical multimodal vocal calls from over 7 hours of singing in two captive siamangs ( Syndactylus sym-phalangus ), a type of gibbon that are the largest in size. These stereotypical calls coincided with a bodily display during solo singing, and were exclusively performed by juvenile individuals. We used computer vision methods (opencv and deeplab-cut) to quantify upper body acceleration of these multimodal displays of two individuals; we found that body acceleration statistically predicted the nearest peak in the amplitude envelope of the call. The results indicate that Siamang singing likely co-evolved with movement due to physical constraints of pectoral limb-respiratory-vocal interactions, similar to birds, bats, and rodents, as well as humans. This has important implications for the locomotor hypotheses of rhythm origins as our results suggest a biomechanical and basic coupling of vocal systems with the (loco)-motor system: in singing Siamangs, and perhaps humans too, as there are homologies to be drawn between how humans and Siamang move in gestural ways with their vocalizations.
Primary education teachers' self-efficacy beliefs for teaching Geography lessons

ABSTRACT This study was carried out to investigate the primary education teachers' self-efficacy regarding geography education, their beliefs regarding the classroom learning environment, and how these beliefs were related to each other and to teachers' background characteristics. Questionnaire data were collected from 489 Dutch primary school teachers. Primary education teachers displayed moderately strong self-efficacy for teaching geography. The overall results suggested that teachers believed that surface learning is relatively an important parameter of teaching geography. The study found out that support from their colleagues was considered less important by teachers in promoting geography education. Teachers' beliefs concerning geography education quality and students' attitudes and motivation were the most important predictor variables for explaining differences in teachers' self-efficacy.
Influence of foam thickness, whipping time and drying temperature on production of guava powder during foam mat drying

The study was carried out for utilization of guava fruit for preparation of foam mat dried fruit pulp powder. The conversion of guava pulp into foam was optimized by whipping time pulp after addition of methyl cellulose as foaming stabilizer and egg albumin (0.8%) as a foaming agent and drying the foamed pulp in mechanical dryer at different temperature (55 o C, 60 o C and 65 o C) to constant moisture content. Drying of foamed guava pulp by using 0.8% egg albumin results in 10.68% yield and was found the most appropriate with respect to desired foaming properties, physico-chemical as well as sensory characteristics. With the increases in the whipping time, the foam density and foam stability decreases significantly however, the percentage of foam expansion was increased. The dried powder by using 0.8% egg albumin contained in range of 7.24-8.02 moisture content, 4.64-4.92 pH, 203.31 - 221.20 mg/100g ascorbic acid, 72.05 to 85.42% WSI, 351.99 to 365.36% WAI and 6.2 – 7.96 overall acceptability. The maximum value of overall acceptability was found to be 7.96 for foamed dried guava pulp powder for temperature of 60 ⁰C with a whipping time of 10 min and 4 mm foam thickness of guava pulp.
Probe Automata for Passage Time Speciﬁcation

Passage time distribution has drawn increasing attention over the past years as an important measure to deﬁne and verify service level agreements. The deﬁnition of passage time requires the speciﬁcation of a condition to start/stop the computation, and possibly of a restriction on the system behavior to be considered be-tween start and stop. Different characterizations have been deﬁned in the past, either state-based, action-based or a mix of the two, either for Markov chains, or for stochastic Petri nets and process algebras. In this paper we propose probe automata as a way to specify passage time for GSPNs that allows one to select entering, goal, and forbidden states, as well as paths of interest starting from any reachable state. The speciﬁcation is in terms of conditions over the current marking, the transition (sequence) being ﬁred, as well as over the marking reached through the ﬁring. Probe automata subsume previous deﬁnitions of passage time for GSPNs and for Tagged GSPNs, the extension of GSPNs that was deﬁned in the past for computing passage time of a tagged token in a GSPN.
Inhibition of Fe2+ Induced Lipid Peroxidation in Rats Brain by Extract of Euphorbia heterophylla in vitro

This study is sought to determine the antioxidant activity and protective ability of aqueous and methanol extractible phytochemicals from Euphorbia heterophylla leaves on lipid peroxidation induced in rat brain by pro-oxidant, in vitro. The extracts of the leaves were prepared, and the ability of the extracts is to inhibit 25 µM FeSO4 induced lipid peroxidation in isolated rats’ brain, were determined. Thereafter, total phenol content, reducing power (FRAP), Fe (II) chelating, and DPPH* free radical scavenging ability of the extracts was determined and considered as an index of antioxidant activity. The results revealed that the extracts inhibit malondialdehyde (MDA) production in the basal and pro-oxidant induced lipid peroxidised rats in a dose-dependent manner, [methanol 80.11%, aqueous 70.3%] with the methanol extract (MEE) significantly (P< 0.05) than that of aqueous extract (AEE). The methanol extract (0.74 ± 0.6 mg/g) had higher total phenol contents than the aqueous (0.57 ± 1.2 mg/g); likewise the methanol extract had higher reducing power (0.08 ± 0.2, 0.03 ± 0.1 mg/g), but had no significant difference in Fe (II) chelating ability (EC50= 0.34, 0.36) with DPPH* scavenging ability (EC50=0.075, 0.075). This antioxidant properties and the protective effect of this leaf could be harnessed in the management and prevention of degenerative diseases in association with oxidative stress.
ON INFORMATICS VISUALIZATION

— Introversion and extroversion are personality traits that assess the type of interaction between people and others. Introversion and extraversion have their advantages and disadvantages. Knowing their personality, people can utilize these advantages and disadvantages for their benefit. This study compares and evaluates several machine learning models and dataset balancing methods to predict the introversion-extraversion personality based on the survey result conducted by Open-Source Psychometrics Project. The dataset was balanced using three balancing methods, and fifteen questions were chosen as the features based on their correlations with the personality self-identification result. The dataset was used to train several supervised machine-learning models. The best model for the Synthetic Minority Oversampling (SMOTE), Adaptive Synthesis Sampling (ADASYN), and Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) datasets was the Random Forest with the 10-fold cross-validation accuracy of 95.5%, 95.3%, and 71.0%. On the original dataset, the best model was Support Vector Machine, with a 10-fold cross-validation accuracy of 73.5%. Based on the results, the best balancing methods to increase the models’ performance were oversampling. Conversely, the hybrid method of oversampling-undersampling did not significantly increase performance. Furthermore, the tree-like models, like Random Forest and Decision Tree, improved performance substantially from the data balancing. In contrast, the other models, excluding the SVM, did not show a significant rise in performance. This research implies that further study is needed on the hybrid balancing method and another classification model to improve personality classification performance.
Adversarial Fragmentation of Robotic Teams Operating Under Reynolds' Rules with Bounded Communication Radius

In this paper, we examine a class of counter-swarm problems featuring small teams of antagonists. The objective of the counter-swarm algorithm is to minimize the connectivity of the team using a single adversarial pursuer. We devise a novel criterion for the connectivity of an undirected graph, based on an augmentation of its Laplacian. We prove theoretically how the criterion depends on the size and the connectivity of the graph. Next, we pose optimal control problems which use this criterion as well as the eigenvalues of the Laplacian. We show how the properties of the team dynamics and their interaction with the pursuer define an envelope within which the pursuer achieves the desired objectives.
Enhancing Hyperspectral Image Classification: A Hybrid CNN Approach with Spatial and Channel Attention Mechanisms

In the domain of hyperspectral imaging classification (HSIC) for remote sensing, recent advancements in deep learning have proven transformative. Convolutional neural networks have demonstrated immense potential in resolving these issues of HSIC, including insufficient labeled data, redundant spatial and spectral features, and overfitting. Traditional convolutional neural networks (CNNs) have effectively extracted spectral and spatial features, but 2D CNNs are limited in spatial modeling, while 3D CNNs alone struggle to distinguish spectral and spatial characteristics. Given that the accuracy of HSIC hinges on both spatial and spectral information, we proposed a hybrid-CNN model designed to mitigate the constraints of 2D and 3D CNNs. Our approach involves leveraging hybrid CNNs with spatial and channel attention (CA) mechanisms to address challenges like overfitting and model complexity. The proposed framework also improves generalization performance compared to 2D or 3D CNNs alone. Experiments were conducted on open datasets from Pavia University, Indian Pines, and Salinas to validate the proposed approach. The results demonstrate the efficacy of the hybrid CNN model with spatial and CA in consistently producing excellent classification results through thorough analyses with different deep-learning models.
Stand-Alone Broad Frequency Range Charge-Balancing System for Neural Stimulators

Safety is a critical consideration when designing an electrical neural stimulator, given the direct contact with neural tissue. This paper presents the design of a charge balancing system suitable for frequencies up to the kilohertz domain, to be used as an add-on system for stimulators over a wide range of frequencies, also covering nerve conduction blocking. It operates independently of the stimulator timing by continuously sensing the offset voltage, and applying a corrective current to the electrode, using the offset compensation technique. To ensure its stand-alone capability, the system is battery-powered, and includes a safety and start-up circuit. Electrical measurements verified the functionality of the circuit, demonstrating a residual offset of only 0.7 mV for 1 V biphasic pulses at 50 kHz. When tested for 20 kHz biphasic pulse at a 5 V amplitude, the offset was measured at -11.6 mV, which is still within the (commonly used) ±50 mV safety window.
G-Learning: Taming the Noise in Reinforcement Learning via Soft Updates

Model-free reinforcement learning algorithms such as Q-learning perform poorly in the early stages of learning in noisy environments, because much effort is spent on unlearning biased estimates of the state-action function. The bias comes from selecting, among several noisy estimates, the apparent optimum, which may actually be suboptimal. We propose G-learning, a new off-policy learning algorithm that regularizes the noise in the space of optimal actions by penalizing deterministic policies at the beginning of the learning. Moreover, it enables naturally incorporating prior distributions over optimal actions when available. The stochastic nature of G-learning also makes it more cost-effective than Q-learning in noiseless but exploration-risky domains. We illustrate these ideas in several examples where G-learning results in significant improvements of the learning rate and the learning cost.
Comprehensive evaluation of RNA-seq alignment methods based on long-read sequencing data

Long-read RNA sequencing (RNA-seq) has revolutionized our ability to comprehensively study transcriptomes, enabling the detection of full-length transcripts. The accurate alignment of long-read RNA-seq is a critical step in downstream analysis. However, with the development of alignment tools and each tool declaring the ability to handle long-read RNA-seq alignment on their own, researchers face the challenge of distinguishing and selecting the most suitable tool for their tasks. But to the best of our knowledge, there is still a lack of comprehensive evaluations on the aligners designed for long-read sequencing data. Here, we conducted a benchmark on five state-of-the-art tools on simulated long-read RNA-seq data from three levels to comprehensively assess the performance of each aligner, and provide valuable insights into the strengths and limitations of each aligner, aiding researchers in selecting the most appropriate tool for their long-read RNA-seq studies. We expected our study to advance the field of transcriptomics and promote the accurate analysis of long-read RNA-seq data in diverse biological contexts.
Passivity-Based Hybrid Systems Approach to Repetitive Learning Control for FES-Cycling with Control Input Saturation

Functional electrical stimulation (FES)-cycling is an effective method of rehabilitation for people with neuromuscular disorders. Muscle stimulation and electric motor inputs are designed to complement the rider's volitional pedaling, but open challenges remain in the analysis of the stability and robustness of the human-machine system under the influence of switching between muscle and motor inputs. Discontinuous switching between muscle stimulation inputs and motor input motivates the use of a hybrid systems analysis, reducing gain conditions compared to a switched systems analysis and yielding robustness to disturbances. In this paper, repetitive learning control (RLC)-based feedforward terms for each muscle group and electric motor are designed to improve cadence tracking and reduce high-gain feedback terms that can cause chattering effects. Muscle stimulation limits are systematically considered for the safety and comfort of the rider, and a cadence controller is designed integrating RLC and robust control terms to account for input saturation. A passivity-based analysis ensures the hybrid system is flow output strictly passive from the rider's volitional effort to the tracking error output. Moreover, the position and cadence tracking errors are shown to asymptotically converge based on a Lyapunov-like stability analysis.
A Distributed Algorithm for Solving A Time-Varying Linear Equation

This paper studies the problem of cooperatively solving a time-varying linear equation of the form $\mathrm{A}(t)x(t)= b(t)$, which always has a unique solution. Each agent has access to only some rows of the time-varying augmented matrix $[\mathrm{A}(t)\ b(t)]$. We propose a distributed algorithm for solving the time-varying linear equation. The proposed distributed algorithm enforces local solutions to track local time-varying manifolds corresponding to local linear sub-equations while simultaneously reaching a consensus. This enables all local solutions to converge to the solution of the original time-varying linear equation. Finally, the effectiveness of the proposed algorithm is demonstrated through its application in a cooperative monitoring task.
A Closer Look at Weak Supervision’s Limitations in WSI Recurrence Score Prediction

Histological examination remains the gold standard for breast cancer diagnosis, prognosis assessment and treatment guidance. Commercial molecular signature test, ONCOTYPEDX® is routinely used for luminal breast cancers to predict the probabilities of response to chemotherapy and disease recurrence. We attempted to predict RS using digital pathology and Weakly Supervised (WS) attention-based models like CLAM (Clustering-constrained Attention Multiple Instance Learning) [1] and TransMIL (Transformer based Correlated Multiple Instance Learning) [2] on our in-house dataset. In tissue samples, the malignant component is haphazardly admixed with the nonmalignant component in variable proportions. This represents a challenge for the WS attention-based models to identify high-valued diagnostic/prognostic areas within whole slide images (WSIs). To address this, we propose an interactive approach with a human in the middle (supervised) by creating a user-friendly Graphical User Interface (GUI) that allows a pathologist to provide feedback to heatmaps generated by any WS attention-based model. We incorporate the feedback from the GUI as expected scores and penalize its difference with the attention scores in the successive training process (current scores). We observe an improvement in RS prediction after the pathologist’s feedback - a 5% rise in AUC and 4% in accuracy for CLAM and a 4.5% increase in AUC and 3% in accuracy for TransMIL. We analyze the generated heatmaps and notice an improvement in cosine similarity between the expected scores and attention scores before and after the feedback - 5% and 10% increase for CLAM and TransMIL, respectively. The implementation of the proposed approach and the dataset is available for download1. Our adaptive, interactive feedback system harmonizes attention scores with expert intuition and instills higher confidence in the system’s predictions. This study establishes a potent synergy between AI and pathologists.
Feasibility Detection for Nested Codesign of Hypersonic Vehicles

Controllability and feasiblity measures are used to determine whether a given system can achieve its specified objective. However, for nonlinear systems with state constraints, the controllable and feasible sets may be highly sensitive to minor perturbations in the system's constraints, initial states and parameters. This becomes particularly important in codesign of hypersonic vehicles, where functions governing the dynamics must be estimated from expensive computational fluid dynamics simulations, and poor initialization can lead to significant waste of resources. By relaxation of the constraints and introduction of a surrogate cost, we provide a method for detecting and quantifying which constraints are violated. To demonstrate the method in a concrete example, we apply the technique to simulation of hypersonic vehicle trajectories.
Data-Driven Reachability Analysis of Lipschitz Nonlinear Systems via Support Vector Data Description

This paper is concerned with data-driven reachability analysis of discrete-time nonlinear systems without any dynamical model. We use only a number of observations of trajectories of the system to estimate the actual reachable set. With the data set, using the Support Vector Data Description (SVDD) technique, we propose a sample-based approximation method to solve the reachability analysis problem, which can be considered as a one-class classification problem. Under the framework of scenario optimization, we then derive over-approximations of the reachable set in a probabilistic sense with Lipschitz continuity and other regularity conditions. Finally, we demonstrate the proposed method on a numerical example.
Lorenzian-Chaos-Like Dynamics in Viral-Immune Cytomorphic Chips

Cytomorphic circuits on VLSI chips, which are founded on common Boltzmann thermodynamic laws between electronic transistor flow and chemical reaction flux, can rapidly simulate arbitrary deterministic and stochastic biological pathways. Circuit design and simulation are therefore useful for quantitative drug-cocktail formulation for multiple diseases, including those involving complex viral-immune interactions. Due to the presence of multiplicatively nonlinear positive and negative feedback, even simplified viral-immune interactions can lead to highly unpredictable behavior. Here, we use prior biological data to parameterize and simulate a simplified viral-immune cytomorphic circuit, whose equations correspond to those of a Lorenzian chaotic system. Cytomorphic chip data exhibit highly unpredictable behavior for certain ranges of parameters. Thus, as the recent pandemic shows, life-and-death outcomes to the same drug or drug cocktail often are highly variable or highly sensitive to patient-specific parameters. Cytomorphic circuits may be useful for designing, predicting, gaining insight into, or reducing therapeutic variability and sensitivity in the future, especially in complex disease models that are necessarily more empirical.
Data Augmentation and Pseudo-sequence of fNIRS for Depression Recognition

Depression is a mental disorder caused by factors such as genetics, life events and social influences, and has become a major public health problem worldwide. Previous studies have demonstrated the potential of functional near-infrared spectroscopy (fNIRS) in the diagnosis of depression. However, in the real medical scene, fNIRS data are difficult to obtain, limited in number and suffer from class imbalance. To overcome these problems, in this paper, we propose a novel model for depression identification based on data augmentation and pseudo-sequence of fNIRS. Specifically, the data augmentation using the time masking and warping method generates richer data. Then, a stimulation task-driven data pseudo-sequence method is designed to map the sequence data into pseudo-sequence activation images. Finally, a depression recognition model is established based on the class imbalance loss function. Experiments show that the precision of our depression recognition model reaches 0.94. This scheme transforms fNIRS data into image sequences, which provides a new solution idea for subsequent research.