Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Spectral Loss Function for Unsupervised Hyperspectral Image Segmentation

Neural networks (NNs) have gained importance in hyperspectral image (HSI) segmentation for earth observation (EO) due to their unparalleled data-driven feature extraction capability. However, in many real-life situations, ground truth is not available, and the performance of unsupervised NNs is still susceptible to enhancement. To overcome this challenge, this letter presents a new loss function to improve the performance of unsupervised HSI segmentation models. The spectral loss function, <inline-formula> <tex-math notation="LaTeX">$Sl$ </tex-math></inline-formula>, which can be included in different models, is based on the purity of the unmixing endmembers and the spectral similarity of the clusters provided by the NN to determine the classes. It is incorporated into a 3-D convolutional autoencoder (AE) to validate its performance on four standard HSI benchmarks. Furthermore, its performance has been qualitatively examined in a real case study, an oil spill without ground truth. The results show that <inline-formula> <tex-math notation="LaTeX">$Sl$ </tex-math></inline-formula> is a breakthrough in unsupervised HS segmentation, obtaining the best overall performance and highlighting the importance of spectral signatures. Additionally, the dimensional reduction is also vital in compacting the spectral information, which facilitates its segmentation. The source code is available at <uri>https://github.com/mhaut/HSI-3DSpLoss</uri>.
Impact of Z′ and universal extra dimension parameters on different asymmetries in Bs→ϕℓ+ℓ- decays

A comprehensive study of the impact of new-physics on different observables for $B_{s}\to \phi \ell^{+}\ell^{-}$ has been done. We examine the new physics models such as $Z^{\prime}$ and UED models, where the effects of new physics coming through the modification of Wilson coefficients. We have analyzed these effects through the theoretical prediction of the branching ratio, the forward-backward asymmetry, lepton polarization asymmetries and the helicity fractions of the final state meson. These all observables will definitely be measured in present and future colliders with great precision. We also pointed out that hadronic uncertainties in various physical observables are small which make them an ideal probe to establish new physics. Therefore, the measurements of these observables for the same decay would permit the detection of physics beyond the Standard Model and will also help us to distinguish between different new physics scenarios.
Foot Stepping Algorithm of Humanoids with Double Support Time Adjustment based on Capture Point Control

Recently, foot stepping strategies of humanoid robots have been actively developed for robust balancing of humanoids against disturbances. In this paper, a novel stepping algorithm adjusting double support phase (DSP) time is proposed. First, the stepping algorithm is proposed based on a model predictive control (MPC) framework for capture point (CP) control and footstep adjustment. Next, when the remaining step time is not enough to adjust the footstep, the DSP scaling method brings the next swing phase forward by reducing the DSP time, which enables the robot to maintain the balance robustly. The robust balance control performance of the proposed method is validated through simulations and experiments when the robot is walking in the presence of external pushes. A more stable balancing performance is realized compared to state-of-the-art stepping controllers.
A variable ESL/LW kinematic plate formulation for free-vibration thermoelastic analysis of laminated structures

Abstract In this article, a fully-coupled thermoelastic formulation is developed to deal with the free-vibration analysis of multilayered plate composite and sandwich structure. Some advanced theories are obtained by expanding the unknown displacement variables along the thickness direction and using equivalent-single-layer (ESL) models, layer-wise (LW) models, and variable-kinematic models. The variable-kinematic models permit to reduce the computational cost of the analyses grouping some layers of the multilayered structure with ESL models and keeping the LW models in other zones of the multilayer. This model is here extended for the free-vibration analysis of fully-coupled thermomechanical problems. The used refined models are grouped in the Carrera unified formulation (CUF), and they accurately describe the displacement field and the temperature distributions along the thickness of the multilayered plate. The governing equations are derived from the principle of virtual displacement, and the finite element method (FEM) is employed to solve them. Isotropic plates, cross-ply composite plates, and sandwich structures with composite skins and simply-supported edges are analyzed. Various aspect ratios are considered. The results, obtained with different theories within CUF context, are compared with the elasticity solutions, and other higher-order analytical and FEM solutions given in the literature. From the results, it is possible to conclude that the plate element based on the CUF is very efficient in the study of thermomechanical problems of composite structures. The variable-kinematic models combining the ESL with the LW models, permit to have a reduction of the computational costs, respect with the full LW models, preserving the accuracy of the results.
Projection-Based Multiple Notch Filtering

A method for multi-frequency notch filtering is described. The approach is based on quadratic programming, which leads to projecting the signal vector onto the orthogonal complement of the subspace spanned by a set of sinusoidal basis vectors, each corresponding to a notch frequency. The projection matrix uses a weighted inner product whose weights are samples of a standard spectral analysis window. An overlap-add method suitable for block processing is also described. The projection-based notch filter (PNF) is compared with linear-phase FIR and IIR multiple notch filters and is found to have improved amplitude and phase distortion.
A General Purpose Real-Time Optimization Strategy Applied to Minimizing Simultaneous Heating and Cooling in Buildings

Supervisory control strategies in heating, ventilating, and air-conditioning (HVAC) systems hold the potential for significant energy savings but they are difficult to set up and often depend on unreliable sensor measurements. In this paper, we propose a sensor-free approach to minimizing the energy penalties due to simultaneous heating and cooling in variable-air-volume (VAV) systems with reheat. A generalpurpose real-time optimizer is described that is easy to set-up and integrate with existing control logic. A cost function is derived that quantifies the simultaneous heating and cooling penalty that does not require any sensor measurements. The optimizer then minimizes this cost function by automatically adjusting the supply temperature setpoint in the air-handling unit. The paper describes the approach and shows results from application from field trials on a 27-zone building.
Probabilistic Pose Estimation From Multiple Hypotheses

Pose estimation assesses the 6D pose of one or many objects in a scene. Considerable attention has been dedicated to the advancement of pose estimation algorithms capable of identifying the orientation of multiple objects within a scene in cases where partial occlusion occurs. However, only a few works focus on developing a parallelizable hypotheses-based estimator that naturally handles object symmetries. These algorithms should also tackle some issues: meaningless perspectives, objects with multiple uncertain local poses but a single global correct pose, and multiple correct poses. This paper proposes a novel probabilistic algorithm for pose estimation that addresses these issues. This probabilistic algorithm combines the information from multiple cameras to achieve a unique prediction that assembles global object information. The algorithm is tested over synthetic objects that simulate these issues. It achieves a rotation error below 1.5°, and a translation error of 1.5 pixels in the datasets used. Those results suggest that the algorithm can handle the mentioned issues up to a certain accuracy. Additionally, the method is compared against a state-of-the-art methodology of the LineMOD dataset. This comparison shows that our algorithm can compete against state-of-the-art algorithms in terms of accuracy.
Unsteady shear flows of colloidal hard-sphere suspensions by dynamic simulation

The rheology during the start-up and cessation of simple shear flow has been investigated for near hard-sphere colloidal suspensions. Simulations augmented by theoretical analysis are used to determine how the non-Newtonian stress development and relaxation depend on the microstructure. Accelerated Stokesian dynamics (ASD) and Brownian dynamics (BD) simulations are used for 0.05 ≤ Pe ≤ 500 in concentrated freely flowing suspensions; the Peclet number defining the ratio of shear to thermal motion is P e = 3 π η γ a 3 / k T with η the suspending fluid viscosity, γ the shear rate, and kT the thermal energy. Theoretical predictions based on the Smoluchowski equation for dilute suspensions are made, and these are primarily used for comparison with results from BD simulations in which hydrodynamic interactions are neglected. For suspensions with hydrodynamics, simulations by ASD are used to probe start-up and flow cessation over a large range of Pe; these studies focus on solid volume fraction ϕ = 0.4, with.
Efficacy of Bidirectional LSTM Model for Network-Based Anomaly Detection

The Internet is vital in daily applications such as education, health, business, etc. Increasing the usage of the Internet and technology also increases the risk. Cyber attackers can use technology to compromise the triad of the CIA (confidentiality, integrity, and confidentiality). Malicious activities occur in our surroundings without our knowing it. Cyberattacks cannot be seen physically, though occurring to our Internet of things (IoT) devices, personal computers, laptops, and even our networking devices. Network anomaly detection is an efficient way of detecting malicious activities. Network-based anomaly detection captures and analyzes attributes of abnormal behavior in a network. Machine learning and deep learning-based approach are attractive among various known methods for network anomaly detection because they can efficiently analyze big network traffic data for malicious activities and detect zero-day attacks. A Recurrent Neural Network (RNN) model is designed to recognize the sequential characteristics of data and then use the patterns to predict the coming scenario. In this research work, seven different optimizers (Nadam, Adam, RMSprop, Adamax, SGD, Adagrad, and Ftrl), epochs, batch size, and the ratio of training testing data size are analyzed for the Bidirectional Long Short Term Memory (Bi-LSTM) network anomaly detection which provides the highest anomaly detection accuracy of 98.52% on the NSL-KDD binary dataset. The performance is compared using accuracy and F1-score metrics. Performance assessment regarding the accuracy and F1-score revealed that the proposed Bi-LSTM anomaly detection model exhibited better performance than the other existing anomaly detection methods.
Optimization of Non-Pharmaceutical Interventions For a Mutating Virus

International focus on the COVID-19 pandemic has caused a wealth of new mathematical models for capturing the impact of a virus. As COVID-19 seems to be approaching an endemic status, it is becoming increasingly clear that a new variant has a strong probability of becoming dominant in a short period of time from the first appearance. A model with the goal of representing past data and forecasting will need the flexibility to incorporate a time-evolution of variants. In this paper we explore a method for encompassing mutating viruses: coupling Ordinary Differential Equations (ODE) and Markov chains. In this approach, ODEs are used to represent classical compartmental models and Markov chains to govern the mutation of the virus between predetermined variants. This method considers a discrete variant space allowing for more simple parameter tuning to previously recorded data. A cost function is designed in order to study optimal decision making with respect to non-pharmaceutical interventions such as social distancing. This model will serve to highlight the importance of considering variants in the long-term decision making process.
Effect of nanofluid concentration on two-phase thermosyphon heat exchanger performance

An approach – relaying on application of nanofluid as a working fluid, to improve performance of the two-phase thermosyphon heat exchanger (TPTHEx) has been proposed. The prototype heat exchanger consists of two horizontal cylindrical vessels connected by two risers and a downcomer. Tube bundles placed in the lower and upper cylinders work as an evaporator and a condenser, respectively. Distilled water and nanofluid water-Al2O3 solution were used as working fluids. Nanoparticles were tested at the concentration of 0.01% and 0.1% by weight. A modified Peclet equation and Wilson method were used to estimate the overall heat transfer coefficient of the tested TPTHEx. The obtained results indicate better performance of the TPTHEx with nanofluids as working fluid compared to distilled water, independent of nanoparticle concentration tested. However, increase in nanoparticle concentration results in overall heat transfer coefficient decrease of the TPTHEx examined. It has been observed that, independent of nanoparticle concentration tested, decrease in operating pressure results in evaporation heat transfer coefficient increase.
Style-Content-Aware Adaptive Normalization Based Pose Guided for Person Image Synthesis

Most of the tasks based on pose-guided person image synthesis have obtained accurate target pose, but still have not obtained reasonable style texture mapping. In this paper, we propose a new two-stage network to decouple style and content, which aims to enhance the accuracy of pose transfer and the realism of a person appearance. Firstly, we propose an Aligned Multi-scale Content Transfer Network(AMSNet) to predict the target edge map for pose content transfer in advance, which can not only preserve clearer texture content but also alleviate spatial misalignment through advancing to transfer pose information. Secondly, we propose a new Style Texture Transfer Network(STNet) to gradually transfer the source style features to the target pose to for reasonable distribution of styles. To achieve highly similar appearance texture to the source style, we use a style-content-aware adaptive normalization method. The source style features are mapped into the same latent space as aligned content images (target pose and edge), and consistency between style texture and content is enhanced through adaptive adjustment of source style and target pose. Experimental results show that the proposed model can synthesize target images consistent with the source style, achieving superior results both quantitatively and qualitatively.
Joint Crowdsensing and Offloading Algorithms for Edge-Assisted Internet of Intelligent Vehicles

In this paper, we aim to propose a new joint crowdsensing and offloading scheme that considers the benefits of social welfare. To induce the sensing participation, we adopt the ideas of cooperative multi-agent reinforcement learning (CMARL) to develop a novel crowdsensing algorithm. Due to the limitation of computation and communication resources in the IoIV system, the Lozano, Hinojosa, and Mármol solution (LHMS) is applied to solve the IoIV resource allocation problem. Our proposed crowdsensing and offloading algorithms are tightly coupled and work together to reach a consensus with reciprocal advantages. The main merits possessed by our hybrid approach are its flexibility and adaptability to current IoIV system situations. Performance evaluations on the proposed scheme show the superiority of our joint approach by comparing it with three existing baseline protocols.
Force control for Robust Quadruped Locomotion: A Linear Policy Approach

This work presents a simple linear policy for direct force control for quadrupedal robot locomotion. The motivation is that force control is essential for highly dynamic and agile motions. We learn a linear policy to generate end-foot trajectory parameters and a centroidal wrench, which is then distributed among the legs based on the foot contact information using a quadratic program (QP) to get the desired ground reaction forces. Unlike the majority of the existing works that use complex nonlinear function approximators to represent the RL policy or model predictive control (MPC) methods with many optimization variables in the order of hundred, our controller uses a simple linear function approximator to represent policy along with only a twelve variable QP for the force distribution. A centroidal dynamics-based MPC method is used to generate reference trajectory data, and then the linear policy is trained using imitation learning to minimize the deviations from the reference trajectory. We demonstrate this compute-efficient controller on our robot Stoch3 in simulation and real-world experiments on indoor and outdoor terrains with push recovery.
Forecasting National-Level Self-Harm Trends With Social Networks

Self-harm pertains to actions of self-inflicted poisoning or injury that lead to either non-fatal injuries or death, irrespective of the individual’s intention. Self-harm incidents not only cause loss to individuals but also incur a negative impact on the nation’s economy. Studies have demonstrated an increase in trends of self-harm that are correlated with the emergence of technological advancements and swift urban expansion in developing countries. The capacity to nowcast and forecast national-level patterns of self-harm trends could be imperative to policymakers and stakeholders in the public health sector, as it would enable them to implement prompt measures to counteract the underlying factors or avert these projected calamities. Prior research has utilized historical data to predict self-harm trends at the population level in various nations using conventional statistical forecasting methods. However, in some countries, such historical statistics may be challenging to obtain or insufficient for accurate prediction, impeding the ability to comprehend and project the national self-harm landscape in a timely manner. This paper proposes FAST, a framework designed to forecast self-harm patterns at the national level by analyzing mental signals obtained from a large volume of social media data. These signals serve as a proxy for real-world population mental health that could be used to enhance the forecastability of self-harm trends. Specifically, language-agnostic language models are first trained to extract different mental signals from collected social media messages. Then, these signals are aggregated and processed into multi-variate time series, on which the time-delay embedding algorithm is applied to transform into temporal embedded instances. Finally, various machine learning regressors are validated for their forecastability. The proposed method is validated through a case study in Thailand, which utilizes a set of 12 mental signals extracted from tweets to forecast death and injury cases resulting from self-harm. The results show that the proposed method outperformed the traditional ARIMA baseline by 43.56% and 36.48% on average in terms of MAPE on forecasting death and injury cases from self-harm, respectively. As far as current understanding permits, our research represents the initial exploration of utilizing aggregated social media information for the purposes of nowcasting and forecasting trends of self-harm on a nationwide scale. The results not only provide insight into improved forecasting techniques for self-harm trends but also establish a foundation for forthcoming social-network-driven applications that hinge on the capacity to predict socioeconomic factors.
Relay Pursuit for Multirobot Target Tracking on Tile Graphs

In this work, we address a visbility-based target tracking problem in a polygonal environment in which a group of mobile observers try to maintain a line-of-sight with a mobile intruder. We build a bridge between data mining and visibility-based tracking using a novel tiling scheme for the polygon. First, we propose a tracking strategy for a team of guards located on the tiles to dynamically track an intruder when complete coverage of the polygon cannot be ensured. Next, we propose a novel variant of the Voronoi Diagram to construct navigation strategies for a team of co-located guards to track an intruder from any initial position in the environment. We present empirical analysis to illustrate the efficacy of the proposed tiling scheme. Simulations and testbed demonstrations are present in a video attachment.
Amplification of noisy gene expression by protein burden: An analytical approach

The overexpression of many proteins can often have a detrimental impact on cellular growth. This expression-growth coupling leads to positive feedback - any increase of intracellular protein concentration reduces the growth rate of cell size expansion that in turn enhances the concentration via reduced dilution. We investigate how such feedback amplifies intrinsic stochasticity in gene expression to drive a skewed distribution of the protein concentration. Our research provides the analytical expression of the distribution after solving the associated Chapman-Kolmogorov equation. With these results, we quantify the enhancement of noise/skewness as a function of expression-growth coupling. This analysis has important implications for the expression of stress factors, where high levels provide protection from stress, but come at the cost of reduced cellular proliferation. Finally, we connect these analytical results to the case of an actively degraded gene product, where the degradation machinery is working close to saturation.
Learnable Tegotae-based Feedback in CPGs with Sparse Observation Produces Efficient and Adaptive Locomotion

Central Pattern generators (CPG) are a biologically inspired, decentralized control architecture that enables model-free, but yet adaptively stable and computational lightweight locomotion capabilities on complex robots. Nevertheless, no unified design guidelines for closed-loop CPG controllers are available in the literature. Therefore, we propose a task-distributed, end-to-end trainable, closed-loop CPG control policy by generalizing and extending Tegotae control. The Tegotae approach modulates CPG activity by quantifying the discrepancy between internal belief states and environmental reactions. Spontaneous and adaptive gait formation towards situationally efficient locomotion patterns are intrinsic properties of Tegotae control. The Tegotae control policy is trained and benchmarked in simulation on a 1D hopping robot. We found that our approach can learn efficient and adaptive locomotion on minimal feedback information, while out-performing unstructured, classic reinforcement learning policies of equal complexity. To the best of our knowledge, this is the first study to fully generalize the Tegotae approach and construct unimpeded, end-to-end trainable Tegotae control policies.