Miracle-dz/UniFAR-FTUs huggingface dataset:
An Equation Free Algorithm Accurately Simulates Macroscale Shocks Arising From Heterogeneous Microscale Systems

Scientists and engineers often create accurate, trustworthy, computational simulation schemes—but all too often these are too computationally expensive to execute over the time or spatial domain of interest. The equation-free approach is to marry such trusted simulations to a framework for numerical macroscale reduction—the patch dynamics scheme. This article extends the patch scheme to scenarios in which the trusted simulation resolves abrupt state changes on the microscale that appear as shocks on the macroscale. Accurate simulation for problems in these scenarios requires capturing the shock within a novel patch, and also modifying the patch coupling rules in the vicinity in order to maintain accuracy. With these two extensions to the patch scheme, straightforward arguments derive consistency conditions that match the usual order of accuracy for patch schemes. The new scheme is successfully tested to simulate a heterogeneous microscale partial differential equation. This technique will empower scientists and engineers to accurately and efficiently simulate, over large spatial domains, multiscale multiphysics systems that have rapid transition layers on the microscale.
Interval-Based Visual-LiDAR Sensor Fusion

Since cameras and Light Detection and Ranging (LiDAR) sensors provide complementary information about the environment, it is beneficial for mobile robot localization to fuse their information by assigning distances measured by the LiDAR to visual features detected in the image. However, existing approaches neglect the uncertainty of the fused information or model it in an optimistic way (e.g., without taking extrinsic calibration errors into account). Since the actual distribution of errors during sensor fusion is often unknown, we assume to only know bounds (or intervals) enclosing the errors. Consequently, we propose to use interval analysis to propagate the error from the input sources to the fused information in a straightforward way. To show the applicability of our approach, we use the fused information for dead reckoning. Since interval analysis is used, the result of our approach are intervals that are guaranteed to enclose the robot's true pose. An evaluation using real data shows that we are indeed able to localize the robot in a guaranteed way. This enables us to detect faults of an established approach, which neglects the uncertainty of the fused information, in three out of ten cases.
Powers of Large Matrices on GPU Platforms to Compute the Roman Domination Number of Cylindrical Graphs

The Roman domination in a graph <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> is a variant of the classical domination, defined by means of a so-called Roman domination function <inline-formula> <tex-math notation="LaTeX">$f\colon V(G)\to \{0,1,2\}$ </tex-math></inline-formula> such that if <inline-formula> <tex-math notation="LaTeX">$f(v)=0$ </tex-math></inline-formula> then, the vertex <inline-formula> <tex-math notation="LaTeX">$v$ </tex-math></inline-formula> is adjacent to at least one vertex <inline-formula> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$f(w)=2$ </tex-math></inline-formula>. The weight <inline-formula> <tex-math notation="LaTeX">$f(G)$ </tex-math></inline-formula> of a Roman dominating function of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> is the sum of the weights of all vertices of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>, that is, <inline-formula> <tex-math notation="LaTeX">$f(G)=\sum _{u\in V(G)}f(u)$ </tex-math></inline-formula>. The Roman domination number <inline-formula> <tex-math notation="LaTeX">$\gamma _{R}(G)$ </tex-math></inline-formula> is the minimum weight of a Roman dominating function of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>. In this paper we propose algorithms to compute this parameter involving the <inline-formula> <tex-math notation="LaTeX">$(\min,+)$ </tex-math></inline-formula> powers of large matrices with high computational requirements and the GPU (Graphics Processing Unit) allows us to accelerate such operations. Specific routines have been developed to efficiently compute the <inline-formula> <tex-math notation="LaTeX">$(\min,+)$ </tex-math></inline-formula> product on GPU architecture, taking advantage of its computational power. These algorithms allow us to compute the Roman domination number of cylindrical graphs <inline-formula> <tex-math notation="LaTeX">$P_{m}\Box ~C_{n}$ </tex-math></inline-formula> i.e., the Cartesian product of a path and a cycle, in cases <inline-formula> <tex-math notation="LaTeX">$m=7,8$ </tex-math></inline-formula>,9 <inline-formula> <tex-math notation="LaTeX">$n\geq 3$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$m\geq $ </tex-math></inline-formula>10<inline-formula> <tex-math notation="LaTeX">$, n\equiv 0\pmod 5$ </tex-math></inline-formula>. Moreover, we provide a lower bound for the remaining cases <inline-formula> <tex-math notation="LaTeX">$m\geq $ </tex-math></inline-formula>10<inline-formula> <tex-math notation="LaTeX">$, n\not \equiv 0\pmod 5$ </tex-math></inline-formula>.
Self-Supervised GANs With Similarity Loss for Remote Sensing Image Scene Classification

With the development of supervised deep neural networks, classification performance on existing remote sensing scene datasets has been markedly improved. However, supervised learning methods rely heavily on large-scale tagged examples to obtain a better prediction performance. The lack of large-scale tagged remote sensing scene images has become the primary bottleneck in scene classification. To deal with this issue, a novel scene classification method using self-supervised gated self-attention generative adversarial networks (GANs) with similarity loss is proposed. Specifically, the gated self-attention module is first introduced into GANs to focus on key scene areas and filter useless information for strengthening feature representations. Then, the pyramidal convolution block is introduced into the residual block of the discriminator to capture different levels of details in the image using different types of filters with varying sizes and depths for enhancing the feature representations of the discriminator. Additionally, a novel similarity loss item is integrated into the discriminator to leverage self-supervised learning. Besides, spectral normalization is introduced into both the generative network and discriminative network to stabilize training and enhance feature representations. The architecture of multilevel feature fusion is integrated into the discriminative network to achieve more discriminant representation. Experimental results on the AID and NWPU-RESISC45 datasets show that the proposed method achieves the best performance compared to the existing unsupervised classification methods.
A Robust Sparse RLS-Volterra Nonlinear Equalizer Using ℓ₀-Regularization for 4 × 150 Gbit/s IMDD-Based Optical Interconnect

Volterra equalizer (VE) is a well-known and effective algorithm to deal with the linear and nonlinear distortions in optical interconnect, but the high computational complexity hinders its practical application. Generally, sparse VEs based on <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>- or <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-regularization are good ways to reduce the complexity by discarding some inessential taps. However, a tap threshold needs to be chosen in these sparse VEs like the threshold-based pruned retraining VE (TR-VE) to decide the discarded taps. And this tap threshold should be adjusted fine to balance the reduced complexity and equalization performance, especially when the testing environments alter. Thus, the reduced complexity in these sparse VEs may fluctuate. To address this issue, a robust and stable complexity reduced sparse VE using <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-regularization (<inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-SR-VE) is proposed in this paper. The recursive least square (RLS) algorithm is used to replace the least mean square algorithm for the faster convergence speed and better equalization performance. The complexity of this equalizer depends on its parameters but not the tap threshold. Once the equalizer parameters are determined, the complexity would not change with the system characteristics, contributing to higher practicability. In our experiment, a 150 Gbit/s PAM8 signal transmission system based on intensity modulation and direct detection (IMDD) is achieved, and a dual-drive Mach-Zehnder modulator for optical single-sideband signal generation is used to mitigate the power fading effect. The experimental results show that with the help of <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-SR-VE, the reduced complexity percentage is stable at 66.18% compared with the RLS-based VE, even after 75 km standard single-mode fiber (SSMF) transmission. By using this equalizer, <inline-formula> <tex-math notation="LaTeX">$4\times 150$ </tex-math></inline-formula> Gbit/s PAM8 signals have also been successfully transmitted over 30 km SSMF at C-band. The reduced complexity variation of <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-TR-VE is >20%, but the reduced complexity of the proposed <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-SR-VE is stable at 60% even after 30 km SSMF for all four lanes.
The Role Played by Mass, Friction, and Inertia on the Driving Torques of Lower-Limb Gait Training Exoskeletons

Lower-limb gait training exoskeletons are extraordinary tools used to reduce the burden of locomotor impairments in patients with neurological diseases. However, the transparent operation and backdrivability of such systems still needs to be improved. Moreover, it is not completely understood how the mechanical design of the robot can interfere with the user’s gait pattern. Aiming to address these shortcomings, we investigate the required driving torques and mechanical power to move the legs under a wide range of actuator’s mass, inertia and friction and thigh/shank lengths. We used the ExoRoboWalker, a six-degree-of-freedom lower-limb exoskeleton, to build a framework model based on the double-pendulum approach integrated with the actuators’ mechanical impedance. Decoupled joint apparent inertia and the Rayleigh’s dissipation function were introduced to the robot’s Lagrangian to consider the effects of gearhead ratio and joint friction in the model. Firstly, it is presented the isolated effect of such variables on the required driving torques of the system. The oscillation frequency for the minimum joint torque was severely affected by variations of inertia, friction, and links length. Secondly, the combined effect of the actuator’s mass, inertia and friction reveled that a heavier exoskeleton with low-ratio transmission required less torque and mechanical power than a lighter one with greater reduction ratio depending on the oscillation frequency, which is remarkable. These findings have important implications for new designs of lower-limb gait training systems.
Tweet Classification Using Conversational Relationships

To analyze disasters in real time, we need a method to collect social networking service (SNS) posts that mention a disaster without using search words. This is because even though most disaster-focused methods use the search words to collect the targeted posts from microblogs, the search-words-based collection schemes miss posts that exclude the search words. In this paper, we propose a method to directly classify posts as disaster-related and non-disaster-related using conversations on Twitter. In this paper, a tweet-merge model and a cluster-merge model are introduced, which modify the latent Dirichlet allocation (LDA)-based topic model and the k-means clustering algorithm to handle the conversation threads of Twitter. Experimental results on two tweets about large-scale disasters in Japan demonstrate the effectiveness of the method.
Predicting Hospital No-Shows Using Machine Learning

All over the globe, significant amounts of patients miss their appointments without cancelling in time or even cancelling at all, resulting in billions of dollars wasted yearly due to increased idle time, overtime and waiting time that the other patients and hospitals face. Hospitals are actively trying to implement methods to try to reduce the idle time caused by patient no-shows by using overbooking and reminder systems. However, these two methods can be very costly. Overbooking can lead to patient dissatisfaction and constant personalized reminders, such as phone calls, to every patient can be annoying and costly in terms of manpower. This paper focuses on offering a solution which mitigates the global phenomenon of medical no-shows by creating a machine learning model using existing patient datasets to discover patterns and relationships between multiple patient variables and their tendency to miss appointments. Therefore, the likelihood of a patient showing up, given their information, may be predicted. The machine learning model used to form the solution predictive model is based on the decision tree classification algorithm. Furthermore, a scheduling system was implemented such that the overall model detects whether a patient has a risk of missing an appointment with a 95% accuracy, upon which it automatically enables the risky patient’s schedule slot for overbooking and notifies medical staff or administration to contact them accordingly.
A Survey of Routing Protocols for Underwater Wireless Sensor Networks

Underwater wireless sensor network (UWSN) is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. However, UWSNs suffer from various limitations and challenges: high ocean interference and noise, high propagation delay, narrow bandwidth, dynamic network topology, and limited battery energy of sensor nodes. The design of routing protocols is one of the solutions to address these issues. A routing protocol can efficiently transfer the data from the source node to the destination node in the network. This article presents a review of underwater routing protocols for UWSNs. We classify the existing underwater routing protocols into three categories: energy-based, data-based, and geographic information-based protocols. In this article, we summarize the underwater routing protocols proposed in recent years. The proposed protocols are described in detail and give advantages and disadvantages. Meanwhile, the performance of different underwater routing protocols is analyzed in detail. Besides, we also present the research challenges and future directions of underwater routing protocols, which can help the researcher better explore in the future.
Effect of oxide ions on separation factors of actinides from lanthanides in reductive extraction

Abstract The distribution coefficients of Am, Ce, and Eu between the salt and metal phases were measured at 1073 K in a reductive extraction system of equimolar NaCl-KCl melt and liquid Ga. By changing the solute concentrations, it was observed that the distribution coefficients were dependent on the oxide ion concentration in the system, possibly due to the formation of such compounds as AmO+. In addition, the mass balance of Am was possibly affected by the formation and precipitation of its oxychlorides and/or oxides in some runs. Taking these observations into consideration, the separation factor between Am and Ce was found to be around 30 in the present system.
Boundary Regions and Their Contribution to Current Spreading in Grounded Gate Nmosts

3-D-TCAD results are presented showing current density profiles for triggered GGNmosts with substantial, and with minimal, drain ballast resistance. That same variation is repeated on two more GGNmosts with reduced source ballast resistance, which reduces the electron–hole current ratio. The simulated current density profiles fill only part of the device, while in the complementary part of the device no current flows. Between these parts, boundary regions exist in which the current density decreases from its peak value to zero. The width of these boundary regions is set by carrier diffusion, and can therefore not be arbitrarily small. As this article shows, the result is that the boundary regions can contribute to GGNmost current spreading. Moreover, the boundary regions can exist in a stable manner over time, and this article shows the physical mechanism by which this happens. Finally, this article shows how boundary regions manifest themselves in TLP <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>)-curves: features relating to boundary regions are identified in TLP <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>)-curves simulated by 3-D-TCAD, and then replicated in measured TLP <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>)-curves. Bottom line is that boundary regions can help spread current in a GGNmost.
Reinforcement Learning based Scheduling for Cooperative EV-to-EV Dynamic Wireless Charging

Previous Electric Vehicle (EV) charging scheduling methods and EV route planning methods require EVs to spend extra waiting time and driving burden for a recharge. With the advancement of dynamic wireless charging for EVs, Mobile Energy Disseminator (MED), which can charge an EV in motion, becomes available. However, existing wireless charging scheduling methods for wireless sensors, which are the most related works to the deployment of MEDs, are not directly applicable for the scheduling of MEDs on city-scale road networks. We present MobiCharger: a Mobile wireless Charger guidance system that determines the number of serving MEDs, and the optimal routes of the MEDs periodically (e.g., every 30 minutes). Through analyzing a metropolitan-scale vehicle mobility dataset, we found that most vehicles have routines, and the temporal change of the number of driving vehicles changes during different time slots, which means the number of MEDs should adaptively change as well. Then, we propose a Reinforcement Learning based method to determine the number and the driving route of serving MEDs. Our experiments driven by the dataset demonstrate that MobiCharger increases the medium state-of-charge and the number of charges of all EVs by 50% and 100%, respectively.
Allocation of Always-On State Retention Storage for Power Gated Circuits—Steady-State- Driven Approach

It is generally known that a considerable portion of flip-flops in circuits is occupied by the ones with mux-feedback loop (called self-loop), which is the critical (inherently unavoidable) bottleneck in minimizing total (always-on) storage size for the allocation of nonuniform multibits for retaining flip-flop states in power gated circuits. This is because it is necessary to replace every self-loop flip-flop with a distinct retention flip-flop with at least one-bit storage for retaining its state since there is no clue where the flip-flop state, when waking up, comes from, i.e., from the mux-feedback loop or from the driving flip-flops other than itself. This work breaks this bottleneck by safely treating a large portion of the self-loop flip-flops as if they were the same as the flip-flops with no self-loop. Specifically, we design a novel mechanism of steady-state monitoring, operating for a few cycles just before sleeping, on a partial set of self-loop flip-flops, by which the expensive state retention storage is never be needed for the monitored flip-flops, contributing to a significant saving on the total size of the always-on state retention storage for power gating. Through experiments with benchmark circuits, it is shown that our proposed method is able to reduce the total number of retention bits by 27.12% on average when at most 2-bit retention flip-flop is used, saving standby power by 19.41% compared with the state-of-the-art conventional method.
Transplacental Transfer of PCBs and Chlorinated Hydrocarbon Pesticides from the Pregnant Striped Dolphin (Stenella coeruleoalba) to Her Fetus

Transplacental transfer of chlorinated hydrocarbons such as PCBs, DDT compounds, HCH isomers and HCB was determined in a pregnant striped dolphin just before parturition. The transfer rates of chlorinated hydrocarbons in the striped dolphin through parturition were estimated as follows: PCBs 4.0%, ΣDDT 4.7%, ΣHCH 8.9% and HCB 9.4%. The concentration ratios of chlorinated hydrocarbons in the blubber of the fetus to that of the mother dolphin were found to be in the order of HCB > HCH isomers > DDT compounds > PCBs. Especially in PCB congeners, these ratios gradually decreased with the increase of chlorine atoms substituted in biphenyls.These observations indicate that the more lipophilic chemicals, such as higher chlorinated biphenyls and DDT compounds, are less transferable from mother to fetus. The transfer characteristics of chlorinated hydrocarbons can be explained by their equilibrium partitionings between blood and blubber, resulting from the differences of lipid compositions in each.
Indirectly Heated Switch as a Platform for Nanosecond Probing of Phase Transition Properties in Chalcogenides

Although phase-change materials (PCMs) have been studied for more than 50 years, temperature-dependent characterization of the phase transition dynamics remains challenging due to the lack of nanosecond-nanoscale thermometry. In this article, we utilize the four-terminal, indirectly heated phase-change switch (IPCS), which was originally designed for nonvolatile radio frequency (RF) applications, as an ultrafast electrothermal platform to study PCM. We propose a novel experimental setup that allows nanosecond probing of the transient resistance of the PCM, beyond the melting temperature (>1100 K), due to the built-in electrical isolation between the PCM path and the thermal actuation path of the IPCS. The embedded metallic heater can induce reversible phase transitions between the crystalline and amorphous phases of the PCM. Our platform enables simultaneous measurements of the dynamics of PCM resistance (as a probe for the phase of the material) and heater temperature, during the application of heating pulses. Furthermore, we map the surface temperature of the IPCS at steady state by scanning thermal microscopy (SThM) and show the effect of cooling by electrodes in devices with overlap between the heater and PCM contacts. Our method can be used to study chalcogenides and other amorphous semiconductors for reconfigurable electronics and neuromorphic hardware.