Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Changes by Learning No Changes: Data-Enclosing-Ball Minimizing Autoencoders for One-Class Change Detection in Multispectral Imagery

Change detection is a long-standing and challenging problem in remote sensing. Very often, features about changes are difficult to model beforehand, thus making the collection of changed samples a challenging task. In comparison, it is much easier to collect numerous no-change samples. It is possible to define a change detection approach using only easily available annotated no-change samples, which we henceforth call one-class change detection. Autoencoder networks being trained on no-change data are natural candidates for addressing this task due to their superior performance when compared with other one-class classification models. However, the autoencoders usually suffer from the problem of overgeneralization, i.e., they tend to generalize too well, thus risking properly reconstructing changed samples. In this article, we propose a novel data-enclosing-ball minimizing autoencoder (DebM-AE) that is trained with dual objectives—a reconstruction error criterion and a minimum volume criterion. The network learns a compact latent space, where encodings of no-change samples have low intraclass variance, which as counterpart has the identification of changed instances. We conducted extensive experiments on three real-world datasets. Results demonstrate advantages of the proposed method over other competitors. We make our data and code publicly available (https://gitlab.lrz.de/ai4eo/reasoning/DebM-AE; https://github.com/lcmou/DebM-AE).
Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Deep Learning Approach

Accurate bathymetric data is crucial for marine and coastal ecosystems. A lot of studies have been carried out for nearshore bathymetry using satellite data. The approach adopted extensively in shallow water depths estimation has recently been one of empirical models. However, the linear empirical model is simple and only takes limited band information at each bathymetric point into consideration. It may be not suitable for complex environments. In this paper, a deep learning framework was proposed for nearshore bathymetry (DL-NB) from ICESat-2 LiDAR and Sentinel-2 Imagery datasets. The bathymetric points from the spaceborne ICESat-2 LiDAR were extracted instead of in situ measurements. By virtue of the two-dimensional convolutional neural network (2D CNN), DL-NB can make full use of the initial multi-spectral information of Sentinel-2 at each bathymetric point and its adjacent areas during the training. Based on the trained model, the bathymetric maps of several study areas were produced including the Appalachian Bay (AB), Virgin Islands (VI), and Cat Island (CI) of the United States. The performance of DL-NB was evaluated by empirical method, machine learning method and multilayer perceptron (MLP). The results indicate that the accuracy of the DL-NB is better than comparative methods can in nearshore bathymetry. After quantitative analysis, the RMSE of DL-NB could achieve 1.01 m, 1.80 m and 0.28 m in AB, VI and CI respectively. Given the same data conditions, the proposed method can be applied for high precise global scale and multitemporal nearshore bathymetric maps generation, which are beneficial to marine environmental change assessment and conservation.
A Hybrid Multistage DNN-Based Collaborative IDPS for High-Risk Smart Factory Networks

New industrial control systems (ICSs) that have been modernized with the industrial Internet of Things (IIoT) are exposed to cyber-attacks that exploit IIoT vulnerabilities. Numerous intrusion detection systems (IDSs) have therefore been proposed to secure ICSs, many of which are based on machine learning, specifically deep neural networks (DNNs). Most of the proposed DNN-based solutions rely on single deep learning models and could be less costly in terms of ICS latency. However, they might have difficulties understanding the increasingly complex data distribution of intrusion patterns. Moreover, single deep learning models may not be effective in capturing the specific patterns of minority classes in highly imbalanced datasets, which is usually the case in cyber-security. Therefore, this paper proposes a novel hybrid multistage DNN-based intrusion detection and prevention system (IDPS) with better accuracy for critical ICSs that cannot afford to compromise on security to improve latency. The proposed approach sequentially learns the decision boundaries of the data that were misclassified or classified with low confidence by previous DNNs. Moreover, it incorporates a collaborative intrusion prevention system (IPS) with an emergency response schema that automatically mitigates attacks as soon as anomalies are detected. The results of experimental evaluations performed on different datasets demonstrate the effectiveness of the proposed solution.
Spectral variations in gamma-ray bursts

We report the initial results of an analysis of 19 gamma‐ray burst energy spectra recorded by the Venera‐13 and 14 SIGNE experiments. The complete data‐base includes some 150 events, with 5 channel energy spectra taken in the 50–700 keV range with 0.5 s time resolution, for 64 s. The observed spectra of each event analyzed were fitted with a power law, bremsstrahlung, and thermal synchrotron model, for each time interval containing a statistically significant number of counts. Although specific examples may be found which exhibit hard‐to‐soft evolution, or a correlation between the effective temperature kT of a fit and the time history, cases of ‘‘random’’ spectral evolution are also present, and there appears to be no single way to characterize spectral evolution. There is also no clear preference for a particular model fit in the data analyzed so far.
Bistatic Radar Positioning of a Moving Target, Using Only Range and Range-Rate Measurements

Of the many applications of bistatic (BS) radars, this article addresses the possibility to replace a monostatic radar with its highly directive antenna, with a coherent BS radar using a single short baseline between the transmitter and the receiver and no direction measurements. The method applies to a moving target and requires both <italic>range</italic> and <italic>range-rate</italic> BS measurements. Theory and field-test results demonstrate, on a two-dimensional scene, how several consecutive pairs of BS <italic>range</italic> <inline-formula><tex-math notation="LaTeX">$( r )$</tex-math></inline-formula> and <italic>range-rate</italic> <inline-formula><tex-math notation="LaTeX">$( {\dot{r}} )$</tex-math></inline-formula> measurements of a moving target can provide the target's position, velocity, and heading (<italic>x, y, v<sub>x</sub>, v<sub>y</sub></italic>). In a three-dimensional scene, with five estimated target parameters (<italic>x, y, h, v<sub>x</sub>, v<sub>y</sub></italic>), a second receiver is needed, not in-line with the original baseline. The <inline-formula><tex-math notation="LaTeX">$( {r,\dot{r}} )$</tex-math></inline-formula> measurements distinguish the suggested concept from most other multistatic radars as well as the well-known multilateration positioning or interferometric angle-of-arrival estimation.
Additivity in cost spanning tree problems

We characterize a rule in cost spanning tree problems using an additiv- ity property and some basic properties. If the set of possible agents has at least three agents, these basic properties are symmetry and separability. If the set of possible agents has two agents, we must add positivity. In both characterizations we can replace separability by population monotonicity.
Late stages of accumulation and early evolution of the planets.

Recently developed solutions of problems are discussed that were traditionally considered fundamental in classical solar system cosmogony: determination of planetary orbit distribution patterns, values for mean eccentricity and orbital inclinations of the planets, and rotation periods and rotation axis inclinations of the planets. Two important cosmochemical aspects of accumulation are examined: the time scale for gas loss from the terrestrial planet zone, and the composition of the planets in terms of isotope data. It was concluded that the early beginning of planet differentiation is a function of the heating of protoplanets during collisions with large (thousands of kilometers) bodies. Energetics, heat mass transfer processes, and characteristic time scales of these processes at the early stages of planet evolution are considered.
Antimicrobial and Antioxidant Activity of Different Honey Samples from Beekeepers and Commercial Producers

Honey contains compounds with antioxidant and antibacterial capacities, such as phenolic compounds and carotenoids. The current analysis evaluates the antioxidant and antibacterial activity of 100 honey samples from beekeepers from Slovakia and commercially purchased ones. Honey samples were diluted to 50%, 25%, 12.5%, and 6.25% concentrations. The antimicrobial activity of honey samples was evaluated against three Gram-positive, three Gram-negative bacteria, and four Candida spp. by well diffusion method. The highest antimicrobial effect of all honey concentrations was expressed as the size of the inhibition zone and was found against Pseudomonas aeruginosa among Gram-negative bacteria, Bacillus subtilis among Gram-positive bacteria, and Candida tropicalis among yeasts. Antibiotics used in the study showed the highest antimicrobial effect compared to all concentrations of honey samples. Slovakian honey from beekeepers and commercial honey samples from the Slovak market showed variable inhibitory effectiveness against microorganisms. The honey concentration of 50% was found the most effective. Lower concentrations of honey exhibited no effect against yeasts. The best antioxidant activity was found in a sample of buckwheat honey yielding 70.83% of DPPH inhibition and 2373.85 μg/g TEAC. Overall, better antioxidant activity was evaluated in honeydew honey.
Computation Offloading for Mobile Cloud Computing Frameworks and Techniques

With mobile devices’ limited resources, computing the increasing amount of data locally puts strain on their batteries, processors, storage and bandwidth making it necessary to offload computeheavy tasks to a close proximity edge cloud server. A research field known as Mobile Cloud Computing (MCC) has emerged to address the shortcomings of mobile devices by means of offloading. However, cost of operation and maintenance of cloud servers alongside the mobile nature of devices that could potentially cause connectivity issues are some of the most prominent challenges that face an efficient offloading process.
Cambrian alkaline rocks at Lobo Hill, Torrance County, New Mexico: More evidence for a Cambrian-Ordovician aulacogen

Every fall since 1950, the New Mexico Geological Society (NMGS) has held an annual Fall Field Conference that explores some region of New Mexico (or surrounding states). Always well attended, these conferences provide a guidebook to participants. Besides detailed road logs, the guidebooks contain many well written, edited, and peer-reviewed geoscience papers. These books have set the national standard for geologic guidebooks and are an essential geologic reference for anyone working in or around New Mexico.
A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance

There is no well-defined utility function for existing multipath TCP algorithms. Therefore, network utility maximization (NUM) for MPTCP is a complex undertaking. To resolve this, we develop a novel condition under which Kelly’s NUM mechanism may be used to explicitly compute the equilibrium. We accomplish this by defining a new utility function for MPTCP by employing Tullock’s rent-seeking paradigm from game theory. We investigate the convergence of no-regret learning in the underlying network games with continuous actions. Based on our understanding of the design space, we propose an original MPTCP algorithm that generalizes existing algorithms and strikes a good balance among the important properties. We implemented this algorithm in the Linux kernel, and we evaluated its performance experimentally.
Microglial Dynamics Modulate Vestibular Compensation in a Rodent Model of Vestibulopathy and Condition the Expression of Plasticity Mechanisms in the Deafferented Vestibular Nuclei

Unilateral vestibular loss (UVL) induces a vestibular syndrome composed of posturo-locomotor, oculomotor, vegetative, and perceptivo-cognitive symptoms. With time, these functional deficits progressively disappear due to a phenomenon called vestibular compensation, known to be supported by the expression in the deafferented vestibular nuclei (VNs) of various adaptative plasticity mechanisms. UVL is known to induce a neuroinflammatory response within the VNs, thought to be caused by the structural alteration of primary vestibular afferents. The acute inflammatory response, expressed in the deafferented VNs was recently proven to be crucial for the expression of the endogenous plasticity supporting functional recovery. Neuroinflammation is supported by reactive microglial cells, known to have various phenotypes with adverse effects on brain tissue. Here, we used markers of pro-inflammatory and anti-inflammatory phenotypes of reactive microglia to study microglial dynamics following a unilateral vestibular neurectomy (UVN) in the adult rat. In addition, to highlight the role of acute inflammation in vestibular compensation and its underlying mechanisms, we enhanced the inflammatory state of the deafferented VNs using systemic injections of lipopolysaccharide (LPS) during the acute phase after a UVN. We observed that the UVN induced the expression of both M1 proinflammatory and M2 anti-inflammatory microglial phenotypes in the deafferented VNs. The acute LPS treatment exacerbated the inflammatory reaction and increased the M1 phenotype while decreasing M2 expression. These effects were associated with impaired postlesional plasticity in the deafferented VNs and exacerbated functional deficits. These results highlight the importance of a homeostatic inflammatory level in the expression of the adaptative plasticity mechanisms underlying vestibular compensation. Understanding the rules that govern neuroinflammation would provide therapeutic leads in neuropathologies associated with these processes.
Theory of ultracold atomic Fermi gases

The physics of quantum degenerate atomic Fermi gases in uniform as well as in harmonically trapped configurations is reviewed from a theoretical perspective. Emphasis is given to the effect of interactions that play a crucial role, bringing the gas into a superfluid phase at low temperature. In these dilute systems, interactions are characterized by a single parameter, the s-wave scattering length, whose value can be tuned using an external magnetic field near a broad Feshbach resonance. The BCS limit of ordinary Fermi superfluidity, the Bose-Einstein condensation (BEC) of dimers, and the unitary limit of large scattering length are important regimes exhibited by interacting Fermi gases. In particular, the BEC and the unitary regimes are characterized by a high value of the superfluid critical temperature, on the order of the Fermi temperature. Different physical properties are discussed, including the density profiles and the energy of the ground-state configurations, the momentum distribution, the fraction of condensed pairs, collective oscillations and pair-breaking effects, the expansion of the gas, the main thermodynamic properties, the behavior in the presence of optical lattices, and the signatures of superfluidity, such as the existence of quantized vortices, the quenching of the moment of inertia, and the consequences ofmore » spin polarization. Various theoretical approaches are considered, ranging from the mean-field description of the BCS-BEC crossover to nonperturbative methods based on quantum Monte Carlo techniques. A major goal of the review is to compare theoretical predictions with available experimental results. « less
Cluster‐based improvement rates for trust establishment models in single or distributed multi‐agent systems

Intelligent agents within open and dynamic multi‐agent systems are becoming increasingly capable in their decision‐making abilities and rely upon the notion of trustworthiness to determine which agents to interact with. To improve the overall performance of trust establishment models which trustees individually select and equip to improve their trustworthiness with trustors, while balancing the resources being spent, a cluster‐based trust establishment model update mechanism is proposed. This cluster‐based approach is applicable to robust trust establishment models which utilize dynamic improvement and disimprovement rate variables to adjust a trustee's behaviors toward trustors to improve or maintain trust with the trustor. By storing a single trust establishment model's dynamic improvement and disimprovement rate variables independently for each trustor and by clustering similar trustors together based on observed experiences, a model can more accurately update a trustee's behaviors toward trustors. Through simulated experiments comparing the performance of the existing integrated trust establishment (ITE) model with and without the cluster‐based approach, with varying trustor to trustee ratios to diversify the agent behaviors, the cluster‐based approach consistently improves a trustee's ability to fully meet a trustor's needs, for less resources than ITE, while minimizing the corresponding impact to the trustee's overall trust.
Accumulation-Mode Device: Experimental of LDMOS With Folded Drift Region Achieving Ultralow Specific ON Resistance

In order to reduce the specific ON resistance (<inline-formula> <tex-math notation="LaTeX">$\text{R}_{\mathbf {{ \mathrm{\scriptscriptstyle ON}},sp}}$ </tex-math></inline-formula>) of power device in the drift region, the folded accumulation lateral double-diffused MOSFET (FALDMOS) is manufactured and analyzed in this article. The drift region of the FALDMOS is etched to form the folded surface, which is similar to the FinFET structure. The oxide inside the trench optimizes the electric field in the drift region, so the doping concentration can be increased while maintaining the breakdown voltage (BV). In addition, the trench increases the area of the drift region covered by the extended gate electrode, which can introduce more accumulated electrons when the device is turned on. The increased doping concentration and accumulated electrons work together to substantially increase the conductivity of the drift region, thereby obtaining ultralow <inline-formula> <tex-math notation="LaTeX">$\text{R}_{ \mathrm{\scriptscriptstyle ON},sp}$ </tex-math></inline-formula>. Furthermore, the folded accumulation LDMOS with split gate (FSLDMOS) is proposed to solve the phenomenon that the BV of the FALDMOS cannot be improved by increasing the drift length with the fixed oxide thickness. The polysilicon above the drift region is etched apart to form an extended gate and a split electrode. The electric field concentration near the drain can be alleviated by adjusting the bias on the split electrode. The FALDMOS and FSLDMOS are manufactured by the 0.35-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> BCD technology and the key processes, such as trench etching and polysilicon filling, are shown. The experimental results show that <inline-formula> <tex-math notation="LaTeX">$\text{R}_{ \mathrm{\scriptscriptstyle ON},sp}$ </tex-math></inline-formula> of the FALDMOS is only 9.3 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>mm2, while that the conventional LDMOS is 36.2 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>mm2, which is reduced by 74.3% with the same BV of 36 V. Moreover, the current density of FALDMOS is five times higher than that of the conventional LDMOS in the same areas. The BV of the FSLDMOS is improved by 66% compared with FALDMOS.
An Optimized Power-Efficiency Coordinated Control Method for EVs Charging and Discharging Applications

Recently, bidirectional wireless power transmission (BD-WPT) technology has been increasingly used in electric vehicles (EVs) charging and discharging applications to realize interactive energy feedback between EVs and the power grid. Although the efficiency of BD-WPT system has been impressively improved, it still heavily relies on complex control methods. This article proposes a simple but effective control method to achieve maximum efficiency with zero reactive power. Specifically, a resonance judgment and phase synchronization network is employed to ensure the resonance performance and synchronization of the bilateral control signals, which realizes the stead autostarting of the secondary side and guarantees the disentanglement between power control processes as well. With the help of voltage ratio design and symmetric phase-shift between bilateral inverters, the optimization efficiency is well achieved. The results of the experiment and simulation match well, showing the effectiveness of the proposed method in this article.
Leader–Follower-Based Self-Triggered Consensus Control of Industrial Induction Motor Drives

Alternating current motors are critical to industry, as they drive many machines in the manufacturing and processing industries. To accomplish heavy tasks, often, a number of small motors must operate cooperatively, which means that the operation of the motors must be coordinated using, for example, consensus control. To accomplish this, the motors must communicate with each other. This communication can be periodic or event driven. As periodic communication may waste communication resources when no control update is needed, we propose a need-based self-triggered communication (STC) mechanism to achieve improved communication efficiency. We propose an STC technique for the leader–follower-based consensus control of induction motors. To study this method, we developed both centralized and distributed STC models. In the centralized approach, each motor is connected to a central unit that calculates the next communication time. When distributed STC is used, each motor calculates the next communication time solely based on information from directly connected neighboring motors, thus eliminating the possibility of a single point of failure. Extensive simulations were conducted to validate the proposed approaches. Our results show that the proposed self-triggered consensus control technique gets the same level of performance as a standard periodic control approach while utilizing fewer communication resources.