Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (FNO) can efficiently approximate solutions, they struggle with inverse problems, sensitivity estimation (du/dp), and concept drift. We address these limitations by introducing a sensitivity-based regularization strategy, called Sensitivity-Constrained Fourier Neural Operators (SC-FNO). SC-FNO achieves high accuracy in predicting solution paths and consistently outperforms standard FNO and FNO with physics-informed regularization. It improves performance in parameter inversion tasks, scales to high-dimensional parameter spaces (tested with up to 82 parameters), and reduces both data and training requirements. These gains are achieved with a modest increase in training time (30% to 130% per epoch) and generalize across various types of differential equations and neural operators. Code and selected experiments are available at: https://github.com/AMBehroozi/SC_Neural_Operators
We analyze a dual-port grid-forming (GFM) control for power systems containing ac and dc transmission, converter-interfaced generation and energy storage, and legacy generation. To operate such a system and provide standard services, state-of-the-art control architectures i) require assigning grid-following (GFL) and GFM controls to different converters, and ii) result in highly complex system dynamics. In contrast, dual-port GFM control (i) subsumes common functions of GFM and GFL controls in a simple controller, ii) can be applied to a wide range of emerging technologies independently of the network configuration, and iii) significantly reduces system complexity. In this work, we provide i) an end-to-end modeling framework that allows to model complex topologies through composition of reduced-order device models, ii) an in-depth discussion of universal dual-port GFM control for emerging power systems, and iii) end-to-end stability conditions that cover a wide range of network topologies, emerging technologies, and legacy technologies. Finally, we validate our findings in detailed case studies.
We investigate a static, asymptotically flat black hole whose central region approaches a de Sitter vacuum. The geometry is controlled by the ADM mass $M_0$ and a core scale $R$, and is generated by an exponentially decaying anisotropic source. After clarifying the stress tensor and the horizon structure, we study scalar-field perturbations, greybody transmission, Hawking emission and the optical image produced by an optically thin infalling flow. The horizon analysis shows that two horizons exist below the critical value $R/M_0\simeq0.7768$, where they merge into an extremal configuration. Increasing the core scale lowers the peak of the scalar effective potential and shifts the quasinormal spectrum away from the Schwarzschild value, with the largest fractional deviations occurring for low multipoles and higher overtones within the WKB domain of validity. The greybody bound indicates stronger filtering as the core becomes more extended, while the QNM--greybody correspondence gives a complementary estimate of the transmission probability in the eikonal regime. The Hawking temperature decreases as the extremal configuration is approached, suppressing the emission rate and shifting i
Provenance information are essential for the traceability of scientific studies or experiments and thus crucial for ensuring the credibility and reproducibility of research findings. This paper discusses a comprehensive provenance framework combining the two types 1. workflow provenance, and 2. data provenance as well as their dimensions and granularity, which enables the answering of W7+1 provenance questions. We demonstrate the applicability by employing a biomedical research use case, that can be easily transferred into other scientific fields. An integration of these concepts into a unified framework enables credibility and reproducibility of the research findings.
3GPP Integrated Access and Backhaul (IAB) allows operators to deploy outdoor mm-wave access networks in a cost-efficient manner, by reusing the same spectrum in access and backhaul. In IAB networks the performance bottleneck is the wireless backhaul segment, where efficient forwarding strategies are needed to effectively use the available capacity. In addition, the performance of the mm-wave IAB backhaul segment is contingent on the availability of line of sight (LoS) conditions in the selected deployment sites. To mitigate LoS dependence, in this paper, we propose to complement the mm-wave backhaul segment of IAB networks with additional Sub6 backhaul links, which contribute to the capacity and robustness of the backhaul network. We refer to IAB networks combining Sub6 and mm-wave links in the backhaul as Sub6 enhanced IAB networks. In this context, the main contribution of this paper is PHaul, a forwarding engine for Sub6 enhanced IAB networks that accomodates different traffic engineering criteria, and combines an offline path selection heuristic with an online Deep Reinforcement Learning (DRL) agent based on Proximal Policy Optimization (PPO). By leveraging a network digital tw
A generic and universal layer engineering strategy for van der Waals (vW) materials, scalable and compatible with the current semiconductor technology is of paramout importance in realizing all-two-dimensional logic circuits and move beyond the silicon scaling limit. In this letter, we demonstrate a scalable and highly controllable microwave plasma based layer engineering strategy for MoS2 and other vW materials. Using this technique we etch MoS2 flakes layer-by-layer starting from arbitrary thickness and area down to the mono- or the few-layer limit. From Raman spectroscopy, atomic force microscopy, photoluminescence spectroscopy, scanning electron microscopy and transmission electron microscopy, we confirm that the structural and morphological properties of the material have not been compromised. The process preserves the pre-etch layer topography and yields a smooth and pristine-like surface. We explore the electrical properties utilising a field effect transistor geometry and find that the mobility values of our samples are comparable to those of the pristine ones. The layer removal does not involve any reactive gasses or chemical reactions and relies on breaking the weak inter
Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this research, we construct a taxonomy of data work artifacts that captures AutoML and human processes. We present a rigorous methodology for its creation and discuss its transferability to the visual design process. We operationalize the taxonomy through the development of AutoMLTrace, a visual interactive sketch showing both the context and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage scenario with an enterprise software development team. Collectively, our research process and findings explore challenges and fruitful avenues for developing data visualization tools that interrogate the sociotechnical relationships in automated data work.
Discotic colloids give rise to a paradigmatic family of liquid crystals with sound applications in Materials Science. In this paper, Monte Carlo simulations are employed to characterize the low-temperature liquid crystal phase diagram and the vapour-liquid coexistence of discotic colloids interacting via a Kihara potential. Discoidal particles with thickness-diameter aspect ratios $L^*\equiv L/D$=\,0.5, 0.3, 0.2 and 0.1 are considered. For the less anisotropic particles ($L^*$$\ge$0.2), coexistence of a vapour phase with the isotropic fluid and with the columnar liquid crystal phase is observed. As the particle anisotropy increases, the vapour-liquid coexistence shifts to lower temperatures and its density range diminishes, eventually merging with coexistences involving the liquid crystal phases. The $L^*=$\,0.1 fluid displays a rich sequence of mesophases, including a nematic phase and a novel lamellar phase in which particles arrange in layers perpendicular to the nematic director.
Alignment-free methods in phylogenetic tree construction have major benefits in computational efficiency over alignment-based methods, but most sacrifice sequence information to pairwise distances, losing the statistical power of maximum likelihood (ML) inference. We describe ML-MAWS, an algorithm that fills this gap by encoding Minimal Absent Words (MAWs) as a binary presence/absence character matrix and estimating using an ML tree under the Lewis Mkv model using ascertainment bias correction. MAWs are obtained in linear time through the traversal of a suffix automaton. Three new elements contribute to the phylogenetic signal: strand-aware filtering combines forward and reverse complement MAW sets to eliminate compositional artifacts; entropy-based multi-length selection uses Shannon entropy maximization to select the most informative lengths of MAWs; and parsimony-informative character capping only retains the most discriminative columns. We tested ML-MAWS on 14 benchmark datasets of bacterial, mitochondrial, viral, and simulated genomes with normalized Robinson Foulds distances and matching split distances, against published reference trees. The results show that the coarse bina
We propose a robust adaptive online synchronization method for leader-follower networks of nonlinear heterogeneous agents with system uncertainties and input magnitude saturation. Synchronization is achieved using a Distributed input Magnitude Saturation Adaptive Control with Reinforcement Learning (DMSAC-RL), which improves the empirical performance of policies trained on off-the-shelf models using Reinforcement Learning (RL) strategies. The leader observes the performance of a reference model, and followers observe the states and actions of the agents they are connected to, but not the reference model. The leader and followers may differ from the reference model in which the RL control policy was trained. DMSAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of augmented input to solve the distributed control problem, including input-matched uncertainty parameters. We show that the synchronization error of the heterogeneous network is Uniformly Ultimately Bounded (UUB). Numerical analysis of a network of Multiple Input Multiple Output (MIMO) systems supports our theoretical findings.
With the rapid development of civil aviation and the significant improvement of people's living standards, taking an air plane has become a common and efficient way of travel. However, due to the flight characteris-tics of the aircraft and the sophistication of the fuselage structure, flight de-lays and flight accidents occur from time to time. In addition, the life risk factor brought by aircraft after an accident is also the highest among all means of transportation. In this work, a model based on back-propagation neural network was used to predict flight accidents. By collecting historical flight data, including a variety of factors such as meteorological conditions, aircraft technical condition, and pilot experience, we trained a backpropaga-tion neural network model to identify potential accident risks. In the model design, a multi-layer perceptron structure is used to optimize the network performance by adjusting the number of hidden layer nodes and the learning rate. Experimental analysis shows that the model can effectively predict flight accidents with high accuracy and reliability.
Multiplicity and clustering of young pre-main sequence stars appear as critical clues to constrain the star formation process. Taurus is the archetypical example of the most quiescent star forming regions that may still retain primeval signatures of star formation. This work identifies local overdense stellar structures at the 99.8\% confidence level above random expectation using the DBSCAN algorithm, and setting its free parameters based on the one-point correlation function and the k-nearest neighbor statistics. Nearly half of the entire stellar population in Taurus is found to be concentrated in 20 dense, tiny and prolate regions called NESTs (for Nested Elementary STructures). They are regularly spaced ($\approx 2$ pc) and mainly oriented along the gas filaments axes. Each NEST contains between 4 and 23 stars. Inside NESTs, the surface density of stars may be as high as 2500 pc$^{-2}$. Nearly half (11) of these NESTs contain about 75\% of the class 0/I objects. The balance between Class I, II, and, III fraction within the NESTs suggests that they may be ordered as an evolutionary temporal scheme. We have inferred that only 20\% of stars in Taurus do not belong to any kind of s
In this paper, a differential MOEMS accelerometer based on the Fabry-Perot (FP) micro-cavities is presented. The optical system of the device consists of two FP cavities and the mechanical system is composed of a proof mass that is suspended by four springs. The applied acceleration tends to move the PM from its resting position. This mechanical displacement can be measured by the FP interferometer formed between the proof mass cross-section and the optical fiber end face. The proposed sensor is fabricated on a silicon on insulator (SOI) wafer using the bulk micromachining method. The results of the sensor characterization show that the accelerometer has a linear response in the range of 1g. Also, the optical sensitivity and resolution of the sensor in the static characterization are 6.52 nm/g and 153ug. The sensor sensitivity in the power measurement is 49.6 mV/g and its resonant is at 1372 Hz. Using the differential measurement method increases the sensitivity of the accelerometer. Based on experimental data, the sensor sensitivity is two times as high as that of a similar MOEMS accelerometer with one FP cavity.
Due to the mismatch between the source and target domains, how to better utilize the biased word information to improve the performance of the automatic speech recognition model in the target domain becomes a hot research topic. Previous approaches either decode with a fixed external language model or introduce a sizeable biasing module, which leads to poor adaptability and slow inference. In this work, we propose CB-Conformer to improve biased word recognition by introducing the Contextual Biasing Module and the Self-Adaptive Language Model to vanilla Conformer. The Contextual Biasing Module combines audio fragments and contextual information, with only 0.2% model parameters of the original Conformer. The Self-Adaptive Language Model modifies the internal weights of biased words based on their recall and precision, resulting in a greater focus on biased words and more successful integration with the automatic speech recognition model than the standard fixed language model. In addition, we construct and release an open-source Mandarin biased-word dataset based on WenetSpeech. Experiments indicate that our proposed method brings a 15.34% character error rate reduction, a 14.13% bias
Birefringent metasurfaces are two-dimensional structures capable of independently controlling the amplitude, phase and polarization of orthogonally polarized incident waves. In this work, we propose a in-depth discussion on the mathematical synthesis of such metasurfaces. We compare two methods, one that is rigorous and based on the exact electromagnetic fields involved in the transformation and one that is based on approximate reflection and transmission coefficients. We next validate the synthesis technique in metasurfaces performing the operations of half- and quarter-wave plates, polarization beam splitting and orbital angular momentum multiplexing.
The transverse momentum spectra at RHIC and LHC for A+A and p+p collisions are studied with Tsallis distributions in different approaches i.e. with and without radial flow. The information on the freeze-out surface in terms of freeze-out volume, temperature, chemical potential and radial flow velocities for different particle species are obtained. These parameters are found to show a systematic behavior with mass dependence. It is observed that the heavier particles freeze-out early as compared to lighter particles and freeze-out surfaces are different for different particles, which is a direct signature of mass dependent differential freeze-out. Further, we observe that the radial flow velocity decreases with increasing mass. This confirms the mass ordering behavior in collectivity observed in heavy-ion collisions. It is also observed that the systems created in peripheral heavy-ion collisions and in proton-proton collisions are of similar thermodynamic nature.
Human-robot interaction is emerging as an important paradigm for integrating persons with disabilities into the workplace. While these systems can enable individuals to work, their design is mostly personalized, hindering widespread use beyond the individual user. The universal design paradigm is a central pillar of inclusive design, describing usability of systems by all. To incorporate universal design into process design for human-robot workplaces expert knowledge is required that is often not available. To simplify process design of human-robot workplaces, we propose a persona-based design approach. First, typical impairments prevalent in the workforce or particularly relevant for the processes are abstracted into personas with disabilities. The work process is subdivided into sequential actions. For each action and persona, strategies are developed to reach the action goal by a design thinking approach. The resulting actions are ordered by level of robot assistance, i.e. robot involvement, and implemented in a behavior tree. Therefore, the macro-behavior of the workplace may adapt to individual personas online. We demonstrate the method in a collaborative box folding process w
In multiplayer games with sequential decision-making, self-interested players form dynamic coalitions to achieve most-preferred temporal goals beyond their individual capabilities. We introduce a novel procedure to synthesize strategies that jointly determine which coalitions should form and the actions coalition members should choose to satisfy their preferences in a subclass of deterministic multiplayer games on graphs. In these games, a leader decides the coalition during each round and the players not in the coalition follow their admissible strategies. Our contributions are threefold. First, we extend the concept of admissibility to games on graphs with preferences and characterize it using maximal sure winning, a concept originally defined for adversarial two-player games with preferences. Second, we define a value function that assigns a vector to each state, identifying which player has a maximal sure winning strategy for certain subset of objectives. Finally, we present a polynomial-time algorithm to synthesize admissible strategies for all players based on this value function and prove their existence in all games within the chosen subclass. We illustrate the benefits of
Parkinson's disease (PD) is a progressive neurodegenerative disease, and it is caused by the loss of dopaminergic neurons in the basal ganglia (BG). Currently, there is no definite cure for PD, and available treatments mainly aim to alleviate its symptoms. Due to impaired neurotransmitter-based information transmission in PD, molecular communication-based approaches can be employed as potential solutions to address this issue. Molecular Communications (MC) is a bio-inspired communication method utilizing molecules for carrying information. This mode of communication stands out for developing bio-compatible nanomachines for diagnosing and treating, particularly in addressing neurodegenerative diseases like PD, due to its compatibility with biological systems. This study presents a novel treatment method that introduces an Intelligent Dopamine Rate Modulator (IDRM), which is located in the synaptic gap between the substantia nigra pars compacta (SNc) and striatum to compensate for insufficiency dopamine release in BG caused by PD. For storing dopamine in the IDRM, dopamine compound (DAC) is swallowed and crossed through the digestive system, blood circulatory system, blood-brain barr
If coupled \emph{feebly} to the Standard Model bath, a dark matter can evade the severe constraints from the direct search experiments. At the same time, such interactions help produce dark matter via the freeze-in mechanism. The freeze-in scenario becomes more interesting if one also includes the thermal masses of the different particles involved in the dark matter phenomenology. Incorporating such thermal corrections opens up the possibility of dark matter production via forbidden channels that remain kinematically disallowed in the standard freeze-in setup. Motivated by this, we investigate such freeze-in production of the dark matter in a minimally extended $U(1)_{L_μ-L_τ}$ framework that remains consistent with the recent muon $(g-2)$ data. Here, the role of the dark matter is played by the scalar with a non-trivial charge under the additional symmetry $U(1)_{L_μ-L_τ}$. This scalar dark matter obtains a thermally corrected mass at high temperatures for a not-so-small self-coupling. We show that the thermal correction to the dark matter mass plays a significant role in the dark matter phenomenology.