Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for GeV gamma-ray emission from SPT-SZ selected galaxy clusters with 15 years of Fermi-LAT data

Galaxy clusters could produce gamma-rays from inverse Compton scattering of cosmic ray electrons or hadronic interactions of cosmic ray protons with the intracluster medium. It is still an open question on whether gamma-ray emission (> GeV energies) has been detected from galaxy clusters. We carry out a systematic search for gamma-ray mission based on 300 galaxy clusters selected from the 2500 deg2 SPT-SZ survey after sorting them in descending order of M 500/z 2, using about 15 years of Fermi-LAT data in the energy range between 1–300 GeV. We were able to detect gamma-ray emission with significance of about 6. 1σ from one cluster, viz. SPT-CL J2012-5649. The estimated photon energy flux from this cluster is approximately equal to 1.3 × 10-6 MeV cm-2 s-1. The gamma-ray signal is observed between 1–10 GeV with the best-fit spectral index equal to -3.61 ± 0.33. However, since there are six radio galaxies spatially coincident with SPT-CL J2012-5649 within the Fermi-LAT PSF, we cannot rule out the possibility this signal could be caused by some of these radio galaxies. Six other SPT-SZ clusters show evidence for gamma-ray emission with significance between 3–5σ. None of the remaining clusters show statistically significant evidence for gamma-ray emission.
Exploring the Horizon: A Comprehensive Survey of Rowhammer

Rowhammer poses a significant security challenge for modern computers, specifically affecting Dynamic Random Access Memory(DRAM). Given society's growing reliance on computer systems, ensuring the reliability of hardware is of utmost importance. This paper provides a comprehensive survey of Rowhammer, examining the literature from various angles. We categorise studies on Rowhammer into attacks, defences, and intriguing work, exploring each category in detail. Furthermore, we classify papers within each category into distinct yet overlapping classes and present an overview of the papers in each class.
Textiverse: A Scalable Visual Analytics System for Exploring Geotagged and Timestamped Text Corpora

We propose Textiverse, a big data approach for mining geotagged timestamped textual data on a map, such as for Twitter feeds, crime reports, or restaurant reviews. We use a scalable data management pipeline that extracts keyphrases from online databases in parallel. We speed up this time-consuming step so that it outpaces the content creation rate of popular social media. The result is presented in a web-based interface that integrates with Google Maps to visualize textual content of massive scale. The visual design is based on aggregating spatial regions into discrete sites and rendering each such site as a circular tag cloud. To demonstrate the intended use of our technique, we first show how it can be used to characterize the U.S.\ National Science Foundation funding status based on all 489,151 awards. We then apply the same technique on visually representing a more spatially scattered and linguistically informal dataset: 1.2 million Twitter posts about the Android mobile operating system.
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification

Standard approaches for uncertainty quantification in deep learning and physics-informed learning have persistent limitations. Indicatively, strong assumptions regarding the data likelihood are required, the performance highly depends on the selection of priors, and the posterior can be sampled only approximately, which leads to poor approximations because of the associated computational cost. This paper introduces and studies confidence interval (CI) estimation for deterministic partial differential equations as a novel problem. That is, to propagate confidence, in the form of CIs, from data locations to the entire domain with probabilistic guarantees. We propose a method, termed Physics-Informed Confidence Propagation (PICProp), based on bi-level optimization to compute a valid CI without making heavy assumptions. We provide a theorem regarding the validity of our method, and computational experiments, where the focus is on physics-informed learning.
Interaction-aware Traffic Prediction and Scenario-based Model Predictive Control for Autonomous Vehicles on Highways

This paper addresses the problem of traffic prediction and control of autonomous vehicles on highways. A modified Interacting Multiple Model Kalman filter algorithm is applied to predict the motion behavior of the traffic participants by considering their interactions. A scenario generation component is used to produce plausible scenarios of the vehicles based on the predicted information. A novel integrated decision-making and control system is proposed by applying a Scenario-based Model Predictive Control approach. The designed controller considers safety, driving comfort, and traffic rules. The recursive feasibility of the controller is guaranteed under the inclusion of the `worst case' as an additional scenario to obtain safe inputs. Finally, the proposed scheme is evaluated using the HighD dataset. Simulation results indicate that the vehicle performs safe maneuvers in different traffic situations under the designed control framework.
A new mixed finite element method for arbitrary element pair for a quasi-static nonlinear permeability thermo-poroelasticity model

In this paper, we develop a multiphysics finite element method for solving the quasi-static thermo-poroelasticity model with nonlinear permeability. The model involves multiple physical processes such as deformation, pressure, diffusion and heat transfer. To reveal the multi-physical processes of deformation, diffusion and heat transfer, we reformulate the original model into a fluid coupled problem that is general Stokes equation coupled with two reaction-diffusion equations. Then, we prove the existence and uniqueness of weak solution for the original problem by the $B$-operator technique and by sequence approximation for the reformulated problem. As for the reformulated problem we propose a fully discrete finite element method which can use arbitrary finite element pairs to solve the displacement $\bu$ pressure $\tau $ and variable $\varpi,\varsigma$, and the backward Euler method for time discretization. Finally, we give the stability analysis of the above proposed method, also we prove that the fully discrete multiphysics finite element method has an optimal convergence order. Numerical experiments show that the proposed method can achieve good results under different finite element pairs and are consistent with the theoretical analysis.
Higher order Lie bracket approximation and averaging of control-affine systems with application to extremum seeking

This paper provides a rigorous derivation for what is known in the literature as the Lie bracket approximation of control-affine systems. In fact, by using Chronological calculus, we show that said Lie bracket approximations are themselves higher-order averaging terms. Hence, this paper bridges both averaging and approximation theories of control-affine systems. In particular, the Lie bracket approximation of order ($n$) turns out to be a higher order averaging of order ($n+1$). The derivation and formulation provided in this paper can be directly reduced to the first and second-order Lie bracket approximations available in the literature. However, we do not need to make many of the unproven assumptions provided in the literature and show that they are in fact natural corollaries from our work. In particular, we use our results to show that important and useful information about control-affine extremum seeking systems can be obtained and used for performance improvement, including faster convergence of very simple structures. We provide multiple numerical simulations to demonstrate both the conceptual elements of this work as well as the significance of our results on extremum seeking with comparison to the literature.
Novel Ion Trap Junction Design for Transporting Qubits in a 2D Array

Junctions are fundamental elements that support qubit locomotion in two-dimensional ion trap arrays and enhance connectivity in emerging trapped-ion quantum computers. In surface ion traps they have typically been implemented by shaping radio frequency (RF) electrodes in a single plane to minimize the disturbance to the pseudopotential. However, this method introduces issues related to RF lead routing that can increase power dissipation and the likelihood of voltage breakdown. Here, we propose and simulate a novel two-layer junction design incorporating two perpendicularly rotoreflected linear ion traps. The traps are vertically separated, and create a trapping potential between their respective planes. The orthogonal orientation of the RF electrodes of each trap relative to the other provides perpendicular axes of confinement that can be used to realize transport in two dimensions. While this design introduces manufacturing and operating challenges, as now two separate structures have to be precisely positioned relative to each other in the vertical direction and optical access from the top is obscured, it obviates the need to route RF leads below the top surface of the trap and eliminates the pseudopotential bumps that occur in typical junctions. In this paper the stability of idealized ion transfer in the new configuration is demonstrated, both by solving the Mathieu equation analytically to identify the stable regions and by numerically modeling ion dynamics. Our novel junction layout enhances the flexibility of microfabricated ion trap control to enable large-scale trapped-ion quantum computing.
Fragility of the magnetic order in the prototypical altermagnet RuO$_2$

Altermagnetism is a topic that has lately been gaining attention and the RuO$_2$ compound is among one of the most studied altermagnetic candidates. However, the survey of available literature on RuO$_2$ properties suggests that there is no consensus about the magnetism of this material. By performing density functional theory calculations, we show that the electronic properties of stoichiometric RuO$_2$ are described in terms of a smaller Hubbard $U$ within DFT+$U$ than the value required to have magnetism. We further argue that Ru vacancies can actually aid the formation of a magnetic state in RuO$_2$. This in turn suggests that a characterization of the amount of Ru vacancies in experimental samples might help the resolution of the controversy between the different experimental results.
Ecological and Genetic Systems Underlying Sustainable Horticulture

III. GENETIC SYSTEMS A. Legume Metabolism and Functional Molecules B. Other Nitrogen:Carbon Interactive Metabolites C. Legume.Arbusdular Mycorrhizal Fungus Interactions D. Molecular Signature of Hairy Vetch—Grown Tomato E. A Working Model Explaining Hairy Vetch—Tomato Interactions F. Genotype x Environment and N:C Interactions IV. AN INTEGRATED APPROACH TO SUSTAINABLE HORTICULTURE LITERATURE CITED
Forward propagation of the Zagros Simply Folded Belt constrained from magnetostratigraphy of growth strata

The temporal evolution of the Zagros Simply Folded Belt is constrained by a magnetostratigraphic sequence containing a progressive unconformity on the southern limb of the Kuh‐e Ghol Ghol anticline, in the Central Fars. The investigated ~1400 m thick sequence exposes a regressive megacycle containing, from bottom to top, open and shallow marine marls and sandy limestones, fine‐ to coarse‐grained fluvial deposits and alluvial conglomerates. Correlating the magnetostratigraphic section with the geomagnetic polarity time scale constrains the transition from marine to fluvial sediment deposition at ~6 Ma. This transition was accompanied by a change in the accumulation rate from ~15 cm/ka to ~40 cm/ka, as measured on lithified sediments. Alluvial river deposits first occurred at ~3.2 Ma. The Kuh‐e Ghol Ghol anticline began to grow at ~3.8 Ma, witnessing fastest limb rotation rates of 40°/Ma at ~3.3 Ma. Reporting magnetostratigraphic sections and ages of growth strata on a map of NE Fars reveal an ~1 cm/a, southwestward migration rate of the deformation front during the middle and late Miocene.
How sediment promotes narrow rifting: Application to the Gulf of California

If the gravitational stress changes related to crustal thinning are large enough to affect the mode of extension, then sedimentation should also affect that mode. We propose that the weight of sediments reduces the difference in crustal buoyancy forces caused by local crustal thinning, allowing a rift to extend more easily in a narrow rift mode. We examine the effect of deposition of large amounts of nonlocally derived sediment on extensional style using two‐dimensional, regional‐scale numerical experiments of extending thick continental crust with varied initial thermal conditions, geometries, rheological parameters, and sedimentation properties. Depending on initial model conditions, the onset of rifting in thick continental crust occurs in the narrow rifting, wide rifting, or core complex mode. With continued extension, all cases eventually transition to a narrow rifting mode. For a system in wide rift mode, moderate to fast sedimentation shortens the time needed to transition to a narrow rift. In the Gulf of California, changes in extensional style correlate with sediment thickness, with an earlier transition to narrow rifting in the north versus the south. We compare our model results to the extensional history of the Gulf of California, where sediments may have caused the northern and north central domain of the gulf (Delfin‐Tiburon and Guaymas segments) to transition to narrow rifting before the south central domains (Alarcon segment).
THE ROLE OF CLIMATE IN THE DISTRIBUTION OF VEGETATION

ABSTRACT Vegetation develops in response to many different stimuli including climatic, edaphic, and cultural conditions. The degree to which the distribution of vegetation can be explained on the basis of climatic conditions depends in part on the proper selection of active climatic factors. Temperature and precipitation by themselves are poor descriptors of climate. Potential evapotranspiration, water surplus, and water deficit (combined in a moisture index) are found to be more useful. An analysis of the vegetation distribution in conterminous United States shows that the Thornthwaite moisture index and annual potential evapotranspiration are not only able to differentiate among the mid-latitude grassland, forest, and desert biochores but also to provide climatic limits for formation classes within each of the biochores. For example, it is possible to identify on the basis of numerical data an oak-hickory climate as distinct from the birch-maple, and a bunch grass climate distinct from the sagebrush. Th.
AFLNETGO:A Directed Fuzzer for Stateful Network Protocol Implementation

Directed Gray Box Fuzzers (DGF) are widely used for patch testing, bug reproduction, and special bug detection. Despite these successes, not all software can benefit from DGF, such as stateful network protocol implementations. In a stateful network protocol implementation, the target is unlocked only in certain states. Running in a state where the target cannot be unlocked wastes a lot of resources. In this article, we propose AFLNETGO, a directed greybox fuzzing scheme for stateful network protocol implementations. AFLNETGO automatically and dynamically identifies the state of the network protocol implementations and the target state at run time. AFLNETGO assigns energy to a state in a state transition graph based on the probability of that state transitioning to a target state, allowing fuzzer processing to focus more on the states of possible execution targets. For comparison, we have implemented the directed method of AFLGo on AFLNET and called it AFLGo plus. We fuzzed random targets in two popular stateful network protocol implementations: LIVE555 and Dcmqrscp. The evaluation results show that AFLNETGO has better directed performance than AFLGo plus for targets.
The Influence of Basin Aridity on the Efficiency of Runoff Predicting Models

The connection between runoff predicting models efficiency and basin aridity is investigated by using data from five representative basins in the mountainous central and northern Greece. Two substantially different models were selected, a monthly water balance model and an empirically fitted rainfall runoff model of the Kalman filter type. Both models were calibrated and validated in each basin and their overall efficiency was evaluated for each basin. The five basins were classified by using a humidity index and were found to range from humid to semi-arid. The efficiency of each model was then tested in terms of decreasing aridity and the two models exhibited a clearly contrasting behaviour; a decreasing efficiency of the Kalman filter model was observed, whereas the water balance model efficiency showed to increase with decreasing aridity. This behaviour is explained by considering the autoregressive characteristics of the corresponding five runoff time series, which are imparted in them by the filtering action of the basin. This factor becomes more important the more arid the basin is. Conclusively, the model of the Kalman filter type, designed to take account of the runoff autoregression, is shown to perform better with increasing aridity while in contrast, the water balance type of model where runoff memory is not accounted for, performs better the more humid the basin is, hence the runoff autoregressive characteristics become less significant.
Energy-Efficient Decoding of Spatially Coupled Low-Density Parity-Check Codes using Adaptive Window Sizes

Wireless communication systems require high error correction capability, increasing data rates, lower round-trip latency, and efficient silicon implementation. Low-density parity-check (LDPC) codes have gained popularity as a class of forward error correction codes due to their ability to approach channel capacity for large block sizes. However, high-throughput decoding implementation is limited to small block sizes and few iterations. Spatially coupled LDPC (SC-LDPC) codes can outperform standard LDPC codes from an error correction perspective and exhibit locality in the Tanner graph by coupling small LDPC block codes (LDPC-BCs). This enables a sliding window decoding (SWD) decoding and, thus, overcomes the limitation of small block sizes. In this paper, we present a new SWD decoding algorithm and its decoder implementation in a 22 nm technology. The new decoder is based on an adaptive window size yielding power savings up to a factor of 4 in the high SNR range compared to the state-of-the-art.
Mathematical Modeling of the Control System of a Municipal Electric Machine

The article presents a mathematical description of the phase control of the traction valve electric drive of a municipal machine. Mathematical relations are developed, mechanical and regulating characteristics of the valve electric drive when adjusted for maximum electromagnetic torque and efficiency are constructed. Because there are requirements for the implementation of a wide range of speed and torque control, as well as requirements for high dynamic and energy characteristics for a traction valve electric drive, then the typical field-oriented vector control is not optimal for solving such problems But, in the traction valve electric drive, mostly a permanent magnet motor is used, where the excitation flux is already formed, and it is only necessary to control the stator field flux vector, its module and angular position relative to the rotor flux vector, which are quite easy to measure or to observe and regulate. Such regulation is the basis of the phase control of the valve electric drive. The regulating ratios are based on the mathematical model of the valve motor and allow you to adjust the operating mode of the traction valve electric drive to the maximum speed, maximum efficiency, constant torque or power, to control the attenuation of the field. The article analyzes the mathematical relations of mechanical and energy characteristics, analyzes the regulating properties, and also solves optimization problems to improve the efficiency of control of the traction valve electric drive of a municipal machine.
Identification and representation of water pathways from production areas to urban catchment outlets: a case study in France

Hydrographic networks in urban areas combine several sources and water pathways, including buried channels and, more recently, source control practices. Moreover, pervious areas may be either directly or indirectly connected to drainage facilities and pipe water seepage. All these pathways have significant operational consequences since they challenge design principles, sizing guidelines and operating rules for both collection and treatment facilities, which might even undermine the efforts to improve the quality of receiving waters. The project proposed herein aims to generate a detailed description of the water pathways on an urban catchment in order to assess the impact of urban planning strategies and provide guidelines for sewer system evaluation and rehabilitation. The work presented in this paper has taken place within the Pin Sec catchment (Nantes, France); it consists of collecting and analyzing groundwater, wastewater and rainwater in the catchment and then applying the isotopic and chemical results to trace the mixing that occurs in the pipes. Results reveal that isotopic tracing is indeed relevant on this catchment. The isotopic signatures of drinking water and groundwater vary sufficiently during spring, up to 1.5‰ for δ 18 O and up to 12‰ for δ 2 H in May. The ratios of defective connections and water seepage may be deduced from this output. Chemicals also support the findings of the isotopic tracing results.
Light Weight ResNet for Detection of Wheat Yellow Rust over Mobile Captured Images from Wheat Fields

Plant diseases act as a major threat to small-scale farmers as they lead to major destruction in the overall food supply. In this, yellow rust disease of wheat is a major cause of concern in wheat producing countries. To provide effective measures for detection and avoidance of the destruction of the yellow rust an early identification is required. Plant disease identification from the images is an interesting research in agriculture and computer fields. The data was collected using the mobile phones and GPS meter from wheat fields of humid subtropical parts of Haryana (India). A total of 10000 images were used in the machine learning program out of which 5000 images from 2022 dataset and 5000 images from 2023 dataset. The dataset consist two categories of healthy and yellow rust images. The ResNet, is a popular deep learning method, was applied to detect the yellow rust disease of wheat. The result of 2022 dataset shows the training accuracy as 98.80% and the test accuracy as 93.19% and the result of 2023 dataset shows the training accuracy as 99.69% and the test accuracy as 99.10%.
Control Performance Characterization and Monitoring Scheme for Power Converters in Weak Grids

In weak grids, control performance of power converters is negatively affected by the significantly varied grid impedance, which can easily lead to degradation of anti-harmonic performance, resonances or even system instability. A rapid control performance monitor is therefore proposed in this work to acquire power converters’ actual performance indexes, i.e., the crossover frequency and phase margin. With the measured real-time information, important functions like health management, control parameters design and adaptive control of power systems can be conveniently realized. The monitor is implemented by perturbating the control loop of the power converter under test with a frequency-autotuned small signal, showing satisfying dynamic response and anti-noise performance. Thanks to the 100% digital implementation, the proposed performance monitor requires no extra hardware and no further cost. The detailed design of the monitor, based on small signal models derivation employing an envelope modeling method, is presented in this article. Extensive experiments were carried out to verify the efficacy of the proposed monitor.