Miracle-dz/UniFAR-FTUs huggingface dataset:
Anisotropic magnetoresistance due to magnetization-dependent spin-orbit interactions

One of recent surprising discoveries is the unusual anisotropic magnetoresistance (UAMR) that depends on two magnetization components perpendicular to the current differently, in contrast to the conventional anisotropic magnetoresistance (AMR) that predicts no change in resistance when the magnetization varies in the plane perpendicular to the current. Using density functional theory and Boltzmann transport equation calculations for bcc Fe, hcp Co, and bcc FeCo alloys, we show that UAMR can be accounted by the magnetization-dependent spin-orbit interactions (SOI): Magnetization-dependent SOI modifies electron energy bands that, in turn, changes resistance. A phenomenological model reveals the intrinsic connection between SOI and order-parameters. Such a mechanism is confirmed by the strong biaxial stain effect on UAMR. Our findings provide an efficient way of searching and optimizing materials with large UAMR, important in the design of high-performance spintronic devices.
On the Degrees of Freedom of RIS-Aided Holographic MIMO Systems

In this paper, we study surface-based communication systems based on different levels of channel state information for system optimization. We analyze the system performance in terms of rate and degrees of freedom (DoF). We show that the deployment of a reconfigurable intelligent surface (RIS) results in increasing the number of DoF, by extending the near-field region. Over Rician fading channels, we show that an RIS can be efficiently optimized only based on the positions of the transmitting and receiving surfaces, while providing good performance if the Rician fading factor is not too small.
SHITARA: Sending Haptic Induced Touchable Alarm by Ring-shaped Air vortex

Social interaction begins with the other person's attention, but it is difficult for a d/Deaf or hard-of-hearing (DHH) person to notice the initial conversation cues. Wearable or visual devices have been proposed previously. However, these devices are cumbersome to wear or must stay within the DHH person's vision. In this study, we have proposed SHITARA, a novel accessibility method with air vortex rings that provides a non-contact haptic cue for a DHH person. We have developed a proof-of-concept device and determined the air vortex ring's accuracy, noticeability and comfortability when it hits a DHH's hair. Though strength, accuracy, and noticeability of air vortex rings decrease as the distance between the air vortex ring generator and the user increases, we have demonstrated that the air vortex ring is noticeable up to 2.5 meters away. Moreover, the optimum strength is found for each distance from a DHH.
An Automata-Based Framework for Verification and Bug Hunting in Quantum Circuits

We introduce a new paradigm for analysing and finding bugs in quantum circuits. In our approach, the problem is given by a triple {P} C {Q} and the question is whether, given a set P of quantum states on the input of a circuit C, the set of quantum states on the output is equal to (or included in) a set Q. While this is not suitable to specify, e.g., functional correctness of a quantum circuit, it is sufficient to detect many bugs in quantum circuits. We propose a technique based on tree automata to compactly represent sets of quantum states and develop transformers to implement the semantics of quantum gates over this representation. Our technique computes with an algebraic representation of quantum states, avoiding the inaccuracy of working with floating-point numbers. We implemented the proposed approach in a prototype tool and evaluated its performance against various benchmarks from the literature. The evaluation shows that our approach is quite scalable, e.g., we managed to verify a large circuit with 40 qubits and 141,527 gates, or catch bugs injected into a circuit with 320 qubits and 1,758 gates, where all tools we compared with failed. In addition, our work establishes a connection between quantum program verification and automata, opening new possibilities to exploit the richness of automata theory and automata-based verification in the world of quantum computing.
Resilient Containment Control of Heterogeneous Multi-Agent Systems Against Unbounded Sensor and Actuator Attacks

Accurate local state measurement is important to ensure the reliable operation of distributed multi-agent systems (MAS). Existing fault-tolerant control strategies generally assume the sensor faults to be bounded and uncorrelated. In this paper, we study the ramifications of allowing the sensor attack injections to be unbounded and correlated. These malicious sensor attacks may bypass the conventional attack-detection methods and compromise the cooperative performance and even stability of the distributed networked MAS. Moreover, the attackers may gain access to the actuation computing channels and manipulate the control input commands. To this end, we consider the resilient containment control problem of general linear heterogeneous MAS in the face of correlated and unbounded sensor attacks, as well as general unbounded actuator attacks. We propose an attack-resilient control framework to guarantee the uniform ultimate boundedness of the closed-loop dynamical systems and preserve the bounded containment performance. Compared with existing literature addressing bounded faults and/or disturbances that are unintentionally caused in the sensor and actuator channels, the proposed control protocols are resilient against unknown unbounded attack signals simultaneously injected into sensor and actuator channels, and hence are more practical in the real-world security applications. A numerical example illustrates the efficacy of the proposed result, by highlighting the resilience improvement over the conventional cooperative control method.
Flow Choking by Drag and Combustion in Supersonic Engine Testing

condition was calculated by incorporating results of a chemical equilibrium numerical code. The fuel rates causing the flow choking were successfully reproduced and matched with the limit of fuel rates observed in our tests. Inlet unstart of the engine caused the loss of the ejector-pumping effect in the diffuser system, which triggered flow choking. Choking was also promoted by the drag of the gas sampling rakes. The choking in diffuser flow and the engine unstart may be coupled, resulting in hysteresis in the diffuser breakdown, which was also experienced in our tests. As the rocket-based, combined-cycle engine is scheduled to be tested under the Mach 4 condition in 2007, the safety of the experiment is now a primary issue. The current analysis showed that the new engine would easily cause choking of diffuser because of the larger propellant supply rates and the relatively low specific impulse. Reasonable operation of the wind tunnel to control the flow choking in future tests was examined.
Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments

In 5G non-standalone mode, an intelligent traffic steering mechanism can vastly aid in ensuring a smooth user experience by selecting the best radio access technology (RAT) from a multi-RAT environment for a specific traffic flow. In this paper, we propose a novel load-aware traffic steering algorithm based on hierarchical reinforcement learning (HRL) while satisfying the diverse quality of service requirements of different traffic types. HRL can significantly increase system performance using a bi-level architecture having a meta-controller and a controller. In our proposed method, the meta-controller provides an appropriate threshold for load balancing, while the controller performs traffic admission to an appropriate RAT in the lower level. Simulation results show that HRL outperforms a Deep Q-Learning (DQN) and a threshold-based heuristic baseline with 8.49%, 12.52% higher average system throughput and 27.74%, 39.13% lower network delay, respectively.
Optimal Transport for Counterfactual Estimation: A Method for Causal Inference

Many problems ask a question that can be formulated as a causal question:"what would have happened if. "For example,"would the person have had surgery if he or she had been Black? "To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by x, a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of x. We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable we are trying to quantify the effect of. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).
RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes

Radiance fields have gradually become a main representation of media. Although its appearance editing has been studied, how to achieve view-consistent recoloring in an efficient manner is still under explored. We present RecolorNeRF, a novel user-friendly color editing approach for the neural radiance fields. Our key idea is to decompose the scene into a set of pure-colored layers, forming a palette. By this means, color manipulation can be conducted by altering the color components of the palette directly. To support efficient palette-based editing, the color of each layer needs to be as representative as possible. In the end, the problem is formulated as an optimization problem, where the layers and their blending weights are jointly optimized with the NeRF itself. Extensive experiments show that our jointly-optimized layer decomposition can be used against multiple backbones and produce photo-realistic recolored novel-view renderings. We demonstrate that RecolorNeRF outperforms baseline methods both quantitatively and qualitatively for color editing even in complex real-world scenes.
Rapid generation and number-resolved detection of spinor rubidium Bose-Einstein condensates

High data acquisition rates and low-noise detection of ultracold neutral atoms present important challenges for the state tomography and interferometric application of entangled quantum states in Bose-Einstein condensates. In this article, we present a high-flux source of $^{87}$Rb Bose-Einstein condensates combined with a number-resolving detection. We create Bose-Einstein condensates of $2\times10^5$ atoms with no discernible thermal fraction within $3.3$ s using a hybrid evaporation approach in a magnetic/optical trap. For the high-fidelity tomography of many-body quantum states in the spin degree of freedom [arXiv:2207.01270], it is desirable to select a single mode for a number-resolving detection. We demonstrate the low-noise selection of subsamples of up to $16$ atoms and their subsequent detection with a counting noise below $0.2$ atoms. The presented techniques offer an exciting path towards the creation and analysis of mesoscopic quantum states with unprecedented fidelities, and their exploitation for fundamental and metrological applications.
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection

Neural sequence generation models are known to “hallucinate”, by producing outputs that are unrelated to the source text. These hallucinations are potentially harmful, yet it remains unclear in what conditions they arise and how to mitigate their impact. In this work, we first identify internal model symptoms of hallucinations by analyzing the relative token contributions to the generation in contrastive hallucinated vs. non-hallucinated outputs generated via source perturbations. We then show that these symptoms are reliable indicators of natural hallucinations, by using them to design a lightweight hallucination detector which outperforms both model-free baselines and strong classifiers based on quality estimation or large pre-trained models on manually annotated English-Chinese and German-English translation test beds.
Power duality for multi-antenna OFDM system in broadcast channel with user scheduling

This paper deals with design and analysis of user scheduling and power allocation for multi-antenna OFDM systems with DPC, ZF-DPC, ZF-BF and TDMA transmit strategies. We consider the general multi-user downlink scheduling problem and power minimization with multi-user rate constraints. According to the channel state, it is shown that there is a power optimal policy which selects a subset of users in each scheduling interval. We present user selection algorithms for DPC, ZF-DPC, ZF-BF and TDMA for multi-antenna OFDM system in broadcast channels, and we also present the practical water-filling solution in this paper. By the selected users with the consideration of fairness, we derive the power optimization algorithm with multi-user rate constraints. We also analyze the power duality of uplink-downlink for the transmit strategies of DPC, ZF-DPC and ZF-BF. Simulation results show that the present user-scheduling algorithm and power minimization algorithm can achieve good power performance, and that the scheduling algorithm can guarantee fairness.
Isentropic compression of cyclotetramethylene tetranitramine (HMX) single crystals to 50GPa

Single crystals of cyclotetramethylene tetranitramine (HMX) were isentropically compressed perpendicular to (010) and (011) faces at the Sandia Z-Machine. A 50GPa ramped magnetic pressure load of about 200ns rise time loaded four specimens of each orientation. HMX specimens were from 300–600μm thick. Velocity histories at the rear of each crystal were measured by Doppler velocimetry. Although a phase change in HMX at 27GPa has been proposed based upon isothermal data, no evidence of this change is seen in our analyses between 5 and 50GPa along the isentrope. Previous isentropic loading experiments on HMX had not shown evidence of a phase change either, but those experiments were complicated by the use of NaCl interferometer windows that have a phase change near the pressure of interest. The experiments described in this paper employed LiF interferometer windows that are known to be absent phase changes in the regime of application. Accurate determination of isentropic compressibility for HMX was not possi.
A three-dimensional strain model for the superconducting properties of strained International Thermonuclear Experimental Reactor Nb3Sn strands

The critical current density parameterization for the International Thermonuclear Experimental Reactor (ITER) Nb3Sn production accounting for the three-dimensional (3D) nature of strain is significant for the analysis of the performance of magnet systems. A simple model for the 3D strain dependence of the critical current density in ITER Nb3Sn strands is proposed in this paper. It is on the basis of the strain-induced variation in the electronic density of states at the Fermi surface. The validity of the proposed scaling law is verified by making comparisons with experimental data acquired on the bare/jacketed Nb3Sn wire. The derived 3D model in axial form is capable of adequately describing the anomalies in the strain function of the Nb3Sn wires jacketed with AISI 316 L stainless steel, which arise from a multi-dimensional strain effect, while none of the existing research models can describe such trend. The model provides a new way of identifying the scaling relation for the critical current density in .
Growth/dissolution model for oxygen precipitation based on the kinetics of phase transformations

A model is presented for the growth and dissolution of oxygen precipitates in Czochralski silicon during heat treatment. Growth and dissolution rates are newly derived and inserted into a set of chemical rate equations and a Fokker–Planck equation. It can calculate the size distribution of the oxygen precipitates and oxygen concentration profile without calculation of the interfacial concentrations at the interface of Si matrix and precipitates. It accounts for the oxidizing ambient effect, the solubility enhancement effect of oxygen, and the surface recombination and generation of point defects. The formation of stacking faults is also taken into account. This approach allows one to calculate more accurately the residual oxygen depth profile and the density distribution of oxygen precipitates which can be measured experimentally. By comparing the simulated results with experimental ones, it is proved that this model can be used to estimate the depth profile and the defect densities under inert conditions and oxidation conditions.
Particle-size segregation in self-channelized granular flows

Abstract Geophysical mass flows such as debris flows, dense pyroclastic flows and snow avalanches can self-channelize on shallow slopes. The confinement afforded by formed levees helps to maintain the flow depth, and hence mobility, allowing self-channelized flows to run out significantly farther than unconfined, spreading flows. Levee formation and self-channelization are strongly associated with particle-size segregation, but can also occur in monodisperse flows. This paper uses the monodisperse depth-averaged theory of Rocha et al. (J. Fluid Mech., vol. 876, 2019, pp. 591–641), which incorporates a hysteretic friction law and second-order depth-averaged viscous terms. Both of these are vital for the formation of a travelling wave that progressively deposits a pair of levees just behind the front. The three-dimensional velocity field is reconstructed in a frame moving with the front assuming Bagnold flow. This enables a bidisperse particle-size segregation theory to be used to solve for the large and small particle concentrations and particle paths in three-dimensions, for the first time. The model shows that the large particles tend to segregate to the surface of the flow, forming a carapace that extends over the centre of the channel, as well as along the external sides and base of the levee walls. The small particles segregate downwards, and are concentrated in the main channel and in the inner levee walls. This supports the contention that a low-friction channel lining provides a secondary mechanism for run-out enhancement. It is also shown that the entire theory scales with particle diameter, so experiments with millimetre-sized particles provide important insights into geophysical-scale flows with boulders and smaller rock fragments. The model shows that self-channelization does not need particle-size segregation to occur, but supports the hypothesis that particle-size segregation and the associated frictional feedback can significantly enhance both the flow mobility and the levee strength.
Missing Stratospheric Ozone Decrease at Southern Hemisphere Middle Latitudes after Mt. Pinatubo: A Dynamical Perspective

AbstractAlthough large total ozone decreases occurred in the Northern Hemisphere extratropics in the years after the volcanic eruption of Mt. Pinatubo that are generally attributed to the eruption, comparable decreases did not emerge in the Southern Hemisphere. To study this missing decrease, a multiple linear regression was applied to the Chemical and Dynamical Influences on Decadal Ozone Change (CANDIDOZ) Assimilated Three-Dimensional Ozone (CATO) dataset including the solar cycle, the quasi-biennial oscillation (QBO), the effect of volcanic eruptions, the lower stratospheric (LS) Eliassen–Palm (EP) flux to describe the Brewer–Dobson circulation, and stratospheric chlorine increase as explanatory variables. Volcanically induced chemical ozone depletion was overcompensated by the QBO and by a pronounced EP flux anomaly. Using NCEP–NCAR reanalysis data, it is found that the anomalous EP flux was caused by several significant stratospheric wave events (SWEs) from September–November 1991 through 1992 that, .
Opinion Leaders for Information Diffusion Using Graph Neural Network in Online Social Networks

Various opportunities are available to depict different domains due to the diverse nature of social networks and researchers' insatiable. An opinion leader is a human entity or cluster of people who can redirect human assessment strategy by intellectual skills in a social network. A more comprehensive range of approaches is developed to detect opinion leaders based on network-specific and heuristic parameters. For many years, deep learning–based models have solved various real-world multifaceted, graph-based problems with high accuracy and efficiency. The Graph Neural Network (GNN) is a deep learning–based model that modernized neural networks’ efficiency by analyzing and extracting latent dependencies and confined embedding via messaging and neighborhood aggregation of data in the network. In this article, we have proposed an exclusive GNN for Opinion Leader Identification (GOLI) model utilizing the power of GNNs to categorize the opinion leaders and their impact on online social networks. In this model, we first measure the n-node neighbor's reputation of the node based on materialized trust. Next, we perform centrality conciliation instead of the input data's conventional node-embedding mechanism. We experiment with the proposed model on six different online social networks consisting of billions of users’ data to validate the model's authenticity. Finally, after training, we found the top-N opinion leaders for each dataset and analyzed how the opinion leaders are influential in information diffusion. The training-testing accuracy and error rate are also measured and compared with the other state-of-art standard Social Network Analysis (SNA) measures. We determined that the GNN-based model produced high performance concerning accuracy and precision.
A Multifactor Ring Signature based Authentication Scheme for Quality Assessment of IoMT Environment in COVID-19 Scenario

The quality of the healthcare environment has become an essential factor for healthcare users to access quality services. Smart healthcare systems use the Internet of Medical Things (IoMT) devices to capture patients’ health data for treatment or diagnostic purposes. This sensitive collected patient data is shared between the different stakeholders across the network to provide quality services. Due to this, healthcare systems are vulnerable to confidentiality, integrity and privacy threats. In the COVID-19 scenario, when collaborative medical consultation is required, the quality assessment of the framework is essential to protect the privacy of doctors and patients. In this paper, a ring signature-based anonymous authentication and quality assessment scheme is designed for collaborative medical consultation environments for quality assessment and protection of the privacy of doctors and patients. This scheme also uses a new KMOV Cryptosystem to ensure the quality of the network and protect the system from different attacks that hamper data confidentiality.