Miracle-dz/UniFAR-FTUs huggingface dataset:
A NEW ASSESSMENT OF THE LARGE-TANK GENERAL ELECTRIC SWELL PROBLEM USING RELAP5-3D

The RELAP5-3D (version 1.5.1) computer program was used to assess a subset of the GE level swell experiments. The primary goal of this new assessment was to provide an updated evaluation of latest fluid flow modeling capability available in RELAP5-3D. In this evaluation great care was taken to faithfully represent the experimental facility and instrumentation. The GE level swell tests were performed using two vessel sizes. The vessels had nominal diameters of 0.3048 m (1 ft) and 1.2192 m (4 ft). This assessment will focus exclusively on four tests, with flow-limiting outlet venturi of different diameters, performed on the larger of the two vessels. The new assessment highlights the sensitivity of choked-flow limited calculations to the value of the input discharge coefficients. Unlike other assessments of choked-flow phenomena, the results of this assessment are essentially insensitive to the particular choked-flow model being employed. However, the use of the optional Henry-Fauske critical flow model is recommended in lieu of the default RELAP5-3D Ransom-Trapp model for consistency with other recent assessments. This evaluation also highlights the effect of time step size on the calculated results. The variation in results obtained using small time steps was unexpected. However, the effect of these variations on the predicted time-dependent blowdown of the system has been shown to be very small. In addition, this assessment has examined a variety of RELAP5-3D interfacial drag correlations. It has been shown that the experimentally-determined void fraction profiles and two-phase mixture levels are best modeled using the optional Vea-Lahey interfacial drag correlation. This paper provides the RELAP5-3D user with guidance as to which correlations to invoke for best-estimate calculations of blowdown transients similar in nature to the GE level swell experiments. Evidence is provided for chosing the interfacial drag model and the critical flow model including its corresponding adjustable coefficients.
Quantum time inversion to prevent DDoS attacks: A potentially realizable TENET technology

An eﬀect known as time inversion was introduced in Christopher Nolan’s ﬁlm TENET , and here we demonstrate that such an eﬀect exists for quantum information. We do this by investigating the von Neumann entropy of experimentally realized photons that are entangled in time. Using these ﬁndings, we design a technology called a Turnstile and apply it to detect Distributed Denial-of-Service (DDoS) attacks in quantum networks. Pragmatically, our work can be viewed as formulating the quantum analogue of the Shannon entropic DDoS detection systems used in classical networks.
Analytical model for p-channel MOSFETs

An analytical model for p-channel MOSFETs is described. The model is based on the unified charge control model (UCCM), which describes both the subthreshold and the above-threshold regimes using one continuous equation. Also derived and incorporated into the model is an equation for the dependence of the hole mobility on gate-to-source voltage and threshold voltage. The model makes it possible to propose a simple and unambiguous characterization procedure for extracting device parameters. Detailed measurements of capacitance-voltage and current-voltage characteristics of p-channel MOSFETs with different gate lengths are reported. Results are in excellent agreement with experimental results. The model is ideally suited for applications in computer-aided design software for simulation of both digital and analog circuits, and for automated parameter extraction. >
The Effects of Metallicity on Convective Overshoot Behavior in Models of δ Scuti Variable Stars

δ Scuti variables are stars which exhibit periodic changes in their luminosity through radial and nonradial pulsations. Internally, these stars have relatively small convective cores, and convective overshoot can significantly affect the size. Recently, models of radial pulsation in δ Scuti stars found a strong correlation between the pulsation constant (Q) as a function of effective temperature and the amount of convective overshoot within the star. However, only models with metallicities of Z = 0.02 were examined, leaving the dependence of` this relationship on chemical composition unknown. In this work, we have extended the model grid to cover a range of metallicities using Modules for Experiments in Stellar Astrophysics (), and analyzed the models’ pulsation properties using GYRE. By varying the models’ mass, rotation speed, convective overshoot, and metallicity, we studied the behavior of Q at low temperature. We found that the updated convective boundary treatment in MESA changes the overshoot dependence found previously, and the value of the slope depends on both rotation and overshoot. We also found that there is a metallicity dependence in the Q values. The lowest metallicity models in our grid reached higher temperatures than previously studied, revealing a parabolic relation between logQ and logTeff .
Analysis of very fast transients in transformers

Very fast transient overvoltages generated in gas-insulated switchgear could cause a voltage oscillation inside the connected transformer. A practical method to calculate the high-frequency transients in the transformer winding is developed based on multiconductor transmission-line theory. Resonance characteristics of a transformer are assessed using the inductance matrix obtainable from its winding geometry. Applicability to transients of frequencies up to several megahertz is checked in a model transformer and an actual 500 kV transformer. The calculated interturn voltage waveforms are in particularly good agreement with experiment.
Network In Graph Neural Network

Graph Neural Networks (GNNs) have shown success in learning from graph structured data containing node/edge feature information, with application to social networks, recommendation, fraud detection and knowledge graph reasoning. In this regard, various strategies have been proposed in the past to improve the expressiveness of GNNs. For example, one straightforward option is to simply increase the parameter size by either expanding the hid-den dimension or increasing the number of GNN layers. However, wider hidden layers can easily lead to overfitting, and incrementally adding more GNN layers can potentially result in over-smoothing.In this paper, we present a model-agnostic methodology, namely Network In Graph Neural Network (NGNN ), that allows arbitrary GNN models to increase their model capacity by making the model deeper. However, instead of adding or widening GNN layers, NGNN deepens a GNN model by inserting non-linear feedforward neural network layer(s) within each GNN layer. An analysis of NGNN as applied to a GraphSage base GNN on ogbn-products data demonstrate that it can keep the model stable against either node feature or graph structure perturbations. Furthermore, wide-ranging evaluation results on both node classification and link prediction tasks show that NGNN works reliably across diverse GNN architectures.For instance, it improves the test accuracy of GraphSage on the ogbn-products by 1.6% and improves the hits@100 score of SEAL on ogbl-ppa by 7.08% and the hits@20 score of GraphSage+Edge-Attr on ogbl-ppi by 6.22%. And at the time of this submission, it achieved two first places on the OGB link prediction leaderboard.
Pruning Self-attentions into Convolutional Layers in Single Path

Vision Transformers (ViTs) have achieved impressive performance over various computer vision tasks. However, modeling global correlations with multi-head self-attention (MSA) layers leads to two widely recognized issues: the massive computational resource consumption and the lack of intrinsic inductive bias for modeling local visual patterns. To solve both issues, we devise a simple yet effective method named Single-Path Vision Transformer pruning (SPViT), to efficiently and automatically compress the pre-trained ViTs into compact models with proper locality added. Specifically, we first propose a novel weight-sharing scheme between MSA and convolutional operations, delivering a single-path space to encode all candidate operations. In this way, we cast the operation search problem as finding which subset of parameters to use in each MSA layer, which significantly reduces the computational cost and optimization difficulty, and the convolution kernels can be well initialized using pre-trained MSA parameters. Relying on the single-path space, we introduce learnable binary gates to encode the operation choices in MSA layers. Similarly, we further employ learnable gates to encode the fine-grained MLP expansion ratios of FFN layers. In this way, our SPViT optimizes the learnable gates to automatically explore from a vast and unified search space and flexibly adjust the MSA-FFN pruning proportions for each individual dense model. We conduct extensive experiments on two representative ViTs showing that our SPViT achieves a new SOTA for pruning on ImageNet-1k. For example, our SPViT can trim 52.0% FLOPs for DeiT-B and get an impressive 0.6% top-1 accuracy gain simultaneously. The source code is available at https://github.com/ziplab/SPViT.
Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking

Recently, template-based trackers have become the leading tracking algorithms with promising performance in terms of efficiency and accuracy. However, the correlation operation between query feature and the given template only achieves accurate target localization, but is prone to state estimation error, especially when the target suffers from severe deformation. To address this issue, segmentation-based trackers are proposed that use per-pixel matching to improve the tracking performance of deformable objects effectively. However, most of the existing trackers only match with the target features of the initial frame, thereby lacking the discrimination for handling a variety of challenging factors, e.g., similar distractors, background clutter, and appearance change. To this end, we propose a dynamic compact memory embedding technique to enhance the discrimination of the segmentation-based visual tracking method that can well tell the target from the background. Specifically, we initialize a memory embedding with the target features in the first frame. During the tracking process, the current target features that have certain correlation with the existing memory are updated to the memory embedding online. To further improve the tracking accuracy for deformable objects, we use a weighted point-to-global matching strategy to measure the correlation between the pixelwise query feature and the whole template, so as to capture more detailed deformation information. Extensive evaluations on six challenging tracking benchmarks including VOT2016, VOT2018, VOT2019, GOT-10K, TrackingNet, and LaSOT demonstrate the superiority of our method over recent remarkable trackers. Besides, our tracker outperforms the excellent segmentation-based trackers, i.e., D3S and SiamMask on the DAVIS2017 benchmark. The code is available at https://github.com/peace-love243/CMEDFL.
Crossing the Format Boundary of Text and Boxes: Towards Unified Vision-Language Modeling

In this paper, we propose U NICORN , a vision-language (VL) model that uniﬁes text generation and bounding box prediction into a single architecture. Speciﬁcally, we quan-tize each box into four discrete box tokens and serialize them as a sequence, which can be integrated with text to-kens. We formulate all VL problems as a generation task, where the target sequence consists of the integrated text and box tokens. We then train a transformer encoder-decoder to predict the target in an auto-regressive manner. With such a uniﬁed framework and input-output format, U NICORN achieves comparable performance to task-speciﬁc state of the art on 7 VL benchmarks, covering the visual grounding, grounded captioning, visual question answering, and image captioning tasks. When trained with multi-task ﬁnetuning, U NICORN can approach different VL tasks with a single set of parameters, thus crossing downstream task boundary. We show that having a single model not only saves parameters, but also further boosts the model performance on certain tasks. Finally, U NICORN shows the capability of generalizing to new tasks such as ImageNet object localization.
Deformable image registration with deep network priors: a study on longitudinal PET images

Objective. This paper proposes a novel approach for the longitudinal registration of PET imaging acquired for the monitoring of patients with metastatic breast cancer. Unlike with other image analysis tasks, the use of deep learning (DL) has not significantly improved the performance of image registration. With this work, we propose a new registration approach to bridge the performance gap between conventional and DL-based methods: medical image registration method regularized by architecture (MIRRBA). Approach. MIRRBA is a subject-specific deformable registration method which relies on a deep pyramidal architecture to parametrize the deformation field. Diverging from the usual deep-learning paradigms, MIRRBA does not require a learning database, but only a pair of images to be registered that is used to optimize the network's parameters. We applied MIRRBA on a private dataset of 110 whole-body PET images of patients with metastatic breast cancer. We used different architecture configurations to produce the deformation field and studied the results obtained. We also compared our method to several standard registration approaches: two conventional iterative registration methods (ANTs and Elastix) and two supervised DL-based models (LapIRN and Voxelmorph). Registration accuracy was evaluated using the Dice score, the target registration error, the average Hausdorff distance and the detection rate, while the realism of the registration obtained was evaluated using Jacobian's determinant. The ability of the different methods to shrink disappearing lesions was also computed with the disappearing rate. Main results. MIRRBA significantly improved all metrics when compared to DL-based approaches. The organ and lesion Dice scores of Voxelmorph improved by 6% and 52% respectively, while the ones of LapIRN increased by 5% and 65%. Regarding conventional approaches, MIRRBA presented comparable results showing the feasibility of our method. Significance. In this paper, we also demonstrate the regularizing power of deep architectures and present new elements to understand the role of the architecture in DL methods used for registration.
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning

Representation learning lies at the heart of the empirical success of deep learning for dealing with the curse of dimensionality. However, the power of representation learning has not been fully exploited yet in reinforcement learning (RL), due to i), the trade-off between expressiveness and tractability; and ii), the coupling between exploration and representation learning. In this paper, we first reveal the fact that under some noise assumption in the stochastic control model, we can obtain the linear spectral feature of its corresponding Markov transition operator in closed-form for free. Based on this observation, we propose Spectral Dynamics Embedding (SPEDE), which breaks the trade-off and completes optimistic exploration for representation learning by exploiting the structure of the noise. We provide rigorous theoretical analysis of SPEDE, and demonstrate the practical superior performance over the existing state-of-the-art empirical algorithms on several benchmarks.
Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station

Devising optimal operating strategies for a compressor station relies on the knowledge of compressor characteristics. As the compressor characteristics change with time and use, it is necessary to provide accurate models of the characteristics that can be used in optimization of the operating strategy. This paper proposes a new algorithm for online learning of the characteristics of the compressors using Gaussian Processes. The performance of the new approximation is shown in a case study with three compressors. The case study shows that Gaussian Processes accurately capture the characteristics of compressors even if no knowledge about the characteristics is initially available. The results show that the flexible nature of Gaussian Processes allows them to adapt to the data online making them amenable for use in real-time optimization problems.
The Brownian transport map

Contraction properties of transport maps between probability measures play an important role in the theory of functional inequalities. The actual construction of such maps, however, is a non-trivial task and, so far, relies mostly on the theory of optimal transport. In this work, we take advantage of the infinite-dimensional nature of the Gaussian measure and construct a new transport map, based on the F\"ollmer process, which pushes forward the Wiener measure onto probability measures on Euclidean spaces. Utilizing the tools of the Malliavin and stochastic calculus in Wiener space, we show that this Brownian transport map is a contraction in various settings where the analogous questions for optimal transport maps are open. The contraction properties of the Brownian transport map enable us to prove functional inequalities in Euclidean spaces, which are either completely new or improve on current results. Further and related applications of our contraction results are the existence of Stein kernels with desirable properties (which lead to new central limit theorems), as well as new insights into the Kannan--Lov\'asz--Simonovits conjecture. We go beyond the Euclidean setting and address the problem of contractions on the Wiener space itself. We show that optimal transport maps and causal optimal transport maps (which are related to Brownian transport maps) between the Wiener measure and other target measures on Wiener space exhibit very different behaviors.
A New Candidate Structure of the H2-PRE Phase of Solid Hydrogen

Experimental progress finally reached the metallic solid hydrogen phase, which was predicted by Wigner and Huntington over 80 years ago. However, the different structures in the phase diagram are still been debated due to the difficulty of diffraction experiments for high-pressured hydrogen. The determination of crystal structures under extreme condition is both of the basic condensed matter physics, and in planetary science: the behavior of giant gaseous planets (e.g. Jupiter, Saturn.) strongly depends on the properties of inner high-pressured hydrogen. This work describes new possible structures appearing under high pressures of 400∼600 GPa. We applied a structural search using particle swarm optimization with density functional theory (DFT) to propose several candidate structures. For these structures, we performed fixed-node diffusion Monte Carlo simulations combined with DFT zero-point energy corrections to confirm their relative stability. We found P21/c-8 as a promising candidate structure for the H2-PRE phase. P21/c-8 is predicted the most stable at 400 and 500 GPa. P21/c-8 reproduces qualitatively the IR spectrum peaks observed in the H2-PRE phase. a
Fully kinetic model of plasma expansion in a magnetic nozzle

A self-consistent model is presented for performing steady-state fully kinetic particle-in-cell simulations of magnetised plasma plumes. An energy-based electron reflection prevents the numerical pump instability associated with a typical open-outflow boundary, and is shown to be sufficiently general that both the plume kinetics and plasma potential demonstrate domain independence (within 6%). This is upheld by non-stationary Robin-type boundary conditions on the Poisson’s equation, coupled to a capacitive circuit that allows physical evolution of the downstream potential drop in the transient. The method has been validated against experiments, providing results that fall within the uncertainty of measurements. Simulations are then carried out to study collisional xenon discharges into axisymmetric diverging magnetic nozzles (MNs). Particular discussion is given to the identification of a collision-enhanced potential well arising from charge separation at the plume periphery, the role of ion–neutral charge exchange, and a three-region piecewise polytropic cooling regime for electrons. The polytropic index is shown to depend on the degree of magnetisation. Specifically, in the region near the thruster outlet, the plume is weakly-magnetised due to the cross-field diffusion of electron-heavy particle collisions. Downstream, a strongly-magnetised region of near-isothermal expansion occurs. Finally, in the detached region, the polytropic index tends to that of a more adiabatic unmagnetised case. With an increasing MN field strength, an inferior limit is found to the average polytropic index of γ¯e∼1.16 .
Hyperfine Interaction in a MoS$_2$ Quantum Dot: Decoherence of a Spin-Valley Qubit

A successful and promising device for the physical implementation of electron spin-valley based qubits is the Transition Metal Dichalcogenide monolayer (TMD-ML) semiconductor quantum dot. The electron spin in TMD-ML semiconductor quantum dots can be isolated and controlled with high accuracy, but it still suffers from decoherence due to the unavoidable coupling with the surrounding environment, such as nuclear spin environments. A common tool to investigate systems like the one considered in this work is the density matrix formalism by presenting an exact master equation for a central spin (spin-qubit) system in a time-dependent and coupled to a nuclear spin bath in terms of hyperfine interaction. The master equation provides a unified description of the dynamics of the central spin. Analyzing this in more detail, we calculate fidelity loss due to the Overhauser field from hyperfine interaction in a wide range number of nuclear spins $\mathcal{N}$.
LEGOStore: A Linearizable Geo-Distributed Store Combining Replication and Erasure Coding

We design and implement LEGOStore, an erasure coding (EC) based linearizable data store over geo-distributed public cloud data centers (DCs). For such a data store, the confluence of the following factors opens up opportunities for EC to be latency-competitive with replication: (a) the necessity of communicating with remote DCs to tolerate entire DC failures and implement linearizability; and (b) the emergence of DCs near most large population centers. LEGOStore employs an optimization framework that, for a given object, carefully chooses among replication and EC, as well as among various DC placements to minimize overall costs. To handle workload dynamism, LEGOStore employs a novel agile reconfiguration protocol. Our evaluation using a LEGOStore prototype spanning 9 Google Cloud Platform DCs demonstrates the efficacy of our ideas. We observe cost savings ranging from moderate (5-20%) to significant (60%) over baselines representing the state of the art while meeting tail latency SLOs. Our reconfiguration protocol is able to transition key placements in 3 to 4 inter-DC RTTs (< 1s in our experiments), allowing for agile adaptation to dynamic conditions.