Miracle-dz/UniFAR-FTUs huggingface dataset:
AN EXPERIMENTAL STUDY OF ELASTOMERIC BRIDGE BEARINGS WITH DESIGN RECOMMENDATIONS

Recent AASHTO specifications have placed a number of restrictions on the use of elastomeric bridge bearings. Elastomeric bearings with tapers built in to accommodate span end elevation differences were disallowed by the most current specifications even though no previous research into elastomeric bearing performance has included tests on tapered pads. The purpose of this study was to analyze elastomeric bearing performance on the basis of elastomer hardness, shape factor, reinforcing shim orientation, degree of taper and compressive stress level with the goal of developing a simple design procedure which standardizes as many of those parameters as possible. Particular emphasis was placed on comparing the behavior of flat and tapered bearings. Experimentation included shear, compressive, and rotational stiffness tests, shear and compression fatigue loading, long-term compressive loading, and tests to determine compressive stress limits. In many cases, bearings were intentionally loaded non-uniformly to define safe limits for bearing/girder slope mismatches. Research showed that tapered elastomeric bearings performed equally as well as flat bearings and that manufacturing tapered bearings with steel shims oriented parallel to one another offers several benefits over spacing shims radially. Additionally, bearings made from elastomers with lower hardness ratings displayed several advantages over those made from harder elastomers, particularly rotation capacity far in excess of the current AASHTO limitation. More highly reinforced bearings performed better in compression fatigue and creep tests and easily accommodated compressive stresses in excess of 6.9 MPa (1000 psi). Mismatches between the slope of the girder and the bearing taper had little effect on performance. Also, a field and laboratory investigation was performed into bearing slip and anchorage by friction. Bearings that had slipped while in service were subjected to comparable conditions in a laboratory test apparatus to study their behavior. Research showed that secretion of antiozonant waxes to the bearings' surfaces caused a dramatic lowering of the bearing's friction coefficient and resulted in slipping. Several tests were performed that established friction coefficients for natural rubber against a variety of steel and concrete surfaces. A standard design for Texas bearings is recommended. Recommended changes to the current AASHTO specification are presented.
PIDformer: Transformer Meets Control Theory

In this work, we address two main shortcomings of transformer architectures: input corruption and rank collapse in their output representation. We unveil self-attention as an autonomous state-space model that inherently promotes smoothness in its solutions, leading to lower-rank outputs and diminished representation capacity. Moreover, the steady-state solution of the model is sensitive to input perturbations. We incorporate a Proportional-Integral-Derivative (PID) closed-loop feedback control system with a reference point into the model to improve robustness and representation capacity. This integration aims to preserve high-frequency details while bolstering model stability, rendering it more noise-resilient. The resulting controlled state-space model is theoretically proven robust and adept at addressing the rank collapse. Motivated by this control framework, we derive a novel class of transformers, PID-controlled Transformer (PIDformer), aimed at improving robustness and mitigating the rank-collapse issue inherent in softmax transformers. We empirically evaluate the model for advantages and robustness against baseline transformers across various practical tasks, including object classification, image segmentation, and language modeling.
Poisson-Gamma Dynamical Systems with Non-Stationary Transition Dynamics

Bayesian methodologies for handling count-valued time series have gained prominence due to their ability to infer interpretable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with noisy and incomplete count data. Among these Bayesian models, Poisson-Gamma Dynamical Systems (PGDSs) are proven to be effective in capturing the evolving dynamics underlying observed count sequences. However, the state-of-the-art PGDS still falls short in capturing the time-varying transition dynamics that are commonly observed in real-world count time series. To mitigate this limitation, a non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time, and the evolving transition matrices are modeled by sophisticatedly-designed Dirichlet Markov chains. Leveraging Dirichlet-Multinomial-Beta data augmentation techniques, a fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance due to its capacity to learn non-stationary dependency structure captured by the time-evolving transition matrices.
Four-gluon vertex in collinear kinematics

To date, the four-gluon vertex is the least explored component of the QCD Lagrangian, mainly due to the vast proliferation of Lorentz and color structures required for its description. In this work we present a nonperturbative study of this vertex, based on the one-loop dressed Schwinger-Dyson equation obtained from the 4PI effective action. A vast simplification is brought about by resorting to ``collinear'' kinematics, where all momenta are parallel to each other, and by appealing to the charge conjugation symmetry in order to eliminate certain color structures. Out of the fifteen form factors that comprise the transversely-projected version of this vertex, two are singled out and studied in detail; the one associated with the classical tensorial structure is moderately suppressed in the infrared regime, while the other diverges logarithmically at the origin. Quite interestingly, both form factors display the property known as ``planar degeneracy'' at a rather high level of accuracy. With these results we construct an effective charge that quantifies the strength of the four-gluon interaction, and compare it with other vertex-derived charges from the gauge sector of QCD.
Investigating the basis set convergence of diagrammatically decomposed coupled-cluster correlation energy contributions for the uniform electron gas

We investigate the convergence of coupled-cluster correlation energies and related quantities with respect to the employed basis set size for the uniform electron gas to gain a better understanding of the basis set incompleteness error. To this end, coupled-cluster doubles (CCD) theory is applied to the three dimensional uniform electron gas for a range of densities, basis set sizes and electron numbers. We present a detailed analysis of individual, diagrammatically decomposed contributions to the amplitudes at the level of CCD theory. In particular, we show that only two terms from the amplitude equations contribute to the asymptotic large-momentum behavior of the transition structure factor, corresponding to the cusp region at short interelectronic distances. However, due to the coupling present in the amplitude equations, all decomposed correlation energy contributions show the same asymptotic convergence behavior to the complete basis set limit. These findings provide an additional rationale for the success of a recently proposed correction to the basis set incompleteness error (BSIE) of coupled-cluster theory. Lastly, we examine the BSIE in the coupled-cluster doubles plus perturbative triples [CCD(T)] method, as well as in the newly proposed coupled-cluster doubles plus complete perturbative triples [CCD(cT)] method.
CFD simulation of transient startup for a low specific-speed pump-turbine

Hydropower plants are very well suited for the modern electricity market which depends on increased flexibility and storage capabilities. Nevertheless, increased dynamic operation leads to new challenges in the design as well as the mechanical integrity of the equipment. In particular, the number of start and stops has significantly increased in recent years. The startup of the machine is a transient procedure with considerable load-oscillations which can affect the lifespan of the machine. Detailed understanding of transient effects in pump-turbines is therefore useful for improved prediction of loads and an important tool in pump-turbine layout and design. This study discusses the application of unsteady RANS CFD to fully transient pump-turbine operations. A low specific-speed pump-turbine during startup is investigated and results are discussed accordingly. The boundary conditions are imposed from 1D hydro-acoustic results of the power plant, using time dependent quantities like guide-vane angle, rotational speed and flow rate. Previous studies of the same authors contain simulations such as runaway or load-rejection, where the same method has been successfully applied. Turbine-startup results in terms of head and torque can be compared to the 1D model data for the validation of the CFD procedure. Instantaneous fluctuation of the same quantities can be observed as runner speed increases. A flow feature analysis shows that partial pumping takes place in all individual runner passages, resulting in significant global pressure fluctuations. This phenomenon can be associated with unsteady vortex formation as the characteristic reaches the speed-no load curve close to the S-shape of the characteristic.
HiGPT: Heterogeneous Graph Language Model

Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for nodes and edges. Recent advancements in heterogeneous graph neural networks (HGNNs) have achieved state-of-the-art performance by considering relation heterogeneity and using specialized message functions and aggregation rules. However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets. Most of these frameworks follow the"pre-train"and"fine-tune"paradigm on the same dataset, which restricts their capacity to adapt to new and unseen data. This raises the question:"Can we generalize heterogeneous graph models to be well-adapted to diverse downstream learning tasks with distribution shifts in both node token sets and relation type heterogeneity? '' To tackle those challenges, we propose HiGPT, a general large graph model with Heterogeneous graph instruction-tuning paradigm. Our framework enables learning from arbitrary heterogeneous graphs without the need for any fine-tuning process from downstream datasets. To handle distribution shifts in heterogeneity, we introduce an in-context heterogeneous graph tokenizer that captures semantic relationships in different heterogeneous graphs, facilitating model adaptation. We incorporate a large corpus of heterogeneity-aware graph instructions into our HiGPT, enabling the model to effectively comprehend complex relation heterogeneity and distinguish between various types of graph tokens. Furthermore, we introduce the Mixture-of-Thought (MoT) instruction augmentation paradigm to mitigate data scarcity by generating diverse and informative instructions. Through comprehensive evaluations, our proposed framework demonstrates exceptional performance in terms of generalization performance.
Cold Beam of $^7$Li$^4$He Dimers

We demonstrate an approach for producing a high flux beam of $^7$Li$^4$He dimers by performing post-nozzle seeding of a lithium beam into a supersonic helium expansion. The molecular beam has a transverse temperature of 11(4) mK and a continuous flux on the order of $10^{11}$ dimers/s. By simulating molecular formation in the jet, we find quantitative agreement with the measured dimer flux using ab-initio quantum scattering theory for three-body processes. Extensions of this work could lead towards a more quantitative understanding of other few-body collision processes of alkali and helium atoms.
Intelligent Attractors for Singularly Perturbed Dynamical Systems

Singularly perturbed dynamical systems, commonly known as fast-slow systems, play a crucial role in various applications such as plasma physics. They are closely related to reduced order modeling, closures, and structure-preserving numerical algorithms for multiscale modeling. A powerful and well-known tool to address these systems is the Fenichel normal form, which significantly simplifies fast dynamics near slow manifolds through a transformation. However, the Fenichel normal form is difficult to realize in conventional numerical algorithms. In this work, we explore an alternative way of realizing it through structure-preserving machine learning. Specifically, a fast-slow neural network (FSNN) is proposed for learning data-driven models of singularly perturbed dynamical systems with dissipative fast timescale dynamics. Our method enforces the existence of a trainable, attracting invariant slow manifold as a hard constraint. Closed-form representation of the slow manifold enables efficient integration on the slow time scale and significantly improves prediction accuracy beyond the training data. We demonstrate the FSNN on several examples that exhibit multiple timescales, including the Grad moment system from hydrodynamics, two-scale Lorentz96 equations for modeling atmospheric dynamics, and Abraham-Lorentz dynamics modeling radiation reaction of electrons in a magnetic field.
Steerable current-driven emission of spin waves in magnetic vortex pairs

The efficient excitation of spin waves is a key challenge in the realization of magnonic devices. We demonstrate the current-driven generation of spin waves in antiferromagnetically coupled magnetic vortices. We employ time-resolved scanning transmission X-ray microscopy (TR-STXM) to directly image the emission of spin waves upon the application of an alternating current flowing directly through the magnetic stack. Micromagnetic simulations allow us to identify the origin of the excitation to be the current-driven Oersted field, which in the present system proves to be orders of magnitude more efficient than the commonly used excitation via stripline antennas. Our numerical studies also reveal that the spin-transfer torque can lead to the emission of spin waves as well, yet only at much higher current amplitudes. By using magnetostrictive materials, we futhermore demonstrate that the direction of the magnon propagation can be steered by increasing the excitation amplitude, which modifies the underlying magnetization profile through an additional anisotropy in the magnetic layers. The demonstrated methods allow for the efficient and tunable excitation of spin waves, marking a significant advance in the generation and control of spin waves in magnonic devices.
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data

Variational Autoencoders (VAEs) have gained significant popularity among researchers as a powerful tool for understanding unknown distributions based on limited samples. This popularity stems partly from their impressive performance and partly from their ability to provide meaningful feature representations in the latent space. Wasserstein Autoencoders (WAEs), a variant of VAEs, aim to not only improve model efficiency but also interpretability. However, there has been limited focus on analyzing their statistical guarantees. The matter is further complicated by the fact that the data distributions to which WAEs are applied - such as natural images - are often presumed to possess an underlying low-dimensional structure within a high-dimensional feature space, which current theory does not adequately account for, rendering known bounds inefficient. To bridge the gap between the theory and practice of WAEs, in this paper, we show that WAEs can learn the data distributions when the network architectures are properly chosen. We show that the convergence rates of the expected excess risk in the number of samples for WAEs are independent of the high feature dimension, instead relying only on the intrinsic dimension of the data distribution.
Three‐Dimensional Effects of Artificial Mixing in a Shallow Drinking‐Water Reservoir

Studies that examine the effects of artificial mixing for water‐quality mitigation in lakes and reservoirs often view a water column with a one‐dimensional (1‐D) perspective (e.g., homogenized epilimnetic and hypolimnetic layers). Artificial mixing in natural water bodies, however, is inherently three dimensional (3‐D). Using a 3‐D approach experimentally and numerically, the present study visualizes thermal structure and analyzes constituent transport under the influence of artificial mixing in a shallow drinking‐water reservoir. The purpose is to improve the understanding of artificial mixing, which may help to better design and operate mixing systems. In this reservoir, a side‐stream supersaturation (SSS) hypolimnetic oxygenation system and an epilimnetic bubble‐plume mixing (EM) system were concurrently deployed in the deep region. The present study found that, while the mixing induced by the SSS system does not have a distinct 3‐D effect on the thermal structure, epilimnetic mixing by the EM system causes 3‐D heterogeneity. In the experiments, epilimnetic mixing deepened the lower metalimnetic boundary near the diffuser by about 1 m, with 55% reduction of the deepening rate at 120 m upstream of the diffuser. In a tracer study using a 3‐D hydrodynamic model, the operational flow rate of the EM system is found to be an important short‐term driver of constituent transport in the reservoir, whereas the duration of the EM system operation is the dominant long‐term driver. The results suggest that artificial mixing substantially alters both 3‐D thermal structure and constituent transport, and thus needs to be taken into account for reservoir management.
Stabilizing remote entanglement via waveguide dissipation

Distributing entanglement between remote sites is integral to quantum networks. Here, we demonstrate the autonomous stabilization of remote entanglement between a pair of non-interacting superconducting qubits connected by an open waveguide on a chip. In this setting, the interplay between a classical continuous drive - supplied through the waveguide - and dissipation into the waveguide stabilizes the qubit pair in a dark state, which, asymptotically, takes the form of a Bell state. We use field-quadrature measurements of the photons emitted to the waveguide to perform quantum state tomography on the stabilized states, where we find a concurrence of $0.504^{+0.007}_{-0.029}$ in the optimal setting with a stabilization time constant of 56 $\pm$ 4 ns. We examine the imperfections within our system and discuss avenues for enhancing fidelities and achieving scalability in future work. The decoherence-protected, steady-state remote entanglement offered via dissipative stabilization may find applications in distributed quantum computing, sensing, and communication.
Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization

We develop and analyze stochastic variants of ISTA and a full backtracking FISTA algorithms [Beck and Teboulle, 2009, Scheinberg et al., 2014] for composite optimization without the assumption that stochastic gradient is an unbiased estimator. This work extends analysis of inexact fixed step ISTA/FISTA in [Schmidt et al., 2011] to the case of stochastic gradient estimates and adaptive step-size parameter chosen by backtracking. It also extends the framework for analyzing stochastic line-search method in [Cartis and Scheinberg, 2018] to the proximal gradient framework as well as to the accelerated first order methods.
Middle Paleolithic subsistence in the central Rhine Valley

Les fouilles archeologiques dans le cours moyen de la vallee du Rhin temoignent de pratiques de subsistance diversifiees au cours du Paleolithique moyen. La strategie de notre recherche vise de longues sequences a sedimentation fine qui permettent de recuperer une importante information contextuelle et fournissent d'excellentes perspectives pour les reconstructions paleo-environnementales. A ce jour, des exemples d'exploitation monospecifique du cheval et des bovides sont connus a Tonchesberg et Wallertheim, et des exemples d'assemblages fauniques diversifies avec bovides, equides et cervides bien representes ont ete documentes a Tonchesberg. Les fouilles en cours a Wallertheim sont extremement prometteuses pour reconstituer la subsistance au Paleolithique moyen mais il faut attendre la fin des fouilles pour obtenir des resultats.
Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning

Recent advancements in privacy-preserving machine learning are paving the way to extend the benefits of ML to highly sensitive data that, until now, have been hard to utilize due to privacy concerns and regulatory constraints. Simultaneously, there is a growing emphasis on enhancing the transparency and accountability of machine learning, including the ability to audit ML deployments. While ML auditing and PPML have both been the subjects of intensive research, they have predominately been examined in isolation. However, their combination is becoming increasingly important. In this work, we introduce Arc, an MPC framework for auditing privacy-preserving machine learning. At the core of our framework is a new protocol for efficiently verifying MPC inputs against succinct commitments at scale. We evaluate the performance of our framework when instantiated with our consistency protocol and compare it to hashing-based and homomorphic-commitment-based approaches, demonstrating that it is up to 10^4x faster and up to 10^6x more concise.
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models

Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters. Our approach extends this concept by aggregating"knowledge"derived from models, instead of model parameters. We present a novel framework called CoDream, where clients collaboratively optimize randomly initialized data using federated optimization in the input data space, similar to how randomly initialized model parameters are optimized in FL. Our key insight is that jointly optimizing this data can effectively capture the properties of the global data distribution. Sharing knowledge in data space offers numerous benefits: (1) model-agnostic collaborative learning, i.e., different clients can have different model architectures; (2) communication that is independent of the model size, eliminating scalability concerns with model parameters; (3) compatibility with secure aggregation, thus preserving the privacy benefits of federated learning; (4) allowing of adaptive optimization of knowledge shared for personalized learning. We empirically validate CoDream on standard FL tasks, demonstrating competitive performance despite not sharing model parameters. Our code: https://mitmedialab.github. io/codream.github. io/