Miracle-dz/UniFAR-FTUs huggingface dataset:
Anticrossing of a plasmonic nanoresonator mode and a single quantum dot at room temperature

Room-temperature strong coupling of a single quantum emitter and a single resonant plasmonic mode is a key resource for quantum information processing and quantum sensing at ambient conditions. To beat dephasing, ultrafast energy transfer is achieved by coupling single emitters to a plasmonic nanoresonator with an extremely small mode volume and optimal spectral overlap. Typically, normal mode splittings in luminescence spectra of single-emitter strongly-coupled systems are provided as evidence for strong coupling and to obtain rough estimates of the light-matter coupling strength g. However, a complete anticrossing of a single emitter and a cavity mode as well as the characterization of the uncoupled constituents is usually hard to achieve. Here, we exploit the light-induced oxygen-dependent blue-shift of individual CdSe/ZnS semiconductor quantum dots to tune their transition energy across the resonance of a scanning plasmonic slit resonator after characterizing both single emitter and nano resonator in their uncoupled states. Our results provide clear proof of single-emitter strong light-matter coupling at ambient condition as well as a value for the Rabi splitting at zero detuning 100 meV, consistent with modeling, thereby opening the path towards plexitonic devices that exploit single-photon nonlinearities at ambient conditions.
A surrogate by exchangeability approach to the Curie-Weiss model

We investigate the Curie-Weiss model by means of an explicit construction of its de Finetti measure of exchangeability. Writing the magnetisation as a sum of i.i. d. 's randomised by the underlying De Finetti random variable, we show that the apparition of a phase transition can be understood as a competition between these two sources of randomness, the Gaussian regime corresponding to a marginally relevant disordered system. The study is performed by introducing a surrogate random variable that contains all the appropriate information on the model and which is simpler to study.
State Constrained Stochastic Optimal Control for Continuous and Hybrid Dynamical Systems Using DFBSDE

We develop a computationally efficient learning-based forward-backward stochastic differential equations (FBSDE) controller for both continuous and hybrid dynamical (HD) systems subject to stochastic noise and state constraints. Solutions to stochastic optimal control (SOC) problems satisfy the Hamilton-Jacobi-Bellman (HJB) equation. Using current FBSDE-based solutions, the optimal control can be obtained from the HJB equations using deep neural networks (e.g., long short-term memory (LSTM) networks). To ensure the learned controller respects the constraint boundaries, we enforce the state constraints using a soft penalty function. In addition to previous works, we adapt the deep FBSDE (DFBSDE) control framework to handle HD systems consisting of continuous dynamics and a deterministic discrete state change. We demonstrate our proposed algorithm in simulation on a continuous nonlinear system (cart-pole) and a hybrid nonlinear system (five-link biped).
Response of a half-car model with optimal magnetorheological damper parameters

In this paper, control of the stationary response of a half-car model with a magnetorheological (MR) damper moving over a random road is considered. The MR damper is characterized using Bingham and modified Bouc–Wen models whose parameters are determined optimally using a multi-objective optimization technique and nondominated sorting genetic algorithm II. The multi-objective optimization problem is solved by minimizing the difference between the root mean square sprung mass acceleration, the sum of the front and rear suspension strokes and the sum of the front and rear road holding response of the half-car model with the MR damper, and those of an active suspension system based on linear quadratic regulator (LQR) control. The control force of the MR damper suspension is constrained to lie within ±5% of the control force corresponding to the active suspension system based on LQR control. It is observed that the MR damper suspension systems with optimal parameters perform an order of magnitude better than the passive suspension and perform as well as active suspensions with limited state feedback, and closer to the performance of fully active suspensions. In the case of MR damper suspensions, the vehicle response statistics are obtained using the equivalent linearization method and verified by Monte Carlo simulation.
Speranza: Usable, Privacy-friendly Software Signing

Software repositories, used for wide-scale open software distribution, are a significant vector for security attacks. Software signing provides authenticity, mitigating many such attacks. Developer-managed signing keys pose usability challenges, but certificate-based systems introduce privacy problems. This work, Speranza, uses certificates to verify software authenticity but still provides anonymity to signers using zero-knowledge identity co-commitments. In Speranza, a signer uses an automated certificate authority (CA) to create a private identity-bound signature and proof of authorization. Verifiers check that a signer was authorized to publish a package without learning the signer's identity. The package repository privately records each package's authorized signers, but publishes only commitments to identities in a public map. Then, when issuing certificates, the CA issues the certificate to a distinct commitment to the same identity. The signer then creates a zero-knowledge proof that these are co-commitments. We implemented a proof-of-concept for Speranza. We find that costs to maintainers (signing) and end users (verifying) are small (sub-millisecond), even for a repository with millions of packages. Techniques inspired by recent key transparency systems reduce the bandwidth for serving authorization policies to 2 KiB. Server costs in this system are negligible. Our evaluation finds that Speranza is practical on the scale of the largest software repositories. We also emphasize practicality and deployability in this project. By building on existing technology and employing relatively simple and well-established cryptographic techniques, Speranza can be deployed for wide-scale use with only a few hundred lines of code and minimal changes to existing infrastructure. Speranza is a practical way to bring privacy and authenticity together for more trustworthy open-source software.
Enhanced optomechanical interaction in the unbalanced interferometer

Quantum optomechanical systems enable the study of fundamental questions on quantum nature of massive objects. For that a strong coupling between light and mechanical motion is required, which presents a challenge for massive objects. In particular large interferometric sensors with low frequency oscillators are difficult to bring into quantum regime. Here we propose a modification of the Michelson-Sagnac interferometer, which allows to boost the optomechanical coupling strength. This is done by unbalancing the central beam-splitter of the interferometer, allowing to balance two types of optomechanical coupling present in the system: dissipative and dispersive. We analyse two different configurations, when the optomechanical cavity is formed by the mirror for the laser pump field (power-recycling), and by the mirror for the signal field (signal-recycling). We show that the imbalance of the beam splitter allows to dramatically increase the optical cooling of the test mass motion. We also formulate the conditions for observing quantum radiation-pressure noise and ponderomotive squeezing. Our configuration can serve as the basis for more complex modifications of the interferometer that would utilize the enhanced coupling strength. This will allow to efficiently reach quantum state of large test masses, opening the way to studying fundamental aspects of quantum mechanics and experimental search for quantum gravity.
ParamNet: A Parameter-variable Network for Fast Stain Normalization

In practice, digital pathology images are often affected by various factors, resulting in very large differences in color and brightness. Stain normalization can effectively reduce the differences in color and brightness of digital pathology images, thus improving the performance of computer-aided diagnostic systems. Conventional stain normalization methods rely on one or several reference images, but one or several images are difficult to represent the entire dataset. Although learning-based stain normalization methods are a general approach, they use complex deep networks, which not only greatly reduce computational efficiency, but also risk introducing artifacts. StainNet is a fast and robust stain normalization network, but it has not a sufficient capability for complex stain normalization due to its too simple network structure. In this study, we proposed a parameter-variable stain normalization network, ParamNet. ParamNet contains a parameter prediction sub-network and a color mapping sub-network, where the parameter prediction sub-network can automatically determine the appropriate parameters for the color mapping sub-network according to each input image. The feature of parameter variable ensures that our network has a sufficient capability for various stain normalization tasks. The color mapping sub-network is a fully 1x1 convolutional network with a total of 59 variable parameters, which allows our network to be extremely computationally efficient and does not introduce artifacts. The results on cytopathology and histopathology datasets show that our ParamNet outperforms state-of-the-art methods and can effectively improve the generalization of classifiers on pathology diagnosis tasks. The code has been available at https://github.com/khtao/ParamNet.
Carroll/fracton particles and their duality

We exploit the close relationship between Carroll and fracton symmetries, together with the method of coadjoint orbits, to define and classify classical Carroll and fracton particles. This approach establishes a Carroll/fracton duality and provides an answer to the question “What is a fracton? ”. Under this duality, carrollian energy and center-of-mass correspond to the fracton electric charge and dipole moment, respectively. Then immobile massive Carroll particles correspond to the fracton monopoles, whereas certain mobile Carroll particles (“centrons”) correspond to fracton elementary dipoles. We uncover various new massless carrollian/neutral fractonic particles, provide an action in each case and relate them via a GL(2,R) symmetry. We also comment on the limit from Poincaré particles, the relation to (electric and magnetic) Carroll field theories, contrast Carroll boosts with dipole transformations and highlight a generalisation to curved space ((A)dS Carroll). 1ORCID: 0000-0002-9308-9360 2ORCID: 0000-0003-0989-9959 3ORCID: 0000-0002-3925-3983 ar X iv :2 30 5. 06 73 0v 1 [ he pth ] 1 1 M ay 2 02 3
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications

This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation. In Part 2, this theory is further demonstrated by elaborating detailed methods of designing an electricity spot market (ESM), together with a reserved capacity product (RC) in the ancillary service market (ASM) and a virtual bidding (VB) product in the financial market (FM). Following the theory proposed in Part 1, firstly, market design options in the joint market are specified. Then, the Markov game model is developed, in which we show how to incorporate market design options and uncertain risks in model formulation. A multi-agent policy proximal optimization (MAPPO) algorithm is elaborated, as a practical implementation of the generalized market simulation method developed in Part 1. Finally, the case study demonstrates how to pick the best market design options by using some of the market operation performance indicators proposed in Part 1, based on the simulation results generated by implementing the MAPPO algorithm. The impacts of different market design options on market participants' bidding strategy preference are also discussed.
Nonlinear vibration absorber with pinched hysteresis: Theory and experiments

AbstractA nonlinear vibration absorber exploiting the pinched hysteresis of mixed wire ropes made of NiTiNOL and steel is proposed. The mixed wire ropes are assembled in a mechanical device which, by bending them, provides the restoring force to the oscillating mass. The assembly of mixed wire ropes, subject to cyclic end displacements, gives rise to a pinched force-displacement behavior due to the simultaneous occurrence of interwire friction and phase transformations. A modified Bouc-Wen model is adopted to represent the pinched hysteresis while the differential evolutionary (DE) algorithm is employed to identify the constitutive parameters that reproduce the experimental force-displacement cycles. The DE algorithm is also utilized to optimize the restoring force of the absorber toward mitigation of the dynamic response of the main structure to external disturbances of various magnitudes. The pinched hysteresis provides an equivalent damping ratio and resonance frequency, which tend to become almost con.
A Machine Learning Approach to Improving Timing Consistency between Global Route and Detailed Route

Due to the unavailability of routing information in design stages prior to detailed routing (DR), the tasks of timing prediction and optimization pose major challenges. Inaccurate timing prediction wastes design effort, hurts circuit performance, and may lead to design failure. This work focuses on timing prediction after clock tree synthesis and placement legalization, which is the earliest opportunity to time and optimize a “complete” netlist. The article first documents that having “oracle knowledge” of the final post-DR parasitics enables post-global routing (GR) optimization to produce improved final timing outcomes. To bridge the gap between GR-based parasitic and timing estimation and post-DR results during post-GR optimization, machine learning (ML)-based models are proposed, including the use of features for macro blockages for accurate predictions for designs with macros. Based on a set of experimental evaluations, it is demonstrated that these models show higher accuracy than GR-based timing estimation. When used during post-GR optimization, the ML-based models show demonstrable improvements in post-DR circuit performance. The methodology is applied to two different tool flows—OpenROAD and a commercial tool flow—and results on an open-source 45nm bulk and a commercial 12nm FinFET enablement show improvements in post-DR timing slack metrics without increasing congestion. The models are demonstrated to be generalizable to designs generated under different clock period constraints and are robust to training data with small levels of noise.
Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models

In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series shifted in time relative to one another, and which can be leveraged for the purposes of control, forecasting or clustering. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models. Within our framework, the envisioned pipeline takes as input a set of time series, and creates an enlarged universe of extracted subsequence time series from each input time series, via a sliding window approach. This is then followed by an application of various clustering techniques, (such as k-means++ and spectral clustering), employing a variety of pairwise similarity measures, including nonlinear ones. Once the clusters have been extracted, lead-lag estimates across clusters are robustly aggregated to enhance the identification of the consistent relationships in the original universe. We establish connections to the multireference alignment problem for both the homogeneous and heterogeneous settings. Since multivariate time series are ubiquitous in a wide range of domains, we demonstrate that our method is not only able to robustly detect lead-lag relationships in financial markets, but can also yield insightful results when applied to an environmental data set.
Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning

Adaptive learning is an area of educational technology that consists in delivering personalized learning experiences to address the unique needs of each learner. An important subfield of adaptive learning is learning path personalization: it aims at designing systems that recommend sequences of educational activities to maximize students' learning outcomes. Many machine learning approaches have already demonstrated significant results in a variety of contexts related to learning path personalization. However, most of them were designed for very specific settings and are not very reusable. This is accentuated by the fact that they often rely on non-scalable models, which are unable to integrate new elements after being trained on a specific set of educational resources. In this paper, we introduce a flexible and scalable approach towards the problem of learning path personalization, which we formalize as a reinforcement learning problem. Our model is a sequential recommender system based on a graph neural network, which we evaluate on a population of simulated learners. Our results demonstrate that it can learn to make good recommendations in the small-data regime.
Pyramid Texture Filtering

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation---the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement. Code is available at https://rewindl.github. io/pyramid_texture_filtering/.
A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges

The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions of fairness and mitigation. Finally, we identify the key challenges and provide researchers with guidelines for future directions.
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks

One of the central questions in the theory of deep learning is to understand how neural networks learn hierarchical features. The ability of deep networks to extract salient features is crucial to both their outstanding generalization ability and the modern deep learning paradigm of pretraining and finetuneing. However, this feature learning process remains poorly understood from a theoretical perspective, with existing analyses largely restricted to two-layer networks. In this work we show that three-layer neural networks have provably richer feature learning capabilities than two-layer networks. We analyze the features learned by a three-layer network trained with layer-wise gradient descent, and present a general purpose theorem which upper bounds the sample complexity and width needed to achieve low test error when the target has specific hierarchical structure. We instantiate our framework in specific statistical learning settings -- single-index models and functions of quadratic features -- and show that in the latter setting three-layer networks obtain a sample complexity improvement over all existing guarantees for two-layer networks. Crucially, this sample complexity improvement relies on the ability of three-layer networks to efficiently learn nonlinear features. We then establish a concrete optimization-based depth separation by constructing a function which is efficiently learnable via gradient descent on a three-layer network, yet cannot be learned efficiently by a two-layer network. Our work makes progress towards understanding the provable benefit of three-layer neural networks over two-layer networks in the feature learning regime.
Merger Conditions of Population III Protostar Binaries

Massive close binary stars with extremely small separations have been observed, and they are possible progenitors of gravitational-wave sources. The evolution of massive binaries in the protostellar accretion stage is key to understanding their formation process. We, therefore, investigate how close the protostars, consisting of a high-density core and a vast low-density envelope, can approach each other but not coalesce. To investigate the coalescence conditions, we conduct smoothed particle hydrodynamics simulations following the evolution of equal-mass binaries with different initial separations. Since Population (Pop) I and III protostars have similar interior structures, we adopt a specific Pop III model with the mass and radius of 7.75 M ⊙ and 61.1 R ⊙ obtained by the stellar evolution calculations. Our results show that the binary separation decreases due to the transport of the orbital angular momentum to spin angular momentum. If the initial separation is less than about 80% of the sum of the protostellar radius, the binary coalesces in a time shorter than the tidal lock timescale. The mass loss up to the merging is ≲3%. After coalescence, the star rotates rapidly, and its interior structure is independent of the initial separation. We conclude that there must be some orbital shrinking mechanism after the protostars contract to enter the zero-age main-sequence stage.