Miracle-dz/UniFAR-FTUs huggingface dataset:
Reward Imputation with Sketching for Contextual Batched Bandits

Contextual batched bandit (CBB) is a setting where a batch of rewards is observed from the environment at the end of each episode, but the rewards of the non-executed actions are unobserved, resulting in partial-information feedback. Existing approaches for CBB often ignore the rewards of the non-executed actions, leading to underutilization of feedback information. In this paper, we propose an efficient approach called Sketched Policy Updating with Imputed Rewards (SPUIR) that completes the unobserved rewards using sketching, which approximates the full-information feedbacks. We formulate reward imputation as an imputation regularized ridge regression problem that captures the feedback mechanisms of both executed and non-executed actions. To reduce time complexity, we solve the regression problem using randomized sketching. We prove that our approach achieves an instantaneous regret with controllable bias and smaller variance than approaches without reward imputation. Furthermore, our approach enjoys a sublinear regret bound against the optimal policy. We also present two extensions, a rate-scheduled version and a version for nonlinear rewards, making our approach more practical. Experimental results show that SPUIR outperforms state-of-the-art baselines on synthetic, public benchmark, and real-world datasets.
Better Approximation for Interdependent SOS Valuations

Submodular over signal (SOS) defines a family of interesting functions for which there exist truthful mechanisms with constant approximation to the social welfare for agents with interdependent valuations. The best-known truthful auction is of $4$-approximation and a lower bound of 2 was proved. We propose a new and simple truthful mechanism to achieve an approximation ratio of 3.315.
Nuclear spin squeezing via electric quadrupole interaction

Control over nuclear spin fluctuations is essential for processes that rely on preserving the quantum state of an embedded system. For this purpose, squeezing is a viable alternative, so far that has not been properly exploited for the nuclear spins. Of particular relevance in solids is the electric quadrupole interaction (QI), which operates on nuclei having spin higher than 1/2. In its general form, QI involves an electric field gradient (EFG) biaxiality term. Here, we show that as this EFG biaxiality increases, it enables continuous tuning of single-particle squeezing from the one-axis twisting to the two-axis countertwisting limits. A detailed analysis of QI squeezing is provided, exhibiting the intricate consequences of EFG biaxiality. The initial states over the Bloch sphere are mapped out to identify those favorable for fast initial squeezing, or for prolonged squeezings. Furthermore, the evolution of squeezing in the presence of a phase-damping channel and an external magnetic field are investigated. We observe that dephasing drives toward an anti-squeezed terminal state, the degree of which increases with the spin angular momentum. Finally, QI squeezing in the limiting case of a two-dimensional EFG with a perpendicular magnetic field is discussed, which is of importance for two-dimensional materials, and the associated beat patterns in squeezing are revealed.
Robust offset‐free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models

This paper presents a robust model predictive control (MPC) scheme that provides offset‐free setpoint tracking for systems described by neural nonlinear autoregressive exogenous (NNARX) models. To this end, a NNARX model that learns the dynamics of the plant from input‐output data is augmented with an explicit integral action on the output tracking error. A robust tube‐based MPC is finally designed, leveraging the unique structure of the model, to ensure robust offset‐free convergence to constant reference signals even in case of plant‐model mismatch. Numerical simulations on a water heating system show the effectiveness of the proposed control algorithm.
Hybrid Quantum-Classical General Benders Decomposition Algorithm for Unit Commitment with Multiple Networked Microgrids

—Unit commitment with multiple networked microgrids (UCMNM) is a typical mixed-integer nonlinear programming problem. It requires coordination between the local utility grid controlled by the distribution system operator and the microgrids controlled by the microgrid central controllers. Generalized Benders decomposition algorithm (GBDA) can effectively deal with the mixed integer problems, and we introduce quantum computing in this framework and propose a hybrid distributed decomposition algorithm in this work, named as hybrid quantum-classical generalized Benders decomposition algorithm (HQC-GBDA). For privacy-preserving and independent decision-making, HQC-GBDA decomposes the UCMNM problem into a master problem and a series of sub-problems. The NP-Hard master problem with discrete variables can be transformed into the quadratic unconstrained binary optimization (QUBO) problem, which can be settled by the quantum annealing algorithm. The main contributions of this work include: 1) Based on GBDA, we propose a multi-cut generalized Benders decomposition algorithm (MC-GBDA), which can reduce the number of iterations by adding the Benders feasibility cutting planes to the MP more efficiently; 2) In HQC-GBDA, we reconstruct the NP-Hard master problem into the QUBO problem, which is suitable to be solved by quantum computing and further reduce the complexity of the master problem, and thus improve the performance; 3) We use the D-WAVE quantum annealing machine to solve the QUBO problem of HQC-GBDA and find that HQC-GBDA is faster than its classical counterpart MC-GBDA when dealing with more complex UCMNM problems.
On dualities for non-Abelian gauge theories with continuous center

Formulating gauge theories for gauge groups admitting a continuous center can require to include charged scalars to dene a gauge-coupling function. We show that the gauge-elds in the center can be dualized into form-elds of dimension-dependent degree. The resulting theory admits a smaller gauge group that factorizes out the center, but contains a Chern-Simons type term coupling the scalars to the form-elds. As an explicit example we consider the gauge group being the Heisenberg group and show that the dual action only admits an Abelian gauge symmetry. We comment on the vacuum symmetries in these settings, their supersymmetrization, and point out their importance in string theory.
Optimal Control of Material Micro-Structures

In this paper, we consider the optimal control of material micro-structures. Such material micro-structures are modeled by the so-called phase field model. We study the underlying physical structure of the model and propose a data based approach for its optimal control, along with a comparison to the control using a state of the art Reinforcement Learning (RL) algorithm. Simulation results show the feasibility of optimally controlling such micro-structures to attain desired material properties and complex target micro-structures.
Probabilistic Integration of Object Level Annotations in Chest X-ray Classification

Medical image datasets and their annotations are not growing as fast as their equivalents in the general domain. This makes translation from the newest, more data-intensive methods that have made a large impact on the vision field increasingly more difficult and less efficient. In this paper, we propose a new probabilistic latent variable model for disease classification in chest X-ray images. Specifically we consider chest X-ray datasets that contain global disease labels, and for a smaller subset contain object level expert annotations in the form of eye gaze patterns and disease bounding boxes. We propose a two-stage optimization algorithm which is able to handle these different label granularities through a single training pipeline in a two-stage manner. In our pipeline global dataset features are learned in the lower level layers of the model. The specific details and nuances in the fine-grained expert object-level annotations are learned in the final layers of the model using a knowledge distillation method inspired by conditional variational inference. Subsequently, model weights are frozen to guide this learning process and prevent overfitting on the smaller richly annotated data subsets. The proposed method yields consistent classification improvement across different back-bones on the common benchmark datasets Chest X-ray14 and MIMIC-CXR. This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.
Lepton number violating four-body tau lepton decays

We study the four-body ${\ensuremath{\tau}}^{\ifmmode\pm\else\textpm\fi{}}\ensuremath{\rightarrow}{\ensuremath{\nu}}_{\ensuremath{\tau}}{l}^{\ifmmode\pm\else\textpm\fi{}}{l}^{\ifmmode\pm\else\textpm\fi{}}{X}^{\ensuremath{\mp}}$ decays where $l=e$ or $\ensuremath{\mu}$ and $X=\ensuremath{\pi}$, $K$, $\ensuremath{\rho}$, and ${K}^{*}$ mesons. These decay processes violate the total lepton number ($|\ensuremath{\Delta}L|=2$) and can be induced by the exchange of Majorana neutrinos. We consider a scenario where these decays are dominated by the exchange of only one heavy neutrino, which produces an enhancement of the decay amplitude via the resonant mechanism. Searches for these novel decay channels with branching fractions sensitivities of $O({10}^{\ensuremath{-}7})$ can provide constraints on the parameter space of the Majorana neutrinos, which are stronger than the ones obtained from $\ensuremath{\Delta}L=2$ decays of charged pseudoscalar mesons.
CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation

Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. Cross-lingual NER has been proposed to alleviate this issue by transferring knowledge from high-resource languages to low-resource languages via aligned cross-lingual representations or machine translation results. However, the performance of cross-lingual NER methods is severely affected by the unsatisfactory quality of translation or label projection. To address these problems, we propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER with the help of a multilingual labeled sequence translation model. Specifically, the target sequence is first translated into the source language and then tagged by a source NER model. We further adopt a labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence. Ultimately, the whole pipeline is integrated into an end-to-end model by the way of self-training. Experimental results on two benchmarks demonstrate that our method substantially outperforms the previous strong baseline by a large margin of +3~7 F1 scores and achieves state-of-the-art performance.
An α-regret analysis of Adversarial Bilateral Trade

We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (i.e., determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a mechanism that maximizes efficiency (or gain from trade) while being incentive compatible, individually rational and budget balanced. In this paper we consider gain from trade which is harder to approximate than social welfare. We consider a variety of feedback scenarios and distinguish the cases where the mechanism posts one price and when it can post different prices for buyer and seller. We show several surprising results about the separation between the different scenarios. In particular we show that (a) it is impossible to achieve sublinear $\alpha$-regret for any $\alpha<2$, (b) but with full feedback sublinear $2$-regret is achievable (c) with a single price and partial feedback one cannot get sublinear $\alpha$ regret for any constant $\alpha$ (d) nevertheless, posting two prices even with one-bit feedback achieves sublinear $2$-regret, and (e) there is a provable separation in the $2$-regret bounds between full and partial feedback.
Design and characterization of cochlea-inspired tonotopic resonators

The cochlea has long been the subject of investigation in various research fields due to its intriguing spiral architecture and unique sensing characteristics. One of its most interesting features is the ability to sense acoustic waves at different spatial locations, based on their frequency content. In this work, we propose a novel design for a tonotopic resonator, based on a cochlea-inspired spiral. The resulting structure was subjected to an optimization process to exhibit out-of-plane vibration modes with mean out-of-plane displacement maxima distributed along its centerline spanning nearly a two-decade frequency range. Numerical simulations are performed to demonstrate the concept, which is also confirmed experimentally on a 3D printed structure. The obtained frequency-dependent distribution is shown to be a viable source of information for the discrimination of signals with various frequency components. The harnessed tonotopic features can be used as a fundamental principle to design structures with applications in areas such as non-destructive testing and vibration attenuation.
Deep Clustering With Consensus Representations

The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clustering method. Most existing deep clustering methods are designed for a single clustering method, e.g., k-means, spectral clustering, or Gaussian mixture models, but it is well known that no clustering algorithm works best in all circumstances. Consensus clustering tries to alleviate the individual weaknesses of clustering algorithms by building a consensus between members of a clustering ensemble. Currently, there is no deep clustering method that can include multiple heterogeneous clustering algorithms in an ensemble to update representations and clusterings together. To close this gap, we introduce the idea of a consensus representation that maximizes the agreement between ensemble members. Further, we propose DECCS (Deep Embedded Clustering with Consensus representationS), a deep consensus clustering method that learns a consensus representation by enhancing the embedded space to such a degree that all ensemble members agree on a common clustering result. Our contributions are the following: (1) We introduce the idea of learning consensus representations for heterogeneous clusterings, a novel notion to approach consensus clustering. (2) We propose DECCS, the first deep clustering method that jointly improves the representation and clustering results of multiple heterogeneous clustering algorithms. (3) We show in experiments that learning a consensus representation with DECCS is outperforming several relevant baselines from deep clustering and consensus clustering.
Electron-mediated entanglement of two distant macroscopic ferromagnets within a nonequilibrium spintronic device

Using the nascent concept of quantum spin-transfer torque [A. Zholud et al., Phys. Rev. Lett. {\bf 119}, 257201 (2017); M. D. Petrovi\'{c} {\em et al.}, Phys. Rev. X {\bf 11}, 021062 (2021)], we demonstrate that a current pulse can be harnessed to entangle quantum localized spins of two spatially separated ferromagnets (FMs) which are initially unentangled. The envisaged setup comprises a spin-polarizer (FM$_p$) and a spin-analyzer (FM$_a$) FM layers separated by normal metal (NM) spacer. The injection of a current pulse into the device leads to a time-dependent superposition of many-body states characterized by a high degree of entanglement between the spin degrees of freedom of the two distant FM layers. The non-equilibrium dynamics are due to the transfer of spin angular momentum from itinerant electrons to the localized spins via a quantum spin-torque mechanism that remains active even for {\em collinear but antiparallel} arrangements of the FM$_p$ and FM$_a$ magnetizations (a situation in which the conventional spin-torque is absent). We quantify the mixed-state entanglement generated between the FM layers by tracking the time-evolution of the full density matrix and analyzing the build-up of the mutual logarithmic negativity over time. The effect of decoherence and dissipation in the FM layers due to coupling to bosonic baths at finite temperature, the use of multi-electron current pulses and the dependence on the number of spins are also considered in an effort to ascertain the robustness of our predictions under realistic conditions. Finally, we propose a ``current-pump/X-ray-probe'' scheme, utilizing ultrafast X-ray spectroscopy, that can witness nonequilibrium and transient entanglement of the FM layers by extracting its time-dependent quantum Fisher information.
Trajectory Optimization for Quadruped Mobile Manipulators that Carry Heavy Payload

This paper presents a simplified model-based trajectory optimization (TO) formulation for motion planning on quadruped mobile manipulators that carry heavy payload of known mass. The proposed payload-aware formulation simultaneously plans locomotion, payload manipulation and considers both robot and payload model dynamics while remaining computationally efficient. At the presence of heavy payload, the approach exhibits reduced leg outstretching (thus increased manipulability) in kinematically demanding motions due to the contribution of payload manipulation in the optimization. The framework's computational efficiency and performance is validated through a number of simulation and experimental studies with the bi-manual quadruped CENTAURO robot carrying on its arms a payload that exceeds 15 % of its mass and traversing non-flat terrain.
Simulating Ride-Pooling Services with Pre-Booking and On-Demand Customers

If private vehicle trips can be replaced, ride-pooling services can decrease parking space needed by higher vehicle utilization and increase traffic efficiency by increasing vehicle occupancy. Nevertheless, substantial benefits can only be achieved if a certain market penetration is passed to find enough shareable rides for pooling to take place. Additionally, because of their highly dynamic and stochastic nature on-demand ride-pooling services cannot always guarantee that a request is served. Allowing customers to pre-book their trip in advance could provide benefits for both aspects. Additional knowledge helps an operator to better plan vehicle schedules to improve service efficiency while an accepted trip or a rejection can be communicated early on to the customer. This study presents a simulation framework where a ride-pooling provider offers a service in mixed operation: Customers can either use the service on-demand or pre-book trips. A graph-based batch optimization formulation is proposed to create offline schedules for pre-booking customers. Using two rolling horizons, this offline solution is forwarded to an online optimization for on-demand and pre-booking customers simultaneously. The framework is tested in a case study for Manhattan, NYC. That the graph-based batch optimization is superior to a basic insertion method in terms of solution quality and run-time. Due to additional knowledge, the ride-pooling operator can improve the solution quality significantly by serving more customers while pooling efficiency can be increased. Additionally, customers have shorter waiting and detour times the more customers book a trip in advance.
Differentially Private Online-to-Batch for Smooth Losses

We develop a new reduction that converts any online convex optimization algorithm suffering $O(\sqrt{T})$ regret into an $\epsilon$-differentially private stochastic convex optimization algorithm with the optimal convergence rate $\tilde O(1/\sqrt{T} + \sqrt{d}/\epsilon T)$ on smooth losses in linear time, forming a direct analogy to the classical non-private"online-to-batch"conversion. By applying our techniques to more advanced adaptive online algorithms, we produce adaptive differentially private counterparts whose convergence rates depend on apriori unknown variances or parameter norms.
Robust Action Segmentation from Timestamp Supervision

Action segmentation is the task of predicting an action label for each frame of an untrimmed video. As obtaining annotations to train an approach for action segmentation in a fully supervised way is expensive, various approaches have been proposed to train action segmentation models using different forms of weak supervision, e.g., action transcripts, action sets, or more recently timestamps. Timestamp supervision is a promising type of weak supervision as obtaining one timestamp per action is less expensive than annotating all frames, but it provides more information than other forms of weak supervision. However, previous works assume that every action instance is annotated with a timestamp, which is a restrictive assumption since it assumes that annotators do not miss any action. In this work, we relax this restrictive assumption and take missing annotations for some action instances into account. We show that our approach is more robust to missing annotations compared to other approaches and various baselines.
Tensor Hypercontraction Form of the Perturbative Triples Energy in Coupled-Cluster Theory

We present the working equations for a reduced-scaling method of evaluating the perturbative triples (T) energy in coupled-cluster theory, through the tensor hypercontraction (THC) of the triples amplitudes (tijkabc). Through our method, we can reduce the scaling of the (T) energy from the traditional to a more modest . We also discuss implementation details to aid future research, development, and software realization of this method. Additionally, we show that this method yields submillihartree (mEh) differences from CCSD(T) when evaluating absolute energies and sub-0.1 kcal/mol energy differences when evaluating relative energies. Finally, we demonstrate that this method converges to the true CCSD(T) energy through the systematic increasing of the rank or eigenvalue tolerance of the orthogonal projector, as well as exhibiting sublinear to linear error growth with respect to system size.
Augmentation for Learning From Demonstration with Environmental Constraints

—We introduce a Learning from Demonstration (LfD) approach for contact-rich manipulation tasks with articulated mechanisms. The extracted policy from a single human demonstration generalizes to different mechanisms of the same type and is robust against environmental variations. The key to achieving such generalization and robustness from a single human demonstration is to autonomously augment the initial demonstration to gather additional information through purposefully interacting with the environment. Our real-world experiments on complex mechanisms with multi-DOF demonstrate that our approach can reliably accomplish the task in a changing environment. Videos are available at the: project page.