Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks

Volt/VAR control rules facilitate the autonomous operation of distributed energy resources (DER) to regulate voltage in power distribution grids. According to non-incremental control rules, such as the one mandated by the IEEE Standard 1547, the reactive power setpoint of each DER is computed as a piecewise-linear curve of the local voltage. However, the slopes of such curves are upper-bounded to ensure stability. On the other hand, incremental rules add a memory term into the setpoint update, rendering them universally stable. They can thus attain enhanced steady-state voltage profiles. Optimal rule design (ORD) for incremental rules can be formulated as a bilevel program. We put forth a scalable solution by reformulating ORD as training a deep neural network (DNN). This DNN emulates the Volt/VAR dynamics for incremental rules derived as iterations of proximal gradient descent (PGD). Analytical findings and numerical tests corroborate that the proposed ORD solution can be neatly adapted to single/multi-phase feeders.
Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach

Learning from noisy labels (LNL) plays a crucial role in deep learning. The most promising LNL methods rely on identifying clean-label samples from a dataset with noisy annotations. Such an identification is challenging because the conventional LNL problem, which assumes a single noisy label per instance, is non-identifiable, i.e., clean labels cannot be estimated theoretically without additional heuristics. In this paper, we aim to formally investigate this identifiability issue using multinomial mixture models to determine the constraints that make the problem identifiable. Specifically, we discover that the LNL problem becomes identifiable if there are at least $2C - 1$ noisy labels per instance, where $C$ is the number of classes. To meet this requirement without relying on additional $2C - 2$ manual annotations per instance, we propose a method that automatically generates additional noisy labels by estimating the noisy label distribution based on nearest neighbours. These additional noisy labels enable us to apply the Expectation-Maximisation algorithm to estimate the posterior probabilities of clean labels, which are then used to train the model of interest. We empirically demonstrate that our proposed method is capable of estimating clean labels without any heuristics in several label noise benchmarks, including synthetic, web-controlled, and real-world label noises. Furthermore, our method performs competitively with many state-of-the-art methods.
A Barrier Method for Contact Avoiding Particles in Stokes Flow

Rigid particles in a Stokesian fluid can physically not overlap, as a thin layer of fluid always separates a particle pair, exerting increasingly strong repulsive forces on the bodies for decreasing separations. Numerically, resolving these lubrication forces comes at an intractably large cost even for moderate system sizes. Hence, it can typically not be guaranteed that particle collisions and overlaps do not occur in a dynamic simulation, independently of the choice of method to solve the Stokes equations. In this work, non-overlap constraints, in terms of the Euclidean distance between boundary points on the particles, are represented via a barrier energy. We solve for the minimum magnitudes of repelling contact forces between any particle pair in contact to correct for overlaps by enforcing a zero barrier energy at the next time level, given a contact-free configuration at a previous instance in time. The method is tested using a multiblob method to solve the mobility problem in Stokes flow applied to suspensions of spheres, rods and boomerang shaped particles. Collision free configurations are obtained at all instances in time. The effect of the contact forces on the collective order of a set of rods in a background flow that naturally promote particle interactions is also illustrated.
Study of new physics effects in lepton flavour violating B decays into K2^star(1430)l1 l2

Lepton flavour violation (LFV) is one of the most trending topics to probe new physics (NP). The powerful accelerators have enhanced their intensities to observe the LFV decays very precisely. In this situation, the theorists are also interested to study these decays in various NP models and in model independent way to get precise results. Motivated by these results we have studied B decays into K2^star(1430)l1 l2 in non-universal Z^' model. Here, we have structured the two-fold angular distribution of the decays in terms of transversity amplitudes and the transversity amplitudes are formed with NP Wilson coefficients. The variation of the branching ratios and forward backward asymmetries show the sensitivity of NP. The observables calculated in this work are very interesting and might provide a new way towards NP.
A Bayesian Perspective on Feller, Pollard and the Complete Monotonicity of the Mittag-Leﬄer Function

Pollard used contour integration to show that the Mittag-Leﬄer function is the Laplace transform of a positive function, thereby proving that it is completely monotone. He also cited personal communication by Feller of a discovery of the result by “methods of probability theory”. Feller used the two-dimensional Laplace transform of a bivariate distribution to derive the result. We prove the result by a Bayesian approach. We proceed to prove the complete monotonicity of the multi-parameter Mittag-Leﬄer function, thereby generalising the Pollard result by methods of Bayesian probability theory.
Cost Inference for Feedback Dynamic Games from Noisy Partial State Observations and Incomplete Trajectories

In multi-agent dynamic games, the Nash equilibrium state trajectory of each agent is determined by its cost function and the information pattern of the game. However, the cost and trajectory of each agent may be unavailable to the other agents. Prior work on using partial observations to infer the costs in dynamic games assumes an open-loop information pattern. In this work, we demonstrate that the feedback Nash equilibrium concept is more expressive and encodes more complex behavior. It is desirable to develop specific tools for inferring players' objectives in feedback games. Therefore, we consider the dynamic game cost inference problem under the feedback information pattern, using only partial state observations and incomplete trajectory data. To this end, we first propose an inverse feedback game loss function, whose minimizer yields a feedback Nash equilibrium state trajectory closest to the observation data. We characterize the landscape and differentiability of the loss function. Given the difficulty of obtaining the exact gradient, our main contribution is an efficient gradient approximator, which enables a novel inverse feedback game solver that minimizes the loss using first-order optimization. In thorough empirical evaluations, we demonstrate that our algorithm converges reliably and has better robustness and generalization performance than the open-loop baseline method when the observation data reflects a group of players acting in a feedback Nash game.
DiffXPBD

We present DiffXPBD, a novel and efficient analytical formulation for the differentiable position-based simulation of compliant constrained dynamics (XPBD). Our proposed method allows computation of gradients of numerous parameters with respect to a goal function simultaneously leveraging a performant simulation model. The method is efficient, thus enabling differentiable simulations of high resolution geometries and degrees of freedom (DoFs). Collisions are naturally included in the framework. Our differentiable model allows a user to easily add additional optimization variables. Every control variable gradient requires the computation of only a few partial derivatives which can be computed using automatic differentiation code. We demonstrate the efficacy of the method with examples such as elastic cloth and volumetric material parameter estimation, initial value optimization, optimizing for underlying body shape and pose by only observing the clothing, and optimizing a time-varying external force sequence to match sparse keyframe shapes at specific times. Our approach demonstrates excellent efficiency and we demonstrate this on high resolution meshes with optimizations involving over 26 million degrees of freedom. Making an existing solver differentiable requires only a few modifications and the model is compatible with both modern CPU and GPU multi-core hardware.
Accurate target tracking using a single imaging sensor

We shall show how to extract various important information on the motion of a target from a sequence of its image frames obtained from a single staring imaging sensor which is mounted on an aircraft. Specifically, we present an algorithm which estimates following 15 parameters of an enemy aircraft from its image sequence; position, linear velocity, attitude, instantaneous angular velocity and acceleration. The attitude information of the aircraft is obtained through the image matching operation. In simulations, we show that the proposed algorithm is superior to the conventional algorithms which do not use the attitude information.
Anonymous Pattern Molecular Fingerprint and its Applications on Property Identification

Molecular fingerprints are significant cheminformatics tools to map molecules into vectorial space according to their characteristics in diverse functional groups, atom sequences, and other topological structures. In this paper, we investigate a novel molecular fingerprint <italic>Anonymous-FP</italic> that possesses abundant perception about the underlying interactions shaped in small, medium, and large-scale atom chains. In detail, the possible atom chains from each molecule are sampled and extended as anonymous atom chains using an anonymous encoding manner. After that, the molecular fingerprint <italic>Anonymous-FP</italic> is embedded into vectorial space in virtue of the Natural Language Processing technique <italic>PV-DBOW</italic>. <italic>Anonymous-FP</italic> is studied on molecular property identification via molecule classification experiments on a series of molecule databases and has shown valuable advantages such as less dependence on prior knowledge, rich information content, full structural significance, and high experimental performance. During the experimental verification, the scale of the atom chain or its anonymous pattern is found significant to the overall representation ability of <italic>Anonymous-FP</italic>. Generally, the typical scale <inline-formula><tex-math notation="LaTeX">$r = 8$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mn>8</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="wei-ieq1-3322697.gif"/></alternatives></inline-formula> could enhance the molecule classification performance, and specifically, <italic>Anonymous-FP</italic> gains the classification accuracy to above 93% on all NCI datasets.
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

With the rapid development of deep learning models and hardware support for dense computing, the deep learning (DL) workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broad range of operations scattered across the models. Accelerating a few compute-intensive operations using the expert-tuned implementation of primitives doesn't fully exploit the performance potential of AI hardware. Various efforts have been made to compile a full deep neural network (DNN) graph. One of the biggest challenges is to achieve high-performance tensor compilation by generating expert-level performance code for the dense compute-intensive operations and applying compilation optimization at the scope of DNN computation graph across multiple compute-intensive operations. We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high-performance code generation of the deep neural network graph. oneDNN Graph Compiler addresses unique optimization challenges in the deep learning domain, such as low-precision computation, aggressive fusion of graph operations, optimization for static tensor shapes and memory layout, constant weight optimization, and memory buffer reuse. Experimental results demonstrate significant performance gains over existing tensor compiler and primitives library for performance-critical DNN computation graphs and end-to-end models on Intel® Xeon® Scalable Processors.
A high refractive index metamaterial at visible frequencies formed by stacked cut-wire plasmonic structures

A type of metamaterial composing of stacked cut-wire plasmonic structures has been designed, fabricated, and characterized. The excitation of electric resonance caused by the surface plasmons effect dominates the electric field states, so that the effective refractive index can behave as a dramatic increase at visible frequencies, which is intimately associated with the resonance strength and can be modulated by reasonably changing the structure geometries. The phenomenon has been demonstrated with the measured transmittances being in agreement with simulation results. And the effective parameters judged by Kramers–Kronig relations have been uniquely retrieved from the simulated transmission and reflection data.
A Collapsar Origin for GRB 211211A Is (Just Barely) Possible

Gamma-ray bursts (GRBs) have historically been divided into two classes. Short-duration GRBs are associated with binary neutron star mergers (NSMs), while long-duration bursts are connected to a subset of core-collapse supernovae (SNe). GRB 211211A recently made headlines as the first long-duration burst purportedly generated by an NSM. The evidence for an NSM origin was excess optical and near-infrared emission consistent with the kilonova observed after the gravitational-wave-detected NSM GW170817. Kilonovae derive their unique electromagnetic signatures from the properties of the heavy elements synthesized by rapid neutron capture (the r-process) following the merger. Recent simulations suggest that the “collapsar” SNe that trigger long GRBs may also produce r-process elements. While observations of GRB 211211A and its afterglow rule out an SN typical of those that follow long GRBs, an unusual collapsar could explain both the duration of GRB 211211A and the r-process-powered excess in its afterglow. We use semianalytic radiation transport modeling to evaluate low-mass collapsars as the progenitors of GRB 211211A–like events. We compare a suite of collapsar models to the afterglow-subtracted emission that followed GRB 211211A, and find the best agreement for models with high kinetic energies and an unexpected pattern of 56Ni enrichment. We discuss how core-collapse explosions could produce such ejecta, and how distinct our predictions are from those generated by more straightforward kilonova models. We also show that radio observations can distinguish between kilonovae and the more massive collapsar ejecta we consider here.
A Stochastic Multi-Objective Optimization Framework for Planning and Scheduling of Community Energy Storage Systems

—This paper explores a methodology to optimize the planning and the scheduling of a community energy storage (CES) considering the uncertainty of real power consumption and solar photovoltaic (SPV) generation of the customers in low voltage (LV) distribution networks. To this end, we develop a stochastic multi-objective optimization framework which mini-mizes the investment and the operation costs of the CES provider, and the social costs of the customers (i.e. cost of customers for trading energy with the grid and the CES). The uncertainty of SPV generation and real power consumption are modelled to follow the beta and normal distributions, respectively. Then, the roulette wheel mechanism (RWM) is exploited to formulate a scenario-based stochastic program. The initial scenarios obtained from the RWM, are then reduced by using the K-Means clustering algorithm, to keep the problem tractability. A case study highlights our model provides 10-21% more cumulative economic beneﬁts for the customers and the CES provider, compared with the models that optimize only the CES scheduling. Also, the simulation results for different energy price schemes of the CES provider reﬂect, the customers change their power exchange with the CES and the grid signiﬁcantly, to minimize their social costs.
UNAEN: Unsupervised Abnormality Extraction Network for MRI Motion Artifact Reduction

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting on various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.
Diffusion approximations of Oja's online principal component analysis

Oja’s algorithm of principal component analysis (PCA) has been one of the methods utilized in practice to reduce dimension. In this paper, we focus on the convergence property of the discrete algorithm. To realize that, we view the algorithm as a stochastic process on the parameter space and semi-group. We approximate it by SDEs, and prove large time convergence of the SDEs to ensure its performance. This process is completed in three steps. First, the discrete algorithm can be viewed as a semigroup: S k ϕ = E [ ϕ ( W ( k ))]. Second, we construct stochastic diﬀerential equations (SDEs) on the Stiefel manifold, i.e
Attribute-Centric Compositional Text-to-Image Generation

Despite the recent impressive breakthroughs in text-to-image generation, generative models have difficulty in capturing the data distribution of underrepresented attribute compositions while over-memorizing overrepresented attribute compositions, which raises public concerns about their robustness and fairness. To tackle this challenge, we propose ACTIG, an attribute-centric compositional text-to-image generation framework. We present an attribute-centric feature augmentation and a novel image-free training scheme, which greatly improves model's ability to generate images with underrepresented attributes. We further propose an attribute-centric contrastive loss to avoid overfitting to overrepresented attribute compositions. We validate our framework on the CelebA-HQ and CUB datasets. Extensive experiments show that the compositional generalization of ACTIG is outstanding, and our framework outperforms previous works in terms of image quality and text-image consistency.
Super-Resolution With Sparse Arrays: A Nonasymptotic Analysis of Spatiotemporal Trade-Offs

Sparse arrays have emerged as a popular alternative to the conventional uniform linear array (ULA) due to the enhanced degrees of freedom (DOF) and superior resolution offered by them. In the passive setting, these advantages are realized by leveraging correlation between the received signals at different sensors. This has led to the belief that sparse arrays require a large number of temporal measurements to reliably estimate parameters of interest from these correlations, and therefore they may not be preferred in the sample-starved regime. In this paper, we debunk this myth by performing a rigorous non-asymptotic analysis of the Coarray ESPRIT algorithm. This seemingly counter-intuitive result is a consequence of the scaling of the singular value of the coarray manifold, which compensates for the potentially large covariance estimation error in the limited snapshot regime. Specifically, we show that for a nested array operating in the regime of fewer sources than sensors (<inline-formula><tex-math notation="LaTeX">$S=O(1)$</tex-math></inline-formula>), it is possible to bound the matching distance error between the estimated and true directions of arrival (DOAs) by an arbitrarily small quantity (<inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>) with high probability, provided (i) the number of temporal snapshots (<inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>) scales only logarithmically with the number of sensors (<inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula>), i.e. <inline-formula><tex-math notation="LaTeX">$L=\Omega(\ln(P)/\epsilon^{2})$</tex-math></inline-formula>, and (ii) a suitable separation condition is satisfied. Our results also formally prove the well-known empirical resolution benefits of sparse arrays, by establishing that the minimum separation between sources can be <inline-formula><tex-math notation="LaTeX">$\Omega(1/P^{2})$</tex-math></inline-formula>, as opposed to separation <inline-formula><tex-math notation="LaTeX">$\Omega(1/P)$</tex-math></inline-formula> required by a ULA with the same number of sensors. In addition to the array geometry, our sample complexity expression reveals the dependence on other key model parameters such as Signal to Noise Ratio (SNR) and the dynamic range of the source powers. This enables us to establish the superior noise-resilience of nested arrays both theoretically and empirically.