Miracle-dz/UniFAR-FTUs huggingface dataset:
A M IRROR D ESCENT APPROACH FOR M EAN F IELD C ONTROL APPLIED TO DEMANDE - SIDE MANAGEMENT

We consider a ﬁnite-horizon Mean Field Control problem for Markovian models. The objective function is composed of a sum of convex and Lipschitz functions taking their values on a space of state-action distributions. We introduce an iterative algorithm which we prove to be a Mirror Descent associated with a non-standard Bregman divergence, having a convergence rate of order 1 / √ K . It requires the solution of a simple dynamic programming problem at each iteration. We compare this algorithm with learning methods for Mean Field Games after providing a reformulation of our control problem as a game problem. These theoretical contributions are illustrated with numerical examples applied to a demand-side management problem for power systems aimed at controlling the average power consumption proﬁle of a population of ﬂexible devices contributing to the power system balance.
Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written for a single device into an equivalent distributed program that is capable of scaling up to thousands of devices with no user configuration. Rhino firstly works on a semantically independent intermediate representation of tensor programs, which facilitates its generalization to unprecedented applications. Additionally, it implements a task-oriented controller and a distributed runtime for optimal performance. Rhino explores on a complete and systematic parallelization strategy space that comprises all the paradigms commonly employed in deep learning (DL), in addition to strided partitioning and pipeline parallelism on non-linear models. Aiming to efficiently search for a near-optimal parallel execution plan, our analysis of production clusters reveals general heuristics to speed up the strategy search. On top of it, two optimization levels are designed to offer users flexible trade-offs between the search time and strategy quality. Our experiments demonstrate that Rhino can not only re-discover the expert-crafted strategies of classic, research and production DL models, but also identify novel parallelization strategies which surpass existing systems for novel models.
Dynamic Field-Programmable Logic-Driven Soft Exosuit

The next generation of etextiles foresees an era of smart wearable garments where embedded seamless intelligence provides the ability to sense, process, and perform. Core to this vision is embedded textile functionality enabling dynamic configuration. In this article, we detail a methodology, design, and implementation of a dynamic field-programmable logic-driven fabric soft exosuit. Dynamic field programmability (FP) allows the soft exosuit to alter its functionality and adapt to specific exercise programs depending on the wearers need. The dynamic FP is enabled through motion-based control arm movements of the soft exosuit triggering momentary sensors embedded in the fabric exosuit at specific joint placement points (right arm: wrist and elbow). The embedded circuitry in the fabric exosuit is implemented using a layered and interchangeable approach. This includes logic gate patches (AND, OR, NOT) and a layered textile interconnection panel. This modular and interchangeable design enhances the soft exosuits flexibility and adaptability. A truth table aligning to a rehabilitation healthcare use case was utilized. Tests were completed validating the FP of the soft exosuit and its capability to switch between its embedded logic-driven circuitry and its operational and functionality options controlled by motion movement of the wearers right arm (elbow and wrist). Iterative exercise movement and acceleration-based tests were completed to validate the functionality of the field-programmable logic-driven fabric exosuit. We demonstrate a working soft exosuit prototype with motion-controlled operational functionality that can be applied to rehabilitation applications.
Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications

In recent years, increasingly complex computational models are being built to describe physical systems which has led to increased use of surrogate models to reduce computational cost. In problems related to Structural Health Monitoring (SHM), models capable of both handling high-dimensional data and quantifying uncertainty are required. In this work, our goal is to propose a conditional deep generative model as a surrogate aimed at such applications and high-dimensional stochastic structural simulations in general. To that end, a conditional variational autoencoder (CVAE) utilizing convolutional neural networks (CNNs) is employed to obtain reconstructions of spatially ordered structural response quantities for structural elements that are subjected to stochastic loading. Two numerical examples, inspired by potential SHM applications, are utilized to demonstrate the performance of the surrogate. The model is able to achieve high reconstruction accuracy compared to the reference Finite Element (FE) solutions, while at the same time successfully encoding the load uncertainty.
Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus

We propose a new method based on discrete Fourier analysis to analyze the time evolutionary algorithms spend on plateaus. This immediately gives a concise proof of the classic estimate of the expected runtime of the (1 + 1) evolutionary algorithm on the Needle problem due to Garnier, Kallel, and Schoenauer (1999). We also use this method to analyze the runtime of the (1 + 1) evolutionary algorithm on a benchmark consisting of n/ℓ plateaus of effective size 2ℓ - 1 which have to be optimized sequentially in a LeadingOnes fashion. Using our new method, we determine the precise expected runtime both for static and fitness-dependent mutation rates. We also determine the asymptotically optimal static and fitness-dependent mutation rates. For ℓ = o(n), the optimal static mutation rate is approximately 1.59/n. The optimal fitness dependent mutation rate, when the first k fitness-relevant bits have been found, is asymptotically 1/(k + 1). These results, so far only proven for the single-instance problem LeadingOnes, thus hold for a much broader class of problems. We expect similar extensions to be true for other important results on LeadingOnes. We are also optimistic that our Fourier analysis approach can be applied to other plateau problems as well.
Steric Engineering of Point Defects in Lead Halide Perovskites

Due to their high photovoltaic efficiency and low-cost synthesis, lead halide perovskites have attracted wide interest for application in new solar cell technologies. The most stable and efficient ABX$_3$ perovskite solar cells employ mixed A-site cations, however the impact of cation mixing on carrier trapping and recombination -- key processes that limit photovoltaic performance -- is not fully understood. Here we analyse non-radiative carrier trapping in the mixed A-cation hybrid halide perovskite MA$_{1-x}$Cs$_x$PbI$_3$. By using rigorous first-principles simulations we show that cation mixing leads to a hole trapping rate at the iodine interstitial that is eight orders of magnitude greater than in the single cation system. We demonstrate that the same defect in the same material can display a wide variety of defect activity -- from electrically inactive to recombination centre -- and, in doing so, resolve conflicting reports in the literature. Finally, we propose a new mechanism in which steric effects can be used to determine the rate of carrier trapping; this is achieved by controlling the phase and dynamical response of the lattice through the A-site composition. Our findings elucidate crucial links between chemical composition, defect activity and optoelectronic performance, and suggest a general approach that can help to rationalise the development of new crystalline materials with target defect properties.
Learning From Biased Soft Labels

Knowledge distillation has been widely adopted in a variety of tasks and has achieved remarkable successes. Since its inception, many researchers have been intrigued by the dark knowledge hidden in the outputs of the teacher model. Recently, a study has demonstrated that knowledge distillation and label smoothing can be unified as learning from soft labels. Consequently, how to measure the effectiveness of the soft labels becomes an important question. Most existing theories have stringent constraints on the teacher model or data distribution, and many assumptions imply that the soft labels are close to the ground-truth labels. This paper studies whether biased soft labels are still effective. We present two more comprehensive indicators to measure the effectiveness of such soft labels. Based on the two indicators, we give sufficient conditions to ensure biased soft label based learners are classifier-consistent and ERM learnable. The theory is applied to three weakly-supervised frameworks. Experimental results validate that biased soft labels can also teach good students, which corroborates the soundness of the theory.
Learning Multi-Object Positional Relationships via Emergent Communication

The study of emergent communication has been dedicated to interactive artificial intelligence. While existing work focuses on communication about single objects or complex image scenes, we argue that communicating relationships between multiple objects is important in more realistic tasks, but understudied. In this paper, we try to fill this gap and focus on emergent communication about positional relationships between two objects. We train agents in the referential game where observations contain two objects, and find that generalization is the major problem when the positional relationship is involved. The key factor affecting the generalization ability of the emergent language is the input variation between Speaker and Listener, which is realized by a random image generator in our work. Further, we find that the learned language can generalize well in a new multi-step MDP task where the positional relationship describes the goal, and performs better than raw-pixel images as well as pre-trained image features, verifying the strong generalization ability of discrete sequences. We also show that language transfer from the referential game performs better in the new task than learning language directly in this task, implying the potential benefits of pre-training in referential games. All in all, our experiments demonstrate the viability and merit of having agents learn to communicate positional relationships between multiple objects through emergent communication.
Group Fairness with Uncertainty in Sensitive Attributes

Learning a fair predictive model is crucial to mitigate biased decisions against minority groups in high-stakes applications. A common approach to learn such a model involves solving an optimization problem that maximizes the predictive power of the model under an appropriate group fairness constraint. However, in practice, sensitive attributes are often missing or noisy resulting in uncertainty. We demonstrate that solely enforcing fairness constraints on uncertain sensitive attributes can fall significantly short in achieving the level of fairness of models trained without uncertainty. To overcome this limitation, we propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes. The algorithm is guided by a Gaussian analysis for the independence notion of fairness where we propose a robust quadratically constrained quadratic problem to ensure a strict fairness guarantee with uncertain sensitive attributes. Our algorithm is applicable to both discrete and continuous sensitive attributes and is effective in real-world classification and regression tasks for various group fairness notions, e.g., independence and separation.
Evaluating Trade-offs in Computer Vision Between Attribute Privacy, Fairness and Utility

This paper investigates to what degree and magnitude tradeoffs exist between utility, fairness and attribute privacy in computer vision. Regarding privacy, we look at this important problem specifically in the context of attribute inference attacks, a less addressed form of privacy. To create a variety of models with different preferences, we use adversarial methods to intervene on attributes relating to fairness and privacy. We see that that certain tradeoffs exist between fairness and utility, privacy and utility, and between privacy and fairness. The results also show that those tradeoffs and interactions are more complex and nonlinear between the three goals than intuition would suggest.
Tensorial parametric model order reduction of nonlinear dynamical systems

For a nonlinear dynamical system that depends on parameters, the paper introduces a novel tensorial reduced-order model (TROM). The reduced model is projection-based, and for systems with no parameters involved, it resembles proper orthogonal decomposition (POD) combined with the discrete empirical interpolation method (DEIM). For parametric systems, TROM employs low-rank tensor approximations in place of truncated SVD, a key dimension-reduction technique in POD with DEIM. Three popular low-rank tensor compression formats are considered for this purpose: canonical polyadic, Tucker, and tensor train. The use of multilinear algebra tools allows the incorporation of information about the parameter dependence of the system into the reduced model and leads to a POD-DEIM type ROM that (i) is parameter-specific (localized) and predicts the system dynamics for out-of-training set (unseen) parameter values, (ii) mitigates the adverse effects of high parameter space dimension, (iii) has online computational costs that depend only on tensor compression ranks but not on the full-order model size, and (iv) achieves lower reduced space dimensions compared to the conventional POD-DEIM ROM. The paper explains the method, analyzes its prediction power, and assesses its performance for two specific parameter-dependent nonlinear dynamical systems.
Coalitions of arguments: A tool for handling bipolar argumentation frameworks

Bipolar argumentation frameworks enable to represent two kinds of interaction between arguments: support and conflict. In this paper, we turn a bipolar argumentation framework into a meta-argumentation framework where conflicts occur between sets of arguments, characterized as coalitions of supporting arguments. So, Dung's well-known semantics can be used on this meta-argumentation framework to select the acceptable arguments. © 2009 Wiley Periodicals, Inc. This article is an extended version of a paper accepted for CMNA Workshop (Computational Models of Natural Arguments), which has been published in January 2007. This extension concerns algorithms, complexity results, and some new definitions and properties.
Online Tool Selection with Learned Grasp Prediction Models

Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped with several end-effector tools that can be swapped on-the-fly, based on the target object. Tool-change, however, takes time. Choosing the order of grasps to perform, and corresponding tool-change actions, can improve system throughput; this is the topic of our work. The main challenge in planning tool change is uncertainty - we typically cannot see objects in the bin that are currently occluded. Inspired by queuing and admission control problems, we model the problem as a Markov Decision Process (MDP), where the goal is to maximize expected throughput, and we pursue an approximate solution based on model predictive control, where at each time step we plan based only on the currently visible objects. Special to our method is the idea of void zones, which are geometrical boundaries in which an unknown object will be present, and therefore cannot be accounted for during planning. Our planning problem can be solved using integer linear programming (ILP). However, we find that an approximate solution based on sparse tree search yields near optimal performance at a fraction of the time. Another question that we explore is how to measure the performance of tool-change planning: we find that throughput alone can fail to capture delicate and smooth behavior, and propose a principled alternative. Finally, we demonstrate our algorithms on both synthetic and real world bin picking tasks.
Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-Welfare

Risk assessment algorithms are being adopted by public sector agencies to make high-stakes decisions about human lives. Algorithms model “risk” based on individual client characteristics to identify clients most in need. However, this understanding of risk is primarily based on easily quantifiable risk factors that present an incomplete and biased perspective of clients. We conducted a computational narrative analysis of child-welfare casenotes and draw attention to deeper systemic risk factors that are hard to quantify but directly impact families and street-level decision-making. We found that beyond individual risk factors, the system itself poses a significant amount of risk where parents are over-surveilled by caseworkers and lack agency in decision-making processes. We also problematize the notion of risk as a static construct by highlighting the temporality and mediating effects of different risk, protective, systemic, and procedural factors. Finally, we draw caution against using casenotes in NLP-based systems by unpacking their limitations and biases embedded within them.
Frequency weighted model reduction via optimal projection

The problem of order reduction of a linear, time-invariant system with frequency weighting is considered. The resulting reduced-order model minimizes a quadratic error criterion in the frequency domain with either input or output frequency weighting. The problem is transformed to the time domain where the weighting matrix is expressed as a shaping filter that generates colored noise at the input. When the weighting matrix is singular at infinity, the reduced order model has a direct transmission term even if the original system is strictly proper. The additional degree of freedom enables better approximation over a specified frequency range. <<ETX>>
Spectroscopic analysis of nearby lower‐main‐sequence stars

Abundances of O, Na, Mg, Al, Si, K, Ca, Sc, Ti, V, Cr, Fe, Ni and Ba are determined for 30 nearby lower-main-sequence stars in the Northern sky using high-resolution, high signal-to-noise ratio spectra. Our results show an equilibrium of [Fe/H] I and [Fe/H] II and a much smaller star-to-star scatter of the abundance ratios as a function of metallicity compared with the results of Kotoneva et al. The non-local thermodynamic equilibrium (non-LTE) corrections for oxygen are considered and found to be small (∼―0.04 dex). A flat trend of [O/Fe] exists over the whole metallicity range. The non-LTE effects for some important elements are discussed, and it is found that the abundance pattern for our programme stars is very similar to that of F and G dwarfs.
Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes

Unpaired image-to-image translation methods aim at learning a mapping of images from a source domain to a target domain. Recently, these methods proved to be very useful in biological applications to display subtle phenotypic cell variations otherwise invisible to the human eye. However, current models require a large number of images to be trained, while most microscopy experiments remain limited in the number of images they can produce. In this work, we present an improved CycleGAN architecture that employs self-supervised discriminators to alleviate the need for numerous images. We demonstrate quantitatively and qualitatively that the proposed approach outperforms the CycleGAN baseline, including when it is combined with differentiable augmentations. We also provide results obtained with small biological datasets on obvious and non-obvious cell phenotype variations, demonstrating a straightforward application of this method.
Scrambling and Recovery of Quantum Information in Inhomogeneous Quenches in Two-dimensional Conformal Field Theories

We study various quantum quench processes induced by the M\"obius/sine-square deformation of the Hamiltonian in two-dimensional conformal field theories starting from the thermofield double state in the two copies of the Hilbert space. These quantum quenches, some of which are directly related to the operator entanglement of the time-evolution operators, allow us to study scrambling and recovery of quantum information. In particular, under the SSD time-evolution, we show from the time-dependence of mutual information that the Bell pairs, initially shared by the subsystems of the two Hilbert spaces, may revive even after the mutual information for small subsystems is completely destroyed by quantum information scrambling dynamics. This mutual information is robust against the strong scrambling dynamics. As a consequence, the steady state has a non-local correlation shared not by any of two parties but by three parties. In the holographic dual description, a wormhole connecting the two Hilbert spaces may non-linearly grow with time during the quantum quenches. We also propose effective pictures that describe the dynamics of mutual information during the time-evolution by inhomogeneous Hamiltonians.
The brightest GRB ever detected: GRB 221009A as a highly luminous event at z = 0.151

Context: The extreme luminosity of gamma-ray bursts (GRBs) makes them powerful beacons for studies of the distant Universe. The most luminous bursts are typically detected at moderate/high redshift, where the volume for seeing such rare events is maximized and the star-formation activity is greater than at z = 0. For distant events, not all observations are feasible, such as at TeV energies. Aims: Here we present a spectroscopic redshift measurement for the exceptional GRB 221009A, the brightest GRB observed to date with emission extending well into the TeV regime. Methods: We used the X-shooter spectrograph at the ESO Very Large Telescope (VLT) to obtain simultaneous optical to near-IR spectroscopy of the burst afterglow 0.5 days after the explosion. Results: The spectra exhibit both absorption and emission lines from material in a host galaxy at z = 0.151. Thus GRB 221009A was a relatively nearby burst with a luminosity distance of 745 Mpc. Its host galaxy properties (star-formation rate and metallicity) are consistent with those of LGRB hosts at low redshift. This redshift measurement yields information on the energy of the burst. The inferred isotropic energy release, $E_{\rm iso}>5 \times 10^{54}$ erg, lies at the high end of the distribution, making GRB 221009A one of the nearest and also most energetic GRBs observed to date. We estimate that such a combination (nearby as well as intrinsically bright) occurs between once every few decades to once per millennium.