Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure of optimal control for planetary landing with control and state constraints

This paper studies a vertical powered descent problem in the context of planetary landing, considering glide-slope and thrust pointing constraints and minimizing any final cost. In a first time, it proves the Max-Min-Max or Max-Singular-Max form of the optimal control using the Pontryagin Maximum Principle, and it extends this result to a problem formulation considering the effect of an atmosphere. It also shows that the singular structure does not appear in generic cases. In a second time, it theoretically analyzes the optimal trajectory for a more specific problem formulation to show that there can be at most one contact or boundary interval with the state constraint on each Max or Min arc.
Preliminary Results of a Survey on the Use of Self-Adaptation in Industry

Self-adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-adaptation is currently not clear. To get insights into the use of self-adaptation in practice, we are running a largescale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-adaptive systems. We highlight the main insights obtained so far: motivations for self-adaptation, concrete use cases, and difficulties encountered when applying self-adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study.CCS CONCEPTS • Software and its engineering $\rightarrow$Software system structures; Designing software; Maintaining software.
Toward A Supersymmetric w 1+ ∞ Symmetry in the Celestial Conformal Field Theory

The w 1+ ∞ symmetry algebra appears in the Einstein–Yang–Mills theory, proposed recently by Strominger. In this paper, we derive the supersymmetric w 1+ ∞ symmetry by using the known results on the operator product expansions (OPEs) between the graviton, gravitino, gluon, and gluino in the supersymmetric version of the above theory. We calculate the four additional commutator relations between the soft currents explicitly. In addition, we analyze the works of Odake et al. and Pope et al. and introduce the additional symmetry current that corresponds to the celestial gluino operator. Through this procedure, all seven commutator relations can be connected to the ones associated with the supersymmetric w 1+ ∞ algebra with SU ( N ) symmetry under the restrictions of wedge modes.
Your fairness may vary: Pretrained language model fairness in toxic text classification

The popularity of pretrained language models in natural language processing systems calls for a careful evaluation of such models in down-stream tasks, which have a higher potential for societal impact. The evaluation of such systems usually focuses on accuracy measures. Our findings in this paper call for attention to be paid to fairness measures as well. Through the analysis of more than a dozen pretrained language models of varying sizes on two toxic text classification tasks (English), we demonstrate that focusing on accuracy measures alone can lead to models with wide variation in fairness characteristics. Specifically, we observe that fairness can vary even more than accuracy with increasing training data size and different random initializations. At the same time, we find that little of the fairness variation is explained by model size, despite claims in the literature. To improve model fairness without retraining, we show that two post-processing methods developed for structured, tabular data can be successfully applied to a range of pretrained language models. Warning: This paper contains samples of offensive text.
Agent-based Constraint Solving for Resource Allocation in Manycore Systems

For efficiency reasons, manycore systems are increasingly heterogeneous, which makes the mapping of complex workloads a key problem with a high optimization potential. Constraints express the application requirements like which core type to choose, how many cores to choose, exclusively or non-exclusively, using a certain core, etc. In this work, we propose a decentralized solution for solving application resource constraints by means of an agent-based approach in order to obtain scalability. We translate the constraints into a Distributed Constraint Optimization Problem (DCOP) and propose a local search algorithm RESMGM to solve them. For the first time, we demonstrate the viability and efficiency of the DCOP approach for heterogeneous manycore systems. Our RESMGM algorithm supports a far wider range of constraints than state-of-the-art, leading to superior results, but still has comparable overheads w.r. t. computation and communication.
Learning Invariances with Generalised Input-Convex Neural Networks

Considering smooth mappings from input vectors to continuous targets, our goal is to characterise subspaces of the input domain, which are invariant under such mappings. Thus, we want to characterise manifolds implicitly defined by level sets. Specifically, this characterisation should be of a global parametric form, which is especially useful for different informed data exploration tasks, such as building grid-based approximations, sampling points along the level curves, or finding trajectories on the manifold. However, global parameterisations can only exist if the level sets are connected. For this purpose, we introduce a novel and flexible class of neural networks that generalise input-convex networks. These networks represent functions that are guaranteed to have connected level sets forming smooth manifolds on the input space. We further show that global parameterisations of these level sets can be always found efficiently. Lastly, we demonstrate that our novel technique for characterising invariances is a powerful generative data exploration tool in real-world applications, such as computational chemistry.
Ponderomotive forces, Stokes drift, and momentum in acoustic and electromagnetic waves

We reveal universal connections between three important phenomena in classical wave physics: (i) the ponderomotive force acting on the medium particles in an oscillatory wavefield, (ii) the Stokes drift of free medium particles in a wave field, and (iii) the canonical wave momentum in a medium. We analyse these phenomena for (a) longitudinal sound waves in a gas or fluid and (b) transverse electromagnetic waves interacting with electrons in a plasma or metal. In both cases, assuming quasi-monochromatic yet arbitrarily inhomogeneous wavefields, the connections between the ponderomotive force, Stokes drift velocity, kinetic energy, and canonical wave momentum carried by the medium particles are given by the same simple expressions. This sheds light on the nature of these phenomena, their fine interplay, and can be useful for applications to dynamical transport phenomena in various types of waves.
CO Emission, Molecular Gas, and Metallicity in Main-sequence Star-forming Galaxies at z ∼ 2.3

We present observations of CO(3−2) in 13 main-sequence z = 2.0–2.5 star-forming galaxies at log(M*/M⊙)=10.2–10.6 that span a wide range in metallicity (O/H) based on rest-optical spectroscopy. We find that LCO(3−2)′ /SFR decreases with decreasing metallicity, implying that the CO luminosity per unit gas mass is lower in low-metallicity galaxies at z ∼ 2. We constrain the CO-to-H2 conversion factor (α CO) and find that α CO inversely correlates with metallicity at z ∼ 2. We derive molecular gas masses (M mol) and characterize the relations among M *, SFR, M mol, and metallicity. At z ∼ 2, M mol increases and the molecular gas fraction (M mol/M *) decreases with increasing M *, with a significant secondary dependence on SFR. Galaxies at z ∼ 2 lie on a near-linear molecular KS law that is well-described by a constant depletion time of 700 Myr. We find that the scatter about the mean SFR−M *, O/H−M *, and M mol−M * relations is correlated such that, at fixed M *, z ∼ 2 galaxies with larger M mol have higher SFR and lower O/H. We thus confirm the existence of a fundamental metallicity relation at z ∼ 2, where O/H is inversely correlated with both SFR and M mol at fixed M *. These results suggest that the scatter of the z ∼ 2 star-forming main sequence, mass–metallicity relation, and M mol–M * relation are primarily driven by stochastic variations in gas inflow rates. We place constraints on the mass loading of galactic outflows and perform a metal budget analysis, finding that massive z ∼ 2 star-forming galaxies retain only 30% of metals produced, implying that a large mass of metals resides in the circumgalactic medium.
A Machine Learning Approach to Automatic Classification of Eight Sleep Disorders

In this research, we attempt to answer the following basic research questions: Is a machine learning model able to classify all types of sleep disorders with high accuracy? Among the different modalities of sleep disorder signals, are some more important than others? Do raw signals improve the performance of a deep learning model when they are used as inputs? Prior research showed that most sleep disorders belong to eight categories. To study the performance of machine learning models in classifying polysomnography recordings into the eight categories of sleep pathologies, we selected the Cyclic Alternating Pattern Sleep Database. We developed a multi-channel Deep Learning model where a set of Convolutional Neural Networks were applied to six channels of raw signals of different modalities, including three channels of EEG signals and one channel each of EMG, ECG , and EOG signals. To compare the performance of the DL model with other models, we designed a model that took spectral features, instead of raw signals, as its inputs. We first studied the"importance"issue of signal modalities using the RF algorithm. We found that ECG contributed most to the important features and EMG second, among the four signal modalities. We then studied the accuracy performance of the proposed machine learning models. We verified that the multi-channel DL-R model, which took raw signals as its inputs, outperformed all other models, with its sensitivity and specificity scores both being above 95 %. This accuracy performance is on a par with those published results which dealt with fewer types of sleep disorders. We adopted two popular heatmap-generating techniques, with which we confirmed that the DL model's superior performance was owing to the CNN network's ability to extract potent features from raw signals.
A Bandwagon Bias Based Model for Opinion Dynamics: Intertwining between Homophily and Influence Mechanisms

Recently a model for the interplay between homophily-based appraisal dynamics and influence-based opinion dynamics has been proposed. The model explores for the first time how the opinions of a group of agents on a certain number of issues/topics is influenced by the agents' mutual appraisal and, conversely, the agents' mutual appraisal is updated based on the agents' opinions on the various issues, according to a homophily model. In this paper we show that a simplified (and, in some situations, more feasible) version of the model, that accounts only for the signs of the agents' appraisals rather than for their numerical values, provides an equally accurate and effective model of the opinion dynamics in small networks. The equilibria reached by this model correspond, almost surely, to situations in which the agents' network is complete and structurally balanced. On the other hand, we ensure that such equlibria can always be reached in a finite number of steps, and, differently from the original model, we rule out other types of equilibria that correspond to disconnected social networks.
Quantum Hall states for Rydberg arrays with laser-assisted dipole-dipole interactions

Rydberg atoms with dipole-dipole interactions provide intriguing platforms to explore exotic quantum many-body physics. Here we propose a novel scheme with laser-assisted dipole-dipole interactions to realize synthetic magnetic field for Rydberg atoms in a two-dimensional array configuration, which gives rise to the exotic bosonic topological states. In the presence of an external effective Zeeman splitting gradient, the dipole-dipole interaction between neighboring Rydberg atoms along the gradient direction is suppressed, but can be assisted when Raman lights are applied to compensate the energy difference. With this scheme we generate a controllable uniform magnetic field for the complex spin-exchange coupling model, which can be mapped to hard core bosons coupling to an external synthetic magnetic field. The highly tunable flat Chern bands of the hard core bosons are then obtained and moreover, the bosonic fractional quantum Hall states can be achieved with experimental feasibility. This work opens an avenue for the realization of the highly-sought-after bosonic topological orders using Rydberg atoms.
Activation Regression for Continuous Domain Generalization with Applications to Crop Classification

Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem, demonstrating how models generalise better with appropriate domain knowledge. We develop a dataset spatially distributed across the entire continental United States, providing macroscopic in-sight into the effects of geography on crop classiﬁcation in multi-spectral and temporally distributed satellite imagery. Our method demonstrates improved generalisabil-ity from 1) passing geographically correlated climate variables along with the satellite data to a Transformer model and 2) regressing on the model features to reconstruct these domain variables. Combined, we provide a novel perspec-tive on geographic generalisation in satellite imagery and a simple-yet-effective approach to leverage domain knowledge. Code is available at: https://github.com/ samar-khanna/cropmap
RankNEAT: outperforming stochastic gradient search in preference learning tasks

Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events. When a neural network is instead faced with subjectively defined labels---such as human demonstrations or annotations---SGD may struggle to explore the deceptive and noisy loss landscapes caused by the inherent bias and subjectivity of humans. While neural networks are often trained via preference learning algorithms in an effort to eliminate such data noise, the de facto training methods rely on gradient descent. Motivated by the lack of empirical studies on the impact of evolutionary search to the training of preference learners, we introduce the RankNEAT algorithm which learns to rank through neuroevolution of augmenting topologies. We test the hypothesis that RankNEAT outperforms traditional gradient-based preference learning within the affective computing domain, in particular predicting annotated player arousal from the game footage of three dissimilar games. RankNEAT yields superior performances compared to the gradient-based preference learner (RankNet) in the majority of experiments since its architecture optimization capacity acts as an eficient feature selection mechanism, thereby, eliminating overfitting. Results suggest that RankNEAT is a viable and highly eficient evolutionary alternative to preference learning.
ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP): Evidence for a Molecular Jet Launched at an Unprecedented Early Phase of Protostellar Evolution

Protostellar outflows and jets play a vital role in star formation as they carry away excess angular momentum from the inner disk surface, allowing the material to be transferred toward the central protostar. Theoretically, low-velocity and poorly collimated outflows appear from the beginning of the collapse at the first hydrostatic core (FHSC) stage. With growing protostellar core mass, high-density jets are launched, entraining an outflow from the infalling envelope. Until now, molecular jets have been observed at high velocity (≳100 km s−1) in early Class 0 protostars. We, for the first time, detect a dense molecular jet in SiO emission with low velocity (∼4.2 km s−1, deprojected ∼24 km s−1) from source G208.89–20.04Walma (hereafter G208Walma) using ALMA Band 6 observations. This object has some characteristics of FHSCs, such as a small outflow/jet velocity, extended 1.3 mm continuum emission, and N 2D+ line emission. Additional characteristics, however, are typical of early protostars: collimated outflow and SiO jet. The full extent of the outflow corresponds to a dynamical timescale of ∼ 930−100+200 yr. The spectral energy distribution also suggests a very young source having an upper limit of T bol ∼ 31 K and L bol ∼ 0.8 L ⊙. We conclude that G208Walma is likely in the transition phase from FHSC to protostar, and the molecular jet has been launched within a few hundred years of initial collapse. Therefore, G208Walma may be the earliest object discovered in the protostellar phase with a molecular jet.
Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural Network

Atmospheric turbulence distorts visual imagery and is always problematic for information interpretation by both human and machine. Most well-developed approaches to remove atmospheric turbulence distortion are model-based. However, these methods require high computation and large memory making real-time operation infeasible. Deep learning-based approaches have hence gained more attention but currently work efficiently only on static scenes. This paper presents a novel learning-based framework offering short temporal spanning to support dynamic scenes. We exploit complex-valued convolutions as phase information, altered by atmospheric turbulence, is captured better than using ordinary real-valued convolutions. Two concatenated modules are proposed. The first module aims to remove geometric distortions and, if enough memory, the second module is applied to refine micro details of the videos. Experimental results show that our proposed framework efficiently mitigates the atmospheric turbulence distortion and significantly outperforms existing methods.
Egocentric Human-Object Interaction Detection Exploiting Synthetic Data

We consider the problem of detecting Egocentric HumanObject Interactions (EHOIs) in industrial contexts. Since collecting and labeling large amounts of real images is challenging, we propose a pipeline and a tool to generate photo-realistic synthetic First Person Vision (FPV) images automatically labeled for EHOI detection in a specific industrial scenario. To tackle the problem of EHOI detection, we propose a method that detects the hands, the objects in the scene, and determines which objects are currently involved in an interaction. We compare the performance of our method with a set of state-of-the-art baselines. Results show that using a synthetic dataset improves the performance of an EHOI detection system, especially when few real data are available. To encourage research on this topic, we publicly release the proposed dataset at the following url: https://iplab.dmi. unict.it/EHOI_SYNTH/.
Sketching Algorithms and Lower Bounds for Ridge Regression

We give a sketching-based iterative algorithm that computes a $1+\varepsilon$ approximate solution for the ridge regression problem $\min_x \|Ax-b\|_2^2 +\lambda\|x\|_2^2$ where $A \in R^{n \times d}$ with $d \ge n$. Our algorithm, for a constant number of iterations (requiring a constant number of passes over the input), improves upon earlier work (Chowdhury et al.) by requiring that the sketching matrix only has a weaker Approximate Matrix Multiplication (AMM) guarantee that depends on $\varepsilon$, along with a constant subspace embedding guarantee. The earlier work instead requires that the sketching matrix has a subspace embedding guarantee that depends on $\varepsilon$. For example, to produce a $1+\varepsilon$ approximate solution in $1$ iteration, which requires $2$ passes over the input, our algorithm requires the OSNAP embedding to have $m= O(n\sigma^2/\lambda\varepsilon)$ rows with a sparsity parameter $s = O(\log(n))$, whereas the earlier algorithm of Chowdhury et al. with the same number of rows of OSNAP requires a sparsity $s = O(\sqrt{\sigma^2/\lambda\varepsilon} \cdot \log(n))$, where $\sigma = \opnorm{A}$ is the spectral norm of the matrix $A$. We also show that this algorithm can be used to give faster algorithms for kernel ridge regression. Finally, we show that the sketch size required for our algorithm is essentially optimal for a natural framework of algorithms for ridge regression by proving lower bounds on oblivious sketching matrices for AMM. The sketch size lower bounds for AMM may be of independent interest.
Cliﬀord Circuits can be Properly PAC Learned if and only if RP = NP

Given a dataset of input states, measurements, and probabilities, is it possible to eﬃciently predict the measurement probabilities associated with a quantum circuit? Re-cent work of Caro and Datta [17] studied the problem of PAC learning quantum circuits in an information theoretic sense, leaving open questions of computational eﬃciency. In particular, one candidate class of circuits for which an eﬃcient learner might have been possible was that of Cliﬀord circuits, since the corresponding set of states generated by such circuits, called stabilizer states, are known to be eﬃciently PAC learnable [34]. Here we provide a negative result, showing that proper learning of CNOT circuits is hard for classical learners unless RP = NP . As the classical analogue and subset of Cliﬀord circuits, this naturally leads to a hardness result for Cliﬀord circuits as well. Additionally, we show that if RP = NP then there would exist eﬃcient proper learning algorithms for CNOT and Cliﬀord circuits. By similar arguments, we also ﬁnd that an eﬃcient proper quantum learner for such circuits exists if and only if NP ⊆ RQP .
Superoscillations Made Super Simple

In ordinary circumstances the highest frequency present in a wave is the highest frequency in its Fourier decomposition. It is however possible for there to be a spatial or temporal region of the wave which locally oscillates at a still greater frequency, in a phenomenon known as superoscillation [1]. Superoscillations ﬁnd application in wide range of disciplines [2–4], but at present their generation is based upon constructive approaches which are diﬃcult to implement. Here we address this, exploiting the fact that superoscillations are a product of destructive interference to produce a prescription for generating superoscillations from the superposition of arbitrary waveforms. As a ﬁrst test of the technique, we use it to combine four THz laser ﬁelds generated by periodically poled Lithium Niobate. From this, we are able to predict and observe for the ﬁrst time THz optical superoscillations in the temporal domain. The ability to generate superoscillations in this manner has potential application in a wide range of ﬁelds. It may for example contribute to the experimental realization of the complex pulses required by quantum control, and the generation of attosecond pulses without resorting to nonlinear processes.