Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Trajectory Tracking and Payload Delivery of a Quadrotor Under Multiple State Constraints

With quadrotors becoming immensely popular in applications such as relief operations, infrastructure maintenance etc., a key control design challenge arises when the quadrotor has to manoeuvre through constrained spaces during various operational scenarios: for example, inspecting a pipeline within predefined velocity and space, dropping relief material at a precise location under tight spaces etc., under the face of parametric uncertainties and external disturbances. To tackle such scenarios, a controller needs to ensure a predefined tracking accuracy so as not to violate the constraints while simultaneously tackling uncertainties and disturbances. However, state-of-the-art controllers dealing with constrained system motion are either not applicable for an underactuated system like quadrotor, or cannot tackle system uncertainties under full state constraints. This work attempts to fill such a gap in literature by designing Barrier Lyapunov Function (BLF) based robust controllers to satisfy multiple state-constraints while simultaneously negotiating parametric uncertainties and external disturbances. The superiority of the BLF control method over a typical unconstrained controller is demonstrated, followed by a robust control design to satisfy position and orientation constraints on quadrotor dynamics. Finally, full state-constraints on a quadrotor(i.e., constraints on the position, orientation, linear velocity and angular velocity) are satisfied with robust control. For each control design, the closed-loop system stability is studied analytically and the efficacy of the design is validated extensively either via realistic simulation scenarios or via experiments performed on a real quadrotor.
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making

In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making by another agent. Signalling is intimately connected to time and timing. In service of generating and receiving signals, humans and other animals are known to represent time, determine time since past events, predict the time until a future stimulus, and both recognize and generate patterns that unfold in time. We investigate how different temporal processes impact coordination and signalling between learning agents by introducing a partially observable decision-making domain we call the Frost Hollow. In this domain, a prediction learning agent and a reinforcement learning agent are coupled into a two-part decision-making system that works to acquire sparse reward while avoiding time-conditional hazards. We evaluate two domain variations: machine agents interacting in a seven-state linear walk, and human-machine interaction in a virtual-reality environment. Our results showcase the speed of learning for Pavlovian signalling, the impact that different temporal representations do (and do not) have on agent-agent coordination, and how temporal aliasing impacts agent-agent and human-agent interactions differently. As a main contribution, we establish Pavlovian signalling as a natural bridge between fixed signalling paradigms and fully adaptive communication learning between two agents. We further show how to computationally build this adaptive signalling process out of a fixed signalling process, characterized by fast continual prediction learning and minimal constraints on the nature of the agent receiving signals. Our results therefore suggest an actionable, constructivist path towards communication learning between reinforcement learning agents.
Quantum causality constraints on kappa-Minkowski space-time

The κ-Minkoswki space-time provides a (quantum) noncommutative deformation of the usual Minkowski space-time. However, a notion of causality is difficult to be defined in such a space with noncommutative time. In this paper, we define a notion of causality on a (1 + 1)-dimensional κ-Minkoswki space-time using the more general framework of Lorentzian noncommutative geometry. We show that this notion allows specific causal relations, but limited by a general constraint which is a quantum generalization of the traditional speed of light limit.
Black Mirror: The impact of rotational broadening on the search for reflected light from 51 Pegasi b with high resolution spectroscopy

The extreme contrast ratios between stars and their planets at optical wavelengths make it challenging to isolate the light reflected by exoplanet atmospheres. Yet, these reflective properties reveal key processes occurring in the atmospheres, and they also span wavelengths that include the potential O$_2$ biosignature. High resolution cross-correlation spectroscopy (HRCCS) offers a robust avenue for developing techniques to extract exoplanet reflection spectra. We aimed to extract the optical reflected light spectrum of the non-transiting hot Jupiter 51 Peg b by adapting techniques designed to remove tellurics in infrared HRCCS to instead remove optical stellar lines. Importantly, we investigated the so far neglected impact of the broadening of the reflected host star spectrum due to the difference between the stellar rotation and the planet's orbital velocity. We used 484, R=115000 optical spectra of 51 Peg b from HARPS-N and HARPS, which we aligned to the exact stellar rest frame in order to effectively remove the contaminating host star. However, some stellar residuals remained, likely due to stellar activity. We cross-correlated with an appropriately broadened synthetic stellar model to search for the planet's Doppler-shifting spectrum. We detect no significant reflected light from 51 Peg b and report a S/N=3 upper limit on the contrast ratio of 76.0 ppm (7.60x10$^{-5}$) when including broadening, and 24.0 ppm (2.40x10$^{-5}$) without. These upper limits rule out radius and albedo combinations of previously claimed detections. Broadening can significantly impact the ability of HRCCS to extract reflected light spectra and must be considered when determining the contrast ratio, radius, and albedo of the planet. Asynchronous systems (Prot,$_{\star}\ne$ Porb) are most affected, including most hot Jupiters as well as Earth-size planets in the traditional habitable zones of some M-dwarfs.
Axion-photon multimessenger astronomy with giant flares

We treat prospects for multimessenger astronomy with giant ﬂares (GFs), a rare transient event featured by magnetars that can be as luminous as a hundred of the brightest supernovae ever observed. The beamed photons could correlate with an axion counterpart via resonant conversion in the magnetosphere. In a realistic parameter space, we ﬁnd that the sensitivity limit to galactic GFs for currently viable experiments is g φγ (cid:38) several × 10 − 13 GeV − 1 & g φe (cid:38) few × 10 − 12 . We rule out the compatibility of axion ﬂares with the recent XENON1T excess only due to the time persistence of the signal.
Multistage smoothing based multistep pulse compressor for ultrahigh peak power lasers

Ultrahigh peak power lasers are important scientific tools for frontier laser-physics researches, in which both the peak power improvement and operating safety are very important meanwhile limited by the damage threshold and size of compression gratings currently. Based on a recent reported method “multistep pulse compressor (MPC)”, a multistage smoothing based MPC (MS-MPC) is proposed here to further improve the running safety, operating convenience, and simplify the whole setup of the MPC. In this optimized design, the beam smoothing is not simply executed in the pre-compressor or maincompressor, but separated into multistage. Then, it can protect important optics in every stage directly and reduce the executing difficult of typical MPC at the same time. The prism pair based pre-compressor will induce suitable spatial dispersion which is easier to be achieved and enough to protect the first grating directly. At the same time, the asymmetric four-grating compressor (AFGC) will also induce spatial dispersion to further smooth the laser beam which helps to protect the last grating directly. In this way, 10s-100s PW lasers can be compressed by using current available optics with improved operating safety owing to remove random spatial intensity modulations. Furthermore, an additional beam smoothing stage can be added before the main amplifier to protect the biggest amplification crystal away from damage. This MS-MPC optical design can be easily extended to be used in all exist PW laser facilities to improve their potential compressed pulse energy and running safety. © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
A Negotiating Strategy for a Hybrid Goal Function in Multilateral Negotiation

In various multi-agent negotiation settings, a negotiator's utility depends, either partially or fully, on the sum of negotiators' utilities (i.e., social welfare). While the need for effective negotiating-agent designs that take into account social welfare has been acknowledged in recent work, and even established as a category in automated negotiating agent competitions, very few designs have been proposed to date. In this paper, we present the design principles and results of an extensive evaluation of agent HerbT+, a negotiating agent aiming to maximize a linear tradeoff between individual and social welfare. Our evaluation framework relies on the automated negotiating agents competition (ANAC) and includes a thorough comparison of performance with the top 15 agents submitted between 2015-2018 based on negotiations involving 63 agents submitted to these competitions. We find that, except for a few minor exceptions, when social-welfare plays a substantial role in the agent's goal function, our agent outperforms all other tested designs.
Structure with Semantics: Exploiting Document Relations for Retrieval

Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship between documents can beneﬁt the retrieval mechanism by addressing semantic gaps. However, incorporating these relationships requires tractable mechanisms that balance structure with semantics and take advantage of the prevalent pre-train/ﬁne-tune paradigm. We propose here a holistic approach to learning document representations by integrating intra-document content with inter-document relations. Our deep metric learning solution analyzes the complex neighborhood structure in the relationship network to efﬁciently sample similar/dissimilar document pairs and deﬁnes a novel quintuplet loss function that simultaneously encourages document pairs that are semantically relevant to be closer and structurally unrelated to be far apart in the representation space. Furthermore, the separation margins between the documents are varied ﬂexibly to encode the heterogeneity in relationship strengths. The model is fully ﬁne-tunable and natively supports query projection during inference. We demonstrate that it outperforms competing methods on multiple datasets for document retrieval tasks.
Searching for TESS Photometric Variability of Possible JWST Spectrophotometric Standard Stars

We use data from the Transiting Exoplanet Survey Satellite (TESS) to search for, and set limits on, optical to near-infrared photometric variability of the well-vetted, candidate James Webb Space Telescope (JWST) spectrophotometric standards. Our search of 37 of these candidate standards has revealed measurable periodic variability in 15 stars. The majority of those show variability that is less than half a percent; however, four stars are observed to vary photometrically, from minimum to maximum flux, by more than 1% (the G dwarf HD 38949 and three fainter A dwarfs). Variability of this size would likely impact the error budget in the spectrophotometric calibration of the science instruments aboard JWST. For the 22 candidate standards with no detected variability, we report upper limits on the observed changes in flux. Despite some systematic noise, all stars brighter than 12th magnitude in the TESS band show a 3σ upper limit on the total change in brightness of less than half a percent on timescales between an hour and multiple weeks, empirically establishing their suitability as spectrophotometric standards. We further discuss the value and limits of high-cadence, high-precision photometric monitoring with TESS as a tool to vet the suitability of stars to act as spectrophotometric standards.
Performance of Load Balancers With Bounded Maximum Queue Length in Case of Non-Exponential Job Sizes

In large-scale distributed systems, balancing the load in an efficient way is crucial in order to achieve low latency. Recently, some load balancing policies have been suggested which are able to achieve a bounded maximum queue length in the large-scale limit. However, these policies have thus far only been studied in case of exponential job sizes. As job sizes are more variable in real systems, we investigate how the performance of these policies (and in particular the value of these bounds) is impacted by the job size distribution. We present a unified analysis which can be used to compute the bound on the queue length in case of phase-type distributed job sizes for four load balancing policies. We find that in most cases, the bound on the maximum queue length can be expressed in closed form. In addition, we obtain job size (in)dependent bounds on the expected response time. Our methodology relies on the use of the cavity process. That is, we conjecture that the cavity process captures the behaviour of the real system as the system size grows large. For each policy, we illustrate the accuracy of the cavity process by means of simulation.
Learning Logic Programs From Noisy Failures

Inductive Logic Programming (ILP) is a form of machine learning (ML) which in contrast to many other state of the art ML methods typically produces highly interpretable and reusable models. However, many ILP systems lack the ability to naturally learn from any noisy or partially missclassified training data. We introduce the relaxed learning from failures approach to ILP, a noise handling modification of the previously introduced learning from failures (LFF) approach [13] which is incapable of handling noise. We additionally introduce the novel Noisy Popper ILP system which implements this relaxed approach and is a modification of the existing Popper system [13]. Like Popper, Noisy Popper takes a generatetest-constrain loop to search its hypothesis space wherein failed hypotheses are used to construct hypothesis constraints. These constraints are used to prune the hypothesis space, making the hypothesis search more efficient. However, in the relaxed setting, constraints are generated in a more lax fashion as to avoid allowing noisy training data to lead to hypothesis constraints which prune optimal hypotheses. Constraints unique to the relaxed setting are generated via hypothesis comparison. Additional constraints are generated by weighing the accuracy of hypotheses against their sizes to avoid overfitting through an application of the minimum description length. We support this new setting through theoretical proofs as well as experimental results which suggest that Noisy Popper improves the noise handling capabilities of Popper but at the cost of overall runtime efficiency.
Multiaxis nose-pointing-and-shooting in a biomimetic morphing-wing aircraft

Modern high-performance combat aircraft exceed conventional flight-envelope limits on maneuverability through the use of thrust vectoring, and so achieve supermaneuverability. With ongoing development of biomimetic unmanned aerial vehicles (UAVs), the potential for supermaneuverability through biomimetic mechanisms becomes apparent. So far, this potential has not been well studied: biomimetic UAVs have not yet been shown to be capable of any forms of classical supermaneuverability, as are available to thrust-vectored aircraft. Here we show this capability, by demonstrating how biomimetic morphing-wing UAVs can perform sophisticated slow-timescale nose-pointing-and-shooting (NPAS). Nonlinear flight-dynamics analysis is used to characterize the extent and stability of the multidimensional space of aircraft trim states that arises from biomimetic morphing. Navigating this trim space provides an effective model-based guidance strategy for generating open-loop NPAS maneuvers in simulation. Our results demonstrate the capability of biomimetic aircraft for air combat-relevant supermaneuverability, and provide strategies for the exploration, characterization, and guidance of further forms of classical and nonclassical supermaneuverability in such aircraft.
Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition

The combination of a deep neural network (DNN) -based speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end is a widely used approach to implement overlapping speech recognition. However, the SE front-end generates processing artifacts that can degrade the ASR performance. We previously found that such performance degradation can occur even under fully overlapping conditions, depending on the signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR). To mitigate the degradation, we introduced a rule-based method to switch the ASR input between the enhanced and observed signals, which showed promising results. However, the rule’s optimality was unclear because it was heuristically designed and based only on SIR and SNR values. In this work, we propose a DNN-based switching method that directly estimates whether ASR will perform better on the enhanced or observed signals. We also introduce soft-switching that computes a weighted sum of the enhanced and observed signals for ASR input, with weights given by the switching model’s output posteriors. The proposed learning-based switching showed performance comparable to that of rule-based oracle switching. The soft-switching further improved the ASR performance and achieved a relative character error rate reduction of up to 23 % as compared with the conventional method.
LOKO: Localization-Aware Roll-Out Planning for Future Mobile Networks

The roll-out phase of the next generation of mobile networks (5G) has started and operators are required to devise deployment solutions while pursuing localization accuracy maximization. Enabling location-based services is expected to be a unique selling point for service providers now able to deliver critical mobile services, e.g., autonomous driving, public safety, remote operations. In this paper, we propose a novel roll-out base station placement solution that, given a Throughput-Positioning Ratio (TPR) target, selects the location of new-generation base stations (among available candidate sites) such that the throughput and localization accuracy are jointly maximized. Moving away from the canonical position error bound (PEB) analysis, we develop a realistic framework in which each positioning measurement is affected by errors depending upon the actual wireless channel between the measuring base station and the target device. Our solution, referred to as LOKO, is a fast-converging algorithm that can be readily applied to current 5G (or future) roll-out processes. LOKO is validated by means of an exhaustive simulation campaign considering real existing deployments of a major European network operator as well as synthetic scenarios.
Old and new results on density of stable mappings

Density of stable maps is the common thread of this paper. We review Whitney’s contribution to singularities of differentiable mappings and Thom-Mather theories on C∞ and C0-stability. Infinitesimal and algebraic methods are presented in order to prove Theorem A and Theorem B on density of proper stable and topologically stable mappings f : N → P . Theorem A states that the set of proper stable maps is dense in the set of all proper maps from N to P, if and only if the pair (n, p) is in nice dimensions, while Theorem B shows that density of topologically stable maps holds for any pair (n, p). A short review of results by du Plessis and Wall on the range in which proper smooth mappings are C1stable is given. A Thom-Mather map is a topologically stable map f : N → P whose associated k-jet map j k f : N → P is transverse to the Thom-Mather stratification in J (N, P). We give a detailed description of Thom-Mather maps for pairs (n, p) in the boundary of the nice dimensions. The main open question on density of stable mappings is to determine the pairs (n, p) for which Lipschitz stable mappings are dense. We discuss recent results by Nguyen, Ruas and Trivedi on this subject, formulating conjectures for the density of Lipschitz stable mappings in the boundary of the nice dimensions. At the final section, Damon’s results relatingA-classification of map-germs and KV classification of sections of the discriminant V = Δ(F) of a stable unfolding of f are reviewed and open problems are discussed. Maria Aparecida Soares Ruas Instituto de Ciências Matemáticas e de Computação USP, Av. Trabalhador são-carlense, 400, 13566-590 São Carlos SP, Brazil, e-mail: maasruas@icmc.usp. br 1 ar X iv :2 20 1. 03 88 8v 1 [ m at h. D S] 1 1 Ja n 20 22
Communication Compression for Decentralized Nonconvex Optimization

This paper considers decentralized nonconvex optimization with the cost functions being distributed over agents. Noting that information compression is a key tool to reduce the heavy communication load for decentralized algorithms as agents iteratively communicate with neighbors, we propose three decentralized primal–dual algorithms with compressed communication. The ﬁrst two algorithms are applicable to a general class of compressors with bounded relative compression error and the third algorithm is suitable for two general classes of compressors with bounded absolute compression error. We show that the proposed decentralized algorithms with compressed communication have comparable convergence properties as state-of-the-art algorithms without communication compression. Speciﬁcally, we show that they can ﬁnd ﬁrst-order stationary points with sublinear convergence rate O (1 /T ) when each local cost function is smooth, where T is the total number of iterations, and ﬁnd global optima with linear convergence rate under an additional condition that the global cost function satisﬁes the Polyak–Łojasiewicz condition. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.