Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing protoplanetary disk evolution in the Chamaeleon II region

Context. Characterizing the evolution of protoplanetary disks is necessary to improve our understanding of planet formation. Constraints on both dust and gas are needed to determine the dominant disk dissipation mechanisms. Aims. We aim to compare the disk dust masses in the Chamaeleon II (Cha II) star-forming region with other regions with ages between 1 and 10 Myr. Methods. We use ALMA band 6 observations (1.3 mm) to survey 29 protoplanetary disks in Cha II. Dust mass estimates are derived from the continuum data. Results. Out of our initial sample of 29 disks, we detect 22 sources in the continuum, 10 in 12CO, 3 in 13CO, and none in C18O (J = 2−1). Additionally, we detect two companion candidates in the continuum and 12CO emission. Most disk dust masses are lower than 10 M⊕, assuming thermal emission from optically thin dust. Including non-detections, we derive a median dust mass of 4.5± 1.5 M⊕ from survival analysis. We compare consistent estimations of the distributions of the disk dust mass and the disk-tostellar mass ratios in Cha II with six other low mass and isolated star-forming regions in the age range of 1–10 Myr: Upper Sco, CrA, IC 348, Cha I, Lupus, and Taurus. When comparing the dust-to-stellar mass ratio, we find that the masses of disks in Cha II are statistically different from those in Upper Sco and Taurus, and we confirm that disks in Upper Sco, the oldest region of the sample, are statistically less massive than in all other regions. Performing a second statistical test of the dust mass distributions from similar mass bins, we find no statistical differences between these regions and Cha II. Conclusions. We interpret these trends, most simply, as a sign of decline in the disk dust masses with time or dust evolution. Different global initial conditions in star-forming regions may also play a role, but their impact on the properties of a disk population is difficult to isolate in star-forming regions lacking nearby massive stars.
If you trust me, I will trust you: the role of reciprocity in human-robot trust

Trust is fundamental in human-human and human-robot interaction. Extensive evidence has shown that trust among humans is sustained by reciprocity, whereas knowledge on reciprocal dynamics of trust in human-robot interaction is very limited. To investigate reciprocity in human-robot trust, we designed a joint task in which a human participant and a humanoid robot made perceptual judgments while signaling their trust in the partner. The robot’s trust was dynamically manipulated along the experiment. Results show that participants were less willing to learn from a robot that was showing high trust in them. However, participants were unwilling to overtly disclose their distrust to the robot if they expected future interactions with it, suggesting the emergence of reciprocity in their overt expression of trust. Importantly, these effects disappeared when participants interacted with a computer. Our findings may have an impact on the design of robots that could effectively collaborate with humans.
Finite-Dimensional Boundary Control of the Linear Kuramoto-Sivashinsky Equation Under Point Measurement With Guaranteed $L^2$-Gain

Finite-dimensional observer-based controller design for PDEs is a challenging problem. Recently, such controllers were introduced for the one dimensional (1D) heat equation, under the assumption that one of the observation or control operators is bounded. This article suggests a constructive method for such controllers for 1D parabolic partial differential equations (PDEs) with both observation and control operators being unbounded. We consider the Kuramoto–Sivashinsky equation under either boundary or in-domain point measurement and boundary actuation in the presence of disturbances in the PDE and measurement. We employ a modal decomposition approach via dynamic extension, using eigenfunctions of a Sturm–Liouville operator. The controller dimension is defined by the number of unstable modes, whereas the observer dimension <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> may be larger. We suggest a direct Lyapunov approach to the full-order closed-loop system, which results in a linear matrix inequality (LMI), for input-to-state stabilization (ISS) and guaranteed <inline-formula><tex-math notation="LaTeX">$L^2$</tex-math></inline-formula>-gain, whose elements and dimension depend on <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>. The value of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> and the decay rate are obtained from the LMI. We prove that the LMI is always feasible provided <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> and the <inline-formula><tex-math notation="LaTeX">$L^2$</tex-math></inline-formula> or ISS gains are large enough, thereby obtaining guarantees for our approach. Moreover, for the case of stabilization, we show that feasibility of the LMI for some <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> implies its feasibility for <inline-formula><tex-math notation="LaTeX">$N+1$</tex-math></inline-formula>. Numerical examples demonstrate the efficiency of the method.
Cutting and Pasting in the Torelli subgroup of Out($F_n$)

Using ideas from 3-manifolds, Hatcher--Wahl defined a notion of automorphism groups of free groups with boundary. We study their Torelli subgroups, adapting ideas introduced by Putman for surface mapping class groups. Our main results show that these groups are finitely generated, and also that they satisfy an appropriate version of the Birman exact sequence.
A direction preserving discretization for computing phase-space densities

Ray flow methods are an efficient tool to estimate vibro-acoustic or electromagnetic energy transport in complex domains at high-frequencies. Here, a Petrov-Galerkin discretization of a phase-space boundary integral equation for transporting wave energy densities on two-dimensional surfaces is proposed. The directional dependence of the energy density is approximated at each point on the boundary in terms of a finite local set of directions propagating into the domain. The direction of propagation can be preserved for transport across multi-component domains when the directions within the local set are inherited from a global direction set. The range of applicability and computational cost of the method will be explored through a series of numerical experiments, including wave problems from both acoustics and elasticity in both single and multi-component domains. The domain geometries considered range from both regular and irregular polygons to curved surfaces, including a cast aluminium shock tower from a Range Rover car.
Global energy budgets in turbulent Couette and Poiseuille flows

Abstract Turbulent plane Poiseuille and Couette flows share the same geometry, but produce their flow rate owing to different external drivers: pressure gradient and shear, respectively. By looking at integral energy fluxes, we pose and answer the question as to which flow performs better at creating flow rate. We define a flow efficiency, which quantifies the fraction of power used to produce flow rate instead of being wasted as a turbulent overhead; effectiveness, instead, describes the amount of flow rate produced by a given power. The work by Gatti et al. (J. Fluid Mech., vol. 857, 2018, pp. 345–373), where the constant power input concept was developed to compare turbulent Poiseuille flows with drag reduction, is here extended to compare different flows. By decomposing the mean velocity field into a laminar contribution and a deviation, analytical expressions are derived which are the energy-flux equivalents of the FIK identity. These concepts are applied to literature data supplemented by a new set of direct numerical simulations, to find that Couette flows are less efficient but more effective than Poiseuille flows. The reason is traced to the more effective laminar component of Couette flows, which compensates for their higher turbulent activity. It is also observed that, when the fluctuating fields of the two flows are fed with the same total power fraction, Couette flows dissipate a smaller percentage of it via turbulent dissipation. A decomposition of the fluctuating field into large and small scales explains this feature: Couette flows develop stronger large-scale structures, which alter the mean flow while contributing less significantly to dissipation.
1.6 Tbps Classical Channel Coexistence With DV-QKD Over Hollow Core Nested Antiresonant Nodeless Fibre (HC-NANF)

We demonstrate for the first time the coexistence of a quantum-channel and 8×200 Gpbs 16-QAM optical channels with launching powers as high as -9dBm/channel in a 2 km HC-NANF. Comparative analysis with single-mode fibre reveals that the quantum-channel could not be sustained at such power-levels.
Projective quantum measurements in a tight-binding chain: Stochastic localization and Zeno effect

We address here the issue of quantum measurement in which the interaction between the system at hand and the measuring apparatus causes a collapse of the wave function or the associated density operator. The analysis is carried out for the prototypical tight-binding chain involving a quantum particle hopping between nearestneighbour sites on a lattice, with the system subject to repeated projective measurements to a given detector site. However, unlike previous work, the measurements are performed at random times. In the absence of measurement, the particle while starting from a site is known to spread out to farther and farther sites as time progresses, thus leading to complete delocalization of the particle in time. In presence of measurements, by using a stochastic Liouville equation approach, we derive exact results for the probability at a given time for the particle to be found on different sites and averaged with respect to different realizations of the dynamics. We consider the representative case in which the time gap between successive measurements are sampled independently from an exponential distribution. The novel result of our findings is the revelation of the striking dynamical effect of Stochastic Localization, whereby the particle has at long times time-independent probabilities for it to be found on different sites. This implies localization of the particle at long times, achieved through classical stochasticity involved with sampling of the random times for measurements. An extreme case of such localization is attained if the measurements are done very frequently, when the particle is at long times completely localized at the detector site ! We also point out the stochastic version of the Zeno effect, whereby the dynamical evolution of a quantum system is arrested through repeated measurements at frequent-enough time intervals. In the wake of recent experiments on projective measurements at random times achieved through randomly-pulsed sequences, our proposition of Stochastic Localization is amenable to experimental realization.
Assembly bias in quadratic bias parameters of dark matter halos from forward modeling

We use the forward modeling approach to galaxy clustering combined with the likelihood from the effective-field theory of large-scale structure to measure assembly bias, i.e. the dependence of halo bias on properties beyond the total mass, in the linear (b1) and second order bias parameters (b2 and bK 2) of dark matter halos in N-body simulations. This is the first time that assembly bias in the tidal bias parameter bK 2 is measured. We focus on three standard halo properties: the concentration c, spin λ, and sphericity s, for which we find an assembly bias signal in bK 2 that is opposite to that in b1. Specifically, at fixed mass, halos that get more (less) positively biased in b1, get less (more) negatively biased in bK 2. We also investigate the impact of assembly bias on the b2(b1) and bK 2(b1) relations, and find that while the b2(b1) relation stays roughly unchanged, assembly bias strongly impacts the bK 2(b1) relation. This impact likely extends also to the corresponding relation for galaxies, which motivates future studies to design better priors on bK 2(b1) for use in cosmological constraints from galaxy clustering data.
Data Poisoning Won't Save You From Facial Recognition

Data poisoning has been proposed as a compelling defense against facial recognition models trained on Web-scraped pictures. Users can perturb images they post online, so that models will misclassify future (unperturbed) pictures. We demonstrate that this strategy provides a false sense of security, as it ignores an inherent asymmetry between the parties: users' pictures are perturbed once and for all before being published (at which point they are scraped) and must thereafter fool all future models -- including models trained adaptively against the users' past attacks, or models that use technologies discovered after the attack. We evaluate two systems for poisoning attacks against large-scale facial recognition, Fawkes (500'000+ downloads) and LowKey. We demonstrate how an"oblivious"model trainer can simply wait for future developments in computer vision to nullify the protection of pictures collected in the past. We further show that an adversary with black-box access to the attack can (i) train a robust model that resists the perturbations of collected pictures and (ii) detect poisoned pictures uploaded online. We caution that facial recognition poisoning will not admit an"arms race"between attackers and defenders. Once perturbed pictures are scraped, the attack cannot be changed so any future successful defense irrevocably undermines users' privacy.
Irreversibility and rate dependence in sheared adhesive suspensions

Zhouyang Ge1,2,∗ Raffaella Martone, Luca Brandt, and Mario Minale3† 1 Department of Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden 2 Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada and 3 Department of Engineering, University of Campania “Luigi Vanvitelli”, Real Casa dell’Annunziata, via Roma 29-81031 Aversa, CE, Italy (Dated: June 29, 2021)
Semi-Supervised Raw-to-Raw Mapping

The raw-RGB colors of a camera sensor vary due to the spectral sensitivity differences across different sensor makes and models. This paper focuses on the task of mapping between different sensor raw-RGB color spaces. Prior work addressed this problem using a pairwise calibration to achieve accurate color mapping. Although being accurate, this approach is less practical as it requires: (1) capturing pair of images by both camera devices with a color calibration object placed in each new scene; (2) accurate image alignment or manual annotation of the color calibration object. This paper aims to tackle color mapping in the raw space through a more practical setup. Specifically, we present a semi-supervised raw-to-raw mapping method trained on a small set of paired images alongside an unpaired set of images captured by each camera device. Through extensive experiments, we show that our method achieves better results compared to other domain adaptation alternatives in addition to the single-calibration solution. We have generated a new dataset of raw images from two different smartphone cameras as part of this effort. Our dataset includes unpaired and paired sets for our semi-supervised training and evaluation.
Exact description of quantum stochastic models as quantum resistors

We study the transport properties of generic out-of-equilibrium quantum systems connected to fermionic reservoirs. We develop a new method, based on an expansion of the current in terms of the inverse system size and out of equilibrium formulations such as the Keldysh technique and the Meir-Wingreen formula. Our method allows a simple and compact derivation of the current for a large class of systems showing diffusive/ohmic behavior. In addition, we obtain exact solutions for a large class of quantum stochastic Hamiltonians (QSHs) with time and space dependent noise, using a self consistent Born diagrammatic method in the Keldysh representation. We show that these QSHs exhibit diffusive regimes which are encoded in the Keldysh component of the single particle Green's function. The exact solution for these QSHs models confirms the validity of our system size expansion ansatz, and its efficiency in capturing the transport properties. We consider in particular three fermionic models: i) a model with local dephasing ii) the quantum simple symmetric exclusion process model iii) a model with long-range stochastic hopping. For i) and ii) we compute the full temperature and dephasing dependence of the conductance of the system, both for two- and four-points measurements. Our solution gives access to the regime of finite temperature of the reservoirs which could not be obtained by previous approaches. For iii), we unveil a novel ballistic-to-diffusive transition governed by the range and the nature (quantum or classical) of the hopping. As a by-product, our approach equally describes the mean behavior of quantum systems under continuous measurement.
Graph Convolutional Memory for Deep Reinforcement Learning

: Solving partially-observable Markov decision processes (POMDPs) is critical when applying deep reinforcement learning (DRL) to real-world robotics problems, where agents have an incomplete view of the world. We present graph convolutional memory (GCM) 1 for solving POMDPs using deep reinforcement learning. Unlike recurrent neural networks (RNNs) or transformers, GCM embeds domain-speciﬁc priors into the memory recall process via a knowledge graph. By encapsulating priors in the graph, GCM adapts to speciﬁc tasks but remains applicable to any DRL task. Using graph convolutions, GCM extracts hierarchical graph features, analogous to image features in a convolutional neural network (CNN). We show GCM outperforms long short-term memory (LSTM), gated transformers for reinforcement learning (GTrXL), and differentiable neural computers (DNCs) on control, long-term non-sequential recall, and 3D navigation tasks while using signiﬁcantly fewer parameters.
Fractal Pyramid Networks

We propose a new network architecture, the Fractal Pyramid Networks (PFNs) for pixel-wise prediction tasks as an alternative to the widely used encoder-decoder structure. In the encoder-decoder structure, the input is processed by an encoding-decoding pipeline that tries to get a semantic large-channel feature. Different from that, our proposed PFNs hold multiple information processing pathways and encode the information to multiple separate small-channel features. On the task of self-supervised monocular depth estimation, even without ImageNet pretrained, our models can compete or outperform the state-of-the-art methods on the KITTI dataset with much fewer parameters. Moreover, the visual quality of the prediction is significantly improved. The experiment of semantic segmentation provides evidence that the PFNs can be applied to other pixel-wise prediction tasks, and demonstrates that our models can catch more global structure information.
A Photonic-Circuits-Inspired Compact Network: Toward Real-Time Wireless Signal Classification at the Edge

Machine learning (ML) methods are ubiquitous in wireless communication systems and have proven powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation classification, and cognitive radio. However, the large size of ML models can make them difficult to implement on edge devices for latency-sensitive downstream tasks. In wireless communication systems, ML data processing at a sub-millisecond scale will enable real-time network monitoring to improve security and prevent infiltration. In addition, compact and integratable hardware platforms which can implement ML models at the chip scale will find much broader application to wireless communication networks. Toward real-time wireless signal classification at the edge, we propose a novel compact deep network that consists of a photonic-hardware-inspired recurrent neural network model in combination with a simplified convolutional classifier, and we demonstrate its application to the identification of RF emitters by their random transmissions. With the proposed model, we achieve 96.32% classification accuracy over a set of 30 identical ZigBee devices when using 50 times fewer training parameters than an existing state-of-the-art CNN classifier. Thanks to the large reduction in network size, we demonstrate real-time RF fingerprinting with 0.219 ms latency using a small-scale FPGA board, the PYNQ-Z1.
Homotopically unbounded disks for generic surface diffeomorphisms

In this paper we consider closed orientable surfaces S of positive genus and Cr-diffeomorphisms f : S → S isotopic to the identity (r ≥ 1). The main objective is to study periodic open topological disks which are homotopically unbounded (i.e. which lift to unbounded connected sets in the universal covering). We show that these disks are not uncommon, and are related to important dynamical phenomena. We also study the dynamics on these disks under certain generic conditions. Our first main result implies that for the torus (or for arbitrary surfaces, with an additional condition) if the rotation set of a map has nonempty interior and is not locally constant, then the map is Cr-accumulated by diffeomorphisms exhibiting periodic homotopically unbounded disks. Our second result shows that Cr-generically, if the rotation set has nonempty interior (plus an additional hypothesis if the genus of S is greater than 1) a maximal periodic disk which is unbounded and has a rational prime ends rotation number must be the basin of some compact attractor or repeller contained in the disk. As a byproduct we obtain results describing certain periodic components of the complement of the closure of stable or unstable manifolds of a periodic orbit in the Cr-generic setting.
End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021

This paper describes the submission to the IWSLT 2021 offline speech translation task by the UPC Machine Translation group. The task consists of building a system capable of translating English audio recordings extracted from TED talks into German text. Submitted systems can be either cascade or end-to-end and use a custom or given segmentation. Our submission is an end-to-end speech translation system, which combines pre-trained models (Wav2Vec 2.0 and mBART) with coupling modules between the encoder and decoder, and uses an efficient fine-tuning technique, which trains only 20% of its total parameters. We show that adding an Adapter to the system and pre-training it, can increase the convergence speed and the final result, with which we achieve a BLEU score of 27.3 on the MuST-C test set. Our final model is an ensemble that obtains 28.22 BLEU score on the same set. Our submission also uses a custom segmentation algorithm that employs pre-trained Wav2Vec 2.0 for identifying periods of untranscribable text and can bring improvements of 2.5 to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the given segmentation.