Miracle-dz/UniFAR-FTUs huggingface dataset:
SENSAR: A Visual Tool for Intelligent Robots for Collaborative Human-Robot Interaction

Establishing common ground between an intelligent robot and a human requires communication of the robot's intention, behavior, and knowledge to the human to build trust and assure safety in a shared environment. This paper introduces SENSAR (Seeing Everything iN Situ with Augmented Reality), an augmented reality robotic system that enables robots to communicate their sensory and cognitive data in context over the real-world with rendered graphics, allowing a user to understand, correct, and validate the robot's perception of the world. Our system aims to support human-robot interaction research by establishing common ground where the perceptions of the human and the robot align.
A Fast and Reliable Pick-and-Place Application with a Spherical Soft Robotic Arm

This paper presents the application of a learning control approach for the realization of a fast and reliable pick-and-place application with a spherical soft robotic arm. The arm is characterized by a lightweight design and exhibits compliant behavior due to the soft materials deployed. A soft, continuum joint is employed, which allows for simultaneous control of one translational and two rotational degrees of freedom in a single joint. This allows us to axially approach and pick an object with the attached suction cup during the pick-and-place application. A control allocation based on pressure differences and the antagonistic actuator configuration is introduced, allowing decoupling of the system dynamics and simplifying the modeling and control. A linear parameter-varying model is identified, which is parametrized by the attached load mass and a parameter related to the joint stiffness. A gain-scheduled feedback controller is proposed, which asymptotically stabilizes the robotic system for aggressive tuning and over large variations of the parameters considered. The control architecture is augmented with an iterative learning control scheme enabling accurate tracking of aggressive trajectories involving set point transitions of 60 degrees within 0.3 seconds (no mass attached) to 0.6 seconds (load mass attached). The modeling and control approach proposed results in a reliable realization of a pick-and-place application and is experimentally demonstrated.
The SAMI Galaxy Survey: Stellar Populations of Passive Spiral Galaxies in Different Environments

We investigate the stellar populations of passive spiral galaxies as a function of mass and environment, using integral field spectroscopy data from the Sydney-AAO Multi-object Integral field spectrograph Galaxy Survey. Our sample consists of 52 cluster passive spirals and 18 group/field passive spirals, as well as a set of S0s used as a control sample. The age and [Z/H] estimated by measuring Lick absorption line strength indices both at the center and within 1Re do not show a significant difference between the cluster and the field/group passive spirals. However, the field/group passive spirals with log(M⋆/M⊙) ≳ 10.5 show decreasing [α/Fe] along with stellar mass, which is ∼0.1 dex smaller than that of the cluster passive spirals. We also compare the stellar populations of passive spirals with S0s. In the clusters, we find that passive spirals show slightly younger age and lower [α/Fe] than the S0s over the whole mass range. In the field/group, stellar populations show a similar trend between passive spirals and S0s. In particular, [α/Fe] of the field/group S0s tend to be flattening with increasing mass above log(M⋆/M⊙) ≳ 10.5, similar to the field/group passive spirals. We relate the age and [α/Fe] of passive spirals to their mean infall time in phase space; we find a positive correlation, in agreement with the prediction of numerical simulations. We discuss the environmental processes that can explain the observed trends. The results lead us to conclude that the formation of the passive spirals and their transformation into S0s may significantly depend on their environments.
A Bivariate Normal Inverse Gaussian Process with Stochastic Delay: Efficient Simulations and Applications to Energy Markets

ABSTRACT Using the concept of self-decomposable subordinators introduced by Gardini, Sabino, and Sasso, we build a new bivariate Normal Inverse Gaussian process that can capture stochastic delays. In addition, we also develop a novel path simulation scheme that relies on the mathematical connection between self-decomposable Inverse Gaussian laws and Lévy-driven Ornstein–Uhlenbeck processes with Inverse Gaussian stationary distribution. We show that our approach provides an improvement to the existing simulation scheme detailed in Zhang and Zhang, because it does not rely on an acceptance–rejection method. Eventually, these results are applied to the modelling of energy markets and to the pricing of spread options using the proposed Monte Carlo scheme and Fourier techniques.
Template Controllable keywords-to-text Generation

This paper proposes a novel neural model for the understudied task of generating text from keywords. The model takes as input a set of un-ordered keywords, and part-of-speech (POS) based template instructions. This makes it ideal for surface realization in any NLG setup. The framework is based on the encode-attend-decode paradigm, where keywords and templates are encoded first, and the decoder judiciously attends over the contexts derived from the encoded keywords and templates to generate the sentences. Training exploits weak supervision, as the model trains on a large amount of labeled data with keywords and POS based templates prepared through completely automatic means. Qualitative and quantitative performance analyses on publicly available test-data in various domains reveal our system's superiority over baselines, built using state-of-the-art neural machine translation and controllable transfer techniques. Our approach is indifferent to the order of input keywords.
Tight conditional lower bounds for approximating diameter in directed graphs

Among the most fundamental graph parameters is the Diameter, the largest distance between any pair of vertices in a graph. Computing the Diameter of a graph with m edges requires m2−o(1) time under the Strong Exponential Time Hypothesis (SETH), which can be prohibitive for very large graphs, so efficient approximation algorithms for Diameter are desired. There is a folklore algorithm that gives a 2-approximation for Diameter in Õ(m) time (where Õ notation suppresses logarithmic factors). Additionally, a line of work [SODA’96, STOC’13, SODA’14] concludes with a 3/2-approximation algorithm for Diameter in weighted directed graphs that runs in Õ(m3/2) time. For directed graphs, these are the only known approximation algorithms for Diameter. The 3/2-approximation algorithm is known to be tight under SETH: Roditty and Vassilevska W. [STOC’13] proved that under SETH any 3/2−ε approximation algorithm for Diameter in undirected unweighted graphs requires m2−o(1) time, and then Backurs, Roditty, Segal, Vassilevska W. , and Wein [STOC’18] and the follow-up work of Li proved that under SETH any 5/3−ε approximation algorithm for Diameter in undirected unweighted graphs requires m3/2−o(1) time. Whether or not the folklore 2-approximation algorithm is tight, however, is unknown, and has been explicitly posed as an open problem in numerous papers. Towards this question, Bonnet recently proved that under SETH, any 7/4−ε approximation requires m4/3−o(1), only for directed weighted graphs. We completely resolve this question for directed graphs by proving that the folklore 2-approximation algorithm is conditionally optimal. In doing so, we obtain a series of conditional lower bounds that together with prior work, give a complete time-accuracy trade-off that is tight with the three known algorithms for directed graphs. Specifically, we prove that under SETH for any δ>0, a (2k−1/k−δ)-approximation algorithm for Diameter on directed unweighted graphs requires mk/k−1−o(1) time.
An Empirical Study of Visual Features for DNN Based Audio-Visual Speech Enhancement in Multi-Talker Environments

Audio-visual speech enhancement (AVSE) methods use both audio and visual features for the task of speech enhancement and the use of visual features has been shown to be particularly effective in multi-speaker scenarios. In the majority of deep neural network (DNN) based AVSE methods, the audio and visual data are first processed separately using different sub-networks, and then the learned features are fused to uti-lize the information from both modalities. There have been various studies on suitable audio input features and network architectures, however, to the best of our knowledge, there is no published study that has investigated which visual features are best suited for this specific task. In this work, we perform an empirical study of the most commonly used visual features for DNN based AVSE, the pre-processing requirements for each of these features, and investigate their influence on the performance. Our study shows that despite the overall better performance of embedding-based features, their computationally intensive pre-processing makes their use difficult in low resource systems. For such systems, optical flow or raw pixels-based features might be better suited.
Fast 3D Modeling of Anthropomorphic Robotic Hands Based on A Multi-layer Deformable Design

Current anthropomorphic robotic hands mainly focus on improving their dexterity by devising new mechanical structures and actuation systems. However, most of them rely on a single structure/system (e.g., bone-only) and ignore the fact that the human hand is composed of multiple functional structures (e.g., skin, bones, muscles, and tendons). This not only increases the difficulty of the design process but also lowers the robustness and flexibility of the fabricated hand. Besides, other factors like customization, the time and cost for production, and the degree of resemblance between human hands and robotic hands, remain omitted. To tackle these problems, this study proposes a 3D printable multi-layer design that models the hand with the layers of skin, tissues, and bones. The proposed design first obtains the 3D surface model of a target hand via 3D scanning, and then generates the 3D bone models from the surface model based on a fast template matching method. To overcome the disadvantage of the rigid bone layer in deformation, the tissue layer is introduced and represented by a concentric tube based structure, of which the deformability can be explicitly controlled by a parameter. Besides, a low-cost yet effective underactuated system is adopted to drive the fabricated hand. The proposed design is tested with 33 widely used object grasping types, as well as special objects like fragile silken tofu, and outperforms previous designs remarkably. With the proposed design, anthropomorphic robotic hands can be produced fast with low cost, and be customizable and deformable.
Finite-temperature materials modeling from the quantum nuclei to the hot electron regime

Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modelling of realistic thermodynamic conditions. Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations, that can therefore be combined with extensive thermodynamic sampling. We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point, and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost. We demonstrate the calculation of a broad array of bulk, interfacial and defect properties over a temperature range from 100 to 2500 K, modeling also, when needed, the impact of nuclear quantum fluctuations and electronic entropy. The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.
Think Global, Act Local: Gossip and Client Audits in Verifiable Data Structures

In recent years, there has been increasing recognition of the benefits of having services provide auditable logs of data, as demonstrated by the deployment of Certificate Transparency and the development of other transparency projects. Most proposed systems, however, rely on a gossip protocol by which users can be assured that they have the same view of the log, but the few gossip protocols that do exist today are not suited for near-term deployment. Furthermore, they assume the presence of global sets of auditors, who must be blindly trusted to correctly perform their roles, in order to achieve their stated transparency goals. In this paper, we address both of these issues by proposing a gossip protocol and a verifiable registry, Mog, in which users can perform their own auditing themselves. We prove the security of our protocols and demonstrate via experimental evaluations that they are performant in a variety of potential near-term deployments.
Bayesian Reconstruction of Fourier Pairs

In a number of data-driven applications such as detection of arrhythmia, interferometry or audio compression, observations are acquired indistinctly in the time or frequency domains: temporal observations allow us to study the spectral content of signals (e.g., audio), while frequency-domain observations are used to reconstruct temporal/spatial data (e.g., MRI). Classical approaches for spectral analysis rely either on i) a discretisation of the time and frequency domains, where the fast Fourier transform stands out as the \textit{de facto} off-the-shelf resource, or ii) stringent parametric models with closed-form spectra. However, the general literature fails to cater for missing observations and noise-corrupted data. Our aim is to address the lack of a principled treatment of data acquired indistinctly in the temporal and frequency domains in a way that is robust to missing or noisy observations, and that at the same time models uncertainty effectively. To achieve this aim, we first define a joint probabilistic model for the temporal and spectral representations of signals, to then perform a Bayesian model update in the light of observations, thus jointly reconstructing the complete (latent) time and frequency representations. The proposed model is analysed from a classical spectral analysis perspective, and its implementation is illustrated through intuitive examples. Lastly, we show that the proposed model is able to perform joint time and frequency reconstruction of real-world audio, healthcare and astronomy signals, while successfully dealing with missing data and handling uncertainty (noise) naturally against both classical and modern approaches for spectral estimation.
Evolution of the truncated disc and inner hot-flow of GX 339–4

Aims. We study the changes in geometry of the truncated disc and the inner hot-flow of GX 339–4 by analysing the power spectral density (PSD) extracted from six XMM-Newton observations taken at the very end of an outburst. Methods. We developed a theoretical model of the PSD of GX 339–4 in the 0.3–0.7 keV (thermal-reverberation-dominated) and 0.7–1.5 keV (disc-continuum-dominated) energy bands. The model assumes the standard accretion disc to be truncated at a specific radius, inside of which are two distinct hot-flow zones: one spectrally soft and the other spectrally hard. The effects of disc fluctuations and thermal reverberation are taken into account. Results. This model successfully produces the traditional bumpy PSD profiles and provides good fits to the GX 339–4 data. The truncation radius is found to increase from rtrc ∼ 10 to 55rg as the source luminosity decreases, confirming that the truncation radius can be characterized as a function of luminosity. Keeping in mind the large uncertainty in previous measurements of the truncation radius, our values are larger than some obtained from spectroscopic analysis, but smaller than those implied by reverberation lag analysis. Furthermore, the size of two inner hot-flow zones that are spectrally hard and spectrally soft also increases from ∼5 to 27rg and from ∼3 to 26rg, respectively, as the flux decreases. We find that the radial range of the inner hard zone is always larger than the range of the soft hot-flow zone, but by a comparatively small factor of ∼1.1–2.2
PointTransformer for Shape Classification and Retrieval of 3D and ALS Roof PointClouds

Effective feature representation from Airborne Laser Scanning (ALS) point clouds used for urban modeling was challenging until the advent of deep learning and improved ALS techniques. Most deep learning techniques for 3-D point clouds utilize convolutions that assume a uniform input distribution and cannot learn long-range dependencies, leading to some limitations. Recent works have already shown that adding attention on top of these methods improves performance. This raises a question: can attention layers completely replace convolutions? We propose a fully attentional model-PointTransformer for deriving a rich point cloud representation. The model's shape classification and retrieval performance are evaluated on a large-scale urban dataset - RoofN3D and a standard benchmark dataset ModelNet40. Also, the model is tested on various simulated point corruptions to analyze its effectiveness on real datasets. The proposed method outperforms other state-of-the-art models in the RoofN3D dataset, gives competitive results in the ModelNet40 benchmark, and showcases high robustness to multiple point corruptions. Furthermore, the model is both memory and space-efficient without compromising on performance.
Fast Fourier Intrinsic Network

We address the problem of decomposing an image into albedo and shading. We propose the Fast Fourier Intrinsic Network, FFI-Net in short, that operates in the spectral domain, splitting the input into several spectral bands. Weights in FFI-Net are optimized in the spectral domain, allowing faster convergence to a lower error. FFI-Net is lightweight and does not need auxiliary networks for training. The network is trained end-to-end with a novel spectral loss which measures the global distance between the network prediction and corresponding ground truth. FFI-Net achieves state-of-the-art performance on MPI-Sintel, MIT Intrinsic, and IIW datasets.
BERT-JAM: Boosting BERT-Enhanced Neural Machine Translation with Joint Attention

BERT-enhanced neural machine translation (NMT) aims at leveraging BERT-encoded representations for translation tasks. A recently proposed approach uses attention mechanisms to fuse Transformer's encoder and decoder layers with BERT's last-layer representation and shows enhanced performance. However, their method doesn't allow for the flexible distribution of attention between the BERT representation and the encoder/decoder representation. In this work, we propose a novel BERT-enhanced NMT model called BERT-JAM which improves upon existing models from two aspects: 1) BERT-JAM uses joint-attention modules to allow the encoder/decoder layers to dynamically allocate attention between different representations, and 2) BERT-JAM allows the encoder/decoder layers to make use of BERT's intermediate representations by composing them using a gated linear unit (GLU). We train BERT-JAM with a novel three-phase optimization strategy that progressively unfreezes different components of BERT-JAM. Our experiments show that BERT-JAM achieves SOTA BLEU scores on multiple translation tasks.
When Optimizing f-divergence is Robust with Label Noise

We show when maximizing a properly defined $f$-divergence measure with respect to a classifier's predictions and the supervised labels is robust with label noise. Leveraging its variational form, we derive a nice decoupling property for this particular $f$-divergence when label noise presents, where the divergence is shown to be a linear combination of the variational difference defined on the clean distribution and a bias term introduced due to the noise. The above derivation helps us analyze the robustness of this measure for different $f$-divergence functions. With established robustness, this family of $f$-divergence functions arises as useful metrics for the problem of learning with noisy labels, which do not require the specification of the labels' noise rate. When they are possibly not robust, we propose fixes to make them so. In addition to the analytical results, we present thorough experimental studies.
Balanced Crown Decomposition for Connectivity Constraints

We introduce the balanced crown decomposition that captures the structure imposed on graphs by their connected induced subgraphs of a given size. Such subgraphs are a popular modeling tool in various application areas, where the non-local nature of the connectivity condition usually results in very challenging algorithmic tasks. The balanced crown decomposition is a combination of a crown decomposition and a balanced partition which makes it applicable to graph editing as well as graph packing and partitioning problems. We illustrate this by deriving improved kernelization and approximation algorithms for a variety of such problems. In particular, through this structure, we obtain the first constant-factor approximation for the Balanced Connected Partition (BCP) problem, where the task is to partition a vertex-weighted graph into k connected components of approximately equal weight. We derive a 3-approximation for the two most commonly used objectives of maximizing the weight of the lightest component or minimizing the weight of the heaviest component. Further, we show how to compute our balanced crown decomposition very efficiently which as an intermediate result yields an improved running time in O(|V||E|) for the well-known (Weighted) q-Expansion Lemma.
Spectral index of the Galactic foreground emission in the 50–87 MHz range with LEDA

Total power radiometry with individual meter-wave antennas is a potentially effective means to detect the brightness temperature from the 21 cm line of neutral hydrogen during the Cosmic Dawn. In the process, spectra of integrated sky brightness temperature can be used to quantify properties of foreground emission. In this work we analyze a subset of data from the Large-aperture Experiment to Detect the Dark Age (LEDA) and constrain the spectral index β of foreground emission in the northern sky. We correct for the effect of gain pattern chromaticity and compare estimated absolute temperatures with simulations. We estimate variation in β with local sidereal time (LST) using two widely spaced, zenith-directed radiometers during May and December 2018, and January-May 2019. For times in the 9-12.5h LST window when the Sun is not in the Sky and the Galactic Center is at least 20° below the local horizon’ we estimate β=-2.48\pm 0.07 for the first antenna, and β=-2.57+0.12 for the second. These results are consistent with previous measurements of the southern sky. Anomalously heavy rainstorms moved through the observing site in winter 2019. The coincident timing of episodic shifts in the temperature scales of the two radiometers suggests substantial sensitivity to the the changes in the soil moisture profile, though other variables may also be involved. We extend the analysis December/January data, for which soil conditions are optimal, to an almost 24 h LST range finding results in agreement with simulated sky models.