Miracle-dz/UniFAR-FTUs huggingface dataset:
One-Shot Observation Learning Using Visual Activity Features

Observation learning is the process of learning a task by observing an expert demonstrator. Our principal contribution is a one-shot learning method for robot manipulation tasks in which only a single demonstration is required. The key idea is to encode the demonstration in an activity space defined as part of a previously trained activity classifier. The distance between this encoding and equivalent encodings from trials of a robot performing the same task provides a reward function supporting iterative learning of task completion by the robotic manipulator. We use reinforcement learning for experiments with a simulated robotic manipulator, and stochastic trajectory optimisation for experiments with a real robotic manipulator. We show that the proposed method can be used to learn tasks from a single demonstration under varying viewpoint of observation, object properties, scene background and morphology of the manipulator. Videos of all results, including demonstrations, can be found on: this https URL
Analysis of a Century’s Worth of AR Scorpii Photometry from DASCH and ASAS-SN

AR Scorpii (AR Sco) is a binary star system containing the only known white dwarf (WD) pulsar. Previously reported photometric data sets only provide coverage back to 2005, but we extend the observational baseline for AR Sco back to the beginning of the 20th century by analyzing observations from the Digital Access to a Sky Century at Harvard project (DASCH). We find that the orbital waveform of AR Sco remained constant across that baseline with no significant deviations from its average brightness. This result strongly suggests that the absence of accretion in modern observations is a long-term feature of AR Sco. Additionally, the DASCH light curve provides an opportunity to test an earlier prediction that an obliquity of the WD would result in a precessional period observable in long-term studies of the orbital light curve. The DASCH observations do not indicate the presence of such a period, and we show that previous, inconclusive tests of this hypothesis were insensitive to the existence of a precessional period. Furthermore, the long DASCH baseline enables us to constrain the rate of change of the orbital frequency to , constraining the efficacy of magnetic braking as a mechanism of angular-momentum loss in this system. Finally, we discuss how the combination of the orbital waveform’s stability, high amplitude, and short period should make it possible to identify additional WD pulsars in all-sky survey data.
HydroSyMBA: A 1D Hydrocode Coupled with an N-body Symplectic Integrator

The numerical modeling of coexisting circumplanetary disks/rings and satellites is particularly challenging because each part of the system requires a very different approach. Disks are generally well represented by a fluid-like dense medium, whose evolution can be calculated by a hydrocode. On the other hand, the orbital evolution of satellites is generally performed using N-body integrators. We have developed a new numerical model that combines a one-dimensional hydrocode with the N-body integrator SyMBA. The disk evolves due to its viscosity, and resonant torques from satellites. The latter is applied to the satellites as an additional “kick” to their accelerations. The integrator also includes the ability to spawn new moonlets at the disk’s outer edge if the latter expands beyond a material-dependent Roche limit, as well as the effects of tidal dissipation in the planet and/or the satellite on the satellite orbits. The resulting integrator allows one to accurately model the evolution of an inner circumplanetary disk, and the formation of satellites by accumulation of disk material, all within a single self-consistent framework. Potential applications include the formation of Earth’s Moon, the evolution of the inner Saturn system, the Martian and uranian moons, and compact exoplanetary systems.
Bayesian Spatial Clustering of Extremal Behavior for Hydrological Variables

Abstract To address the need for efficient inference for a range of hydrological extreme value problems, spatial pooling of information is the standard approach for marginal tail estimation. We propose the first extreme value spatial clustering methods which account for both the similarity of the marginal tails and the spatial dependence structure of the data to determine the appropriate level of pooling. Spatial dependence is incorporated in two ways: to determine the cluster selection and to account for dependence of the data over sites within a cluster when making the marginal inference. We introduce a statistical model for the pairwise extremal dependence which incorporates distance between sites, and accommodates our belief that sites within the same cluster tend to exhibit a higher degree of dependence than sites in different clusters. By combining the models for the marginal tails and the dependence structure, we obtain a composite likelihood for the joint spatial distribution. We use a Bayesian framework which learns about both the number of clusters and their spatial structure, and that enables the inference of site-specific marginal distributions of extremes to incorporate uncertainty in the clustering allocation. The approach is illustrated using simulations, the analysis of daily precipitation levels in Norway and daily river flow levels in the UK. Code and data for the simulation study and river flow example are available in the online supplementary materials.
Partial reset: An inexpensive design for testability approach

A design for testability (DFT) method called partial reset is studied. Reset lines are added to a subset of the flip-flops. partial reset improves the fault coverage of sequential circuit, on the average, by 15% over the fault coverage of the original circuit. This approach has lower overhead in terms of test application time and hardware area when compared to scan techniques. This method has been tested on the 1989 ISCAS sequential benchmark circuits and favorable results have been obtained. <<ETX>>
On monic abelian cubics

In this paper, we prove the assertion that the number of monic cubic polynomials $F(x) = x^3 + a_2 x^2 + a_1 x + a_0$ with integer coefficients and irreducible, Galois over ${\mathbb {Q}}$ satisfying $\max \{|a_2|, |a_1|, |a_0|\} \leq X$ is bounded from above by $O(X (\log X)^2)$. We also count the number of abelian monic binary cubic forms with integer coefficients up to a natural equivalence relation ordered by the so-called Bhargava–Shankar height. Finally, we prove an assertion characterizing the splitting field of 2-torsion points of semi-stable abelian elliptic curves.
Eddy Saturation of the Southern Ocean: A Baroclinic Versus Barotropic Perspective

“Eddy saturation” is the regime in which the total time‐mean volume transport of an oceanic current is relatively insensitive to the wind stress forcing and is often invoked as a dynamical description of Southern Ocean circulation. We revisit the problem of eddy saturation using a primitive equations model in an idealized channel setup with bathymetry. We apply only mechanical wind stress forcing; there is no diapycnal mixing or surface buoyancy forcing. Our main aim is to assess the relative importance of two mechanisms for producing eddy‐saturated states: (i) the commonly invoked baroclinic mechanism that involves the competition of sloping isopycnals and restratification by production of baroclinic eddies and (ii) the barotropic mechanism that involves production of eddies through lateral shear instabilities or through the interaction of the barotropic current with bathymetric features. Our results suggest that the barotropic flow component plays a crucial role in determining the total volume transport.
Radial Velocities of Low-mass Candidate TWA Members

Nearby young moving groups provide unique samples of similar age stars for testing the evolution of physical properties. Incomplete and/or incorrect group membership classifications reduce the usefulness of the group, which we assume to be coeval. With near-infrared spectra of two candidate members of the TW Hya Association, 2MASS J12354615−4115531 (TWA 46) and 2MASS J12371238−4021480 (TWA 47), we test their membership by adding radial velocity measurements to the literature. We find that 2MASS J12354615−4115531 is a close spectroscopic binary system with a center-of-mass radial velocity of −6.5 ± 3.9 km s−1. This radial velocity and a Gaia parallax produces a TW Hydra association (TWA) membership probability of 41.9% using the Banyan Σ tool for 2MASS J12354615−4115531. The spectrum of 2MASS J12371238−4021480 shows that it appears to be a single star with a radial velocity consistent with the TW Hya Association and a membership probability of 99.5%. The reduced probability of TWA 46 as a true member of TWA highlights the importance of high-resolution, near-infrared spectra in validating low-mass moving group members.
On Distributed Estimation in Hierarchical Power Constrained Wireless Sensor Networks

We consider distributed estimation of a random source in a hierarchical power constrained wireless sensor network. Sensors within each cluster send their measurements to a cluster head (CH). CHs optimally fuse the received signals and transmit to the fusion center (FC) over orthogonal fading channels. To enable channel estimation at the FC, CHs send pilots, prior to data transmission. We derive the mean square error (MSE) corresponding to the linear minimum mean square error (LMMSE) estimator of the source at the FC, and obtain the Bayesian Cramér-Rao bound (CRB). Our goal is to find (i) the optimal training power, (ii) the optimal power that sensors in a cluster spend to transmit their amplified measurements to their CH, and (iii) the optimal weight vector employed by each CH for its linear signal fusion, such that the MSE is minimized, subject to a network power constraint. To untangle the performance gain that optimizing each set of these variables provide, we also analyze three special cases of the original problem, where in each special case, only two sets of variables are optimized across clusters. We define three factors that allow us to quantify the effectiveness of each power allocation scheme in achieving an MSE-power tradeoff that is close to that of the Bayesian CRB. Combining the information gained from the factors and Bayesian CRB with our computational complexity analysis provides the system designer with quantitative complexity-versus-MSE improvement tradeoffs offered by different power allocation schemes.
Quantifying impacts of the drought 2018 on European ecosystems in comparison to 2003

Abstract. In recent decades, an increasing persistence of atmospheric circulation patterns has been observed. In the course of the associated long-lasting anticyclonic summer circulations, heat waves and drought spells often coincide, leading to so-called hotter droughts. Previous hotter droughts caused a decrease in agricultural yields and increase in tree mortality, and thus, had a remarkable effect on carbon budgets and negative economic impacts. Consequently, a quantification of ecosystem responses to hotter droughts and a better understanding of the underlying mechanisms is crucial. In this context, the European hotter drought of the year 2018 may be considered as a key event. As a first step towards the quantification of its causes and consequences, we here assess anomalies of atmospheric circulation patterns, temperature loads, and climatic water balance as potential drivers of ecosystem responses which are quantified by remote sensing using the MODIS vegetation indices NDVI and EVI. To place the drought of 2018 within a climatological context, we compare its climatic features and remotely sensed ecosystem response with the extreme hot drought of 2003. 2018 was characterized by a climatic dipole, featuring extremely hot and dry weather conditions north of the Alps but comparably cool and moist conditions across large parts of the Mediterranean. Analysing ecosystem response of five dominant land-cover classes, we found significant positive effects of April–July climatic water balance on ecosystem productivity. Negative drought impacts appeared to affect a larger area and to be significantly stronger in 2018 compared to 2003. Moreover, we found a significantly higher sensitivity of pastures and arable land to climatic water balance compared to forests in both years. The stronger coupling and higher sensitivity of ecosystem response in 2018 we explain by the prevailing climatic dipole: while the generally water-limited ecosystems of the Mediterranean experienced above-average climatic water balance, the less drought-adapted ecosystems of Central and Northern Europe experienced a record hot drought. In conclusion, this study quantifies the drought of 2018 as a yet unprecedented event, outlines hotspots of drought-impacted areas in 2018 which should be given particular attention in follow-up studies, and provides valuable insights into the heterogeneous responses of the dominant European ecosystems to hotter drought.
Exploring Model-based Planning with Policy Networks

Model-based reinforcement learning (MBRL) with model-predictive control or online planning has shown great potential for locomotion control tasks in terms of both sample efficiency and asymptotic performance. Despite their initial successes, the existing planning methods search from candidate sequences randomly generated in the action space, which is inefficient in complex high-dimensional environments. In this paper, we propose a novel MBRL algorithm, model-based policy planning (POPLIN), that combines policy networks with online planning. More specifically, we formulate action planning at each time-step as an optimization problem using neural networks. We experiment with both optimization w.r. t. the action sequences initialized from the policy network, and also online optimization directly w.r. t. the parameters of the policy network. We show that POPLIN obtains state-of-the-art performance in the MuJoCo benchmarking environments, being about 3x more sample efficient than the state-of-the-art algorithms, such as PETS, TD3 and SAC. To explain the effectiveness of our algorithm, we show that the optimization surface in parameter space is smoother than in action space. Further more, we found the distilled policy network can be effectively applied without the expansive model predictive control during test time for some environments such as Cheetah. Code is released in this https URL.
Screening Sinkhorn Algorithm for Regularized Optimal Transport

We introduce in this paper a novel strategy for efficiently approximating the Sinkhorn distance between two discrete measures. After identifying neglectable components of the dual solution of the regularized Sinkhorn problem, we propose to screen those components by directly setting them at that value before entering the Sinkhorn problem. This allows us to solve a smaller Sinkhorn problem while ensuring approximation with provable guarantees. More formally, the approach is based on a new formulation of dual of Sinkhorn divergence problem and on the KKT optimality conditions of this problem, which enable identification of dual components to be screened. This new analysis leads to the Screenkhorn algorithm. We illustrate the efficiency of Screenkhorn on complex tasks such as dimensionality reduction and domain adaptation involving regularized optimal transport.
Realization of Symmetry Enforced Two-Dimensional Dirac Fermions in Nonsymmorphic α-Bismuthene.

Two-dimensional (2D) Dirac-like electron gases have attracted tremendous research interest ever since the discovery of free-standing graphene. The linear energy dispersion and non-trivial Berry phase play a pivotal role in the electronic, optical, mechanical and chemical properties of 2D Dirac materials. The known 2D Dirac materials are gapless only within certain approximations, for example, in the absence of SOC. Here we report a route to establishing robust Dirac cones in 2D materials with nonsymmorphic crystal lattice. The nonsymmorphic symmetry enforces Dirac-like band dispersions around certain high-symmetry momenta in the presence of SOC. Through μ-ARPES measurements we observe Dirac-like band dispersions in α-bismuthene. The nonsymmorphic lattice symmetry is confirmed by μ-LEED and STM. Our first-principles simulations and theoretical topological analysis demonstrate the correspondence between nonsymmorphic symmetry and Dirac states. This mechanism can be straightforwardly generalized to other nonsymmorphic materials. The results enlighten the search of symmetry enforced Dirac fermions in the vast uncharted world of nonsymmorphic 2D materials.
Zero-shot Learning and Knowledge Transfer in Music Classification and Tagging

Music classification and tagging is conducted through categorical supervised learning with a fixed set of labels. In principle, this cannot make predictions on unseen labels. Zero-shot learning is an approach to solve the problem by using side information about the semantic labels. We recently investigated this concept of zero-shot learning in music classification and tagging task by projecting both audio and label space on a single semantic space. In this work, we extend the work to verify the generalization ability of zero-shot learning model by conducting knowledge transfer to different music corpora.
Cooperative Lane Changing via Deep Reinforcement Learning

In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning. We show that the reward of the system should consider the overall traffic efficiency instead of the travel efficiency of an individual vehicle. In summary, cooperation leads to a more harmonic and efficient traffic system rather than competition
Multi-resolution Multi-task Gaussian Processes

We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We develop a multi-resolution multi-task (MRGP) framework while allowing for both inter-task and intra-task multi-resolution and multi-fidelity. We develop shallow Gaussian Process (GP) mixtures that approximate the difficult to estimate joint likelihood with a composite one and deep GP constructions that naturally handle biases in the mean. By doing so, we generalize and outperform state of the art GP compositions and offer information-theoretic corrections and efficient variational approximations. We demonstrate the competitiveness of MRGPs on synthetic settings and on the challenging problem of hyper-local estimation of air pollution levels across London from multiple sensing modalities operating at disparate spatio-temporal resolutions.
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting

Robustness against noise is critical for keyword spotting (KWS) in real-world environments. To improve the robustness, a speech enhancement front-end is involved. Instead of treating the speech enhancement as a separated preprocessing before the KWS system, in this study, a pre-trained speech enhancement front-end and a convolutional neural networks (CNNs) based KWS system are concatenated, where a feature transformation block is used to transform the output from the enhancement front-end into the KWS system's input. The whole model is trained jointly, thus the linguistic and other useful information from the KWS system can be back-propagated to the enhancement front-end to improve its performance. To fit the small-footprint device, a novel convolution recurrent network is proposed, which needs fewer parameters and computation and does not degrade performance. Furthermore, by changing the input features from the power spectrogram to Mel-spectrogram, less computation and better performance are obtained. our experimental results demonstrate that the proposed method significantly improves the KWS system with respect to noise robustness.
More accurate numerical radius inequalities (I)

ABSTRACT In this article, we present some new general forms of numerical radius inequalities for Hilbert space operators. The significance of these inequalities follow from the way they extend and refine some known results in this field. Among other inequalities, it is shown that if A is a bounded linear operator on a complex Hilbert space, then where and are the numerical radius and the usual operator norm of A, respectively.
Reliable Data Delivery for Highly Dynamic MANETs

Delivering data packets in reliable and timely manner in MANETs is addressed here. Traditional ad hoc routing protocols fail to serve this purpose. A novel routing algorithm called Position based Opportunistic Routing (POR) is proposed here. POR algorithm clubs the advantage of greedy forwarding with broadcast nature of opportunistic routing. When a node send data packets to its one hop neighbor, the nodes which are very nearer to the neighbor can overhear the transmission. Using this overhearing as a backup even when a node fails to transmits a data packet, the overheard candidates will take the task of forwarding data packets. When the node mobility increases, due to random motion of nodes there are more chances of occurring communication holes called voids. In order to deal with these communication holes, a void handling scheme called Virtual Destination based Void Handling (VDVH) is proposed and clubbed with the POR algorithm. Keywords-greedy forwarding, opportunistic routing, reliable data delivery, void handing, black hole attack.
To Every Artifact Its Voice: Creating Surrogates for Hand-Crafted Indigenous Objects

This article reports on findings from qualitative research undertaken with a group of Aboriginal seniors in Toronto, Canada, to assess how a community-based collection of handcrafted objects could be used to evoke memories of maker culture (craft), as well as to foster meaning-making—all in the course of gathering elements requisite to representing each item in a documented surrogate. The article will discuss how the need to give voice to this unique collection both challenges and enriches traditional approaches to representing and organizing artifacts. A rethinking of surrogate records that center the Indigenous experience in the cataloging process is proposed.
Graphical Based Password Authentication

This paper presents the implementation of pass points, cued click points and in a queue for implementation subjected to its execution of persuasive cued clickpoints considering security . The major goal of this work is to reduce the guessing attack by using Persuasive Technology that should guide users to select stronger passwords. Persuasive Technology influences user to select more difficult password for secure authentication.
A High-Performance Key-Value Query Solution Based on Hash Dictionary and Trie Tree

With the development of Internet, tens of billions query were submitted every day. However, the old response mechanism of query which client submits query to server then server fetches data from database and sends data to client can't meet the requirements any more. We know the time of client to server request can't be cut down. To accelerate query speed, we have to avoid the costing time method that fetches data from database. Key-Value Store is popular today, but it has problems in Points barrels strategy and memory usage. In this paper, we propose a High-Performance Key-Value Query Solution which is based on hash dictionary and trie tree. Instead of fetching data from database, we construct a Key-Value dictionary in the memory to accelerate the whole query time. We use epoll of Linux to finish the asynchronous communication of client and server. For the dictionary, we design two different dictionaries like Hash dictionary, sorted dictionary based on trie tree. Hash dictionary uses Points barrels strategy and Minimal Perfect Hash Function. Sorted dictionary is based on trie tree. To illustrate performance of our solution, we test and record the performance of memory usage and query per second. It turns out that hash dictionary has more effective search time and sorted dictionary can hold more data. Keywords—key-value query; hash; tire tree