Miracle-dz/UniFAR-FTUs huggingface dataset:
A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts

End-to-end Task-oriented Dialogue Systems (TDSs) have attracted a lot of attention for their superiority (e.g., in terms of global optimization) over pipeline modularized TDSs. Previous studies on end-to-end TDSs use a single-module model to generate responses for complex dialogue contexts. However, no model consistently outperforms the others in all cases. We propose a neural Modular Task-oriented Dialogue System(MTDS) framework, in which a few expert bots are combined to generate the response for a given dialogue context. MTDS consists of a chair bot and several expert bots. Each expert bot is specialized for a particular situation, e.g., one domain, one type of action of a system, etc. The chair bot coordinates multiple expert bots and adaptively selects an expert bot to generate the appropriate response. We further propose a Token-level Mixture-of-Expert (TokenMoE) model to implement MTDS, where the expert bots predict multiple tokens at each timestamp and the chair bot determines the final generated token by fully taking into consideration the outputs of all expert bots. Both the chair bot and the expert bots are jointly trained in an end-to-end fashion. To verify the effectiveness of TokenMoE, we carry out extensive experiments on a benchmark dataset. Compared with the baseline using a single-module model, our TokenMoE improves the performance by 8.1% of inform rate and 0.8% of success rate.
H0LiCOW – XIII. A 2.4 per cent measurement of H0 from lensed quasars: 5.3σ tension between early- and late-Universe probes

We present a measurement of the Hubble constant (H0) and other cosmological parameters from a joint analysis of six gravitationally lensed quasars with measured time delays. All lenses except the first are analysed blindly with respect to the cosmological parameters. In a flat Λ cold dark matter (ΛCDM) cosmology, we find $H_{0} = 73. 3_{-1.8}^{+1.7}~\mathrm{km~s^{-1}~Mpc^{-1}}$, a $2.4{{\ \rm per\ cent}}$ precision measurement, in agreement with local measurements of H0 from type Ia supernovae calibrated by the distance ladder, but in 3. 1σ tension with Planck observations of the cosmic microwave background (CMB). This method is completely independent of both the supernovae and CMB analyses. A combination of time-delay cosmography and the distance ladder results is in 5. 3σ tension with Planck CMB determinations of H0 in flat ΛCDM. We compute Bayes factors to verify that all lenses give statistically consistent results, showing that we are not underestimating our uncertainties and are able to control our systematics. We explore extensions to flat ΛCDM using constraints from time-delay cosmography alone, as well as combinations with other cosmological probes, including CMB observations from Planck, baryon acoustic oscillations, and type Ia supernovae. Time-delay cosmography improves the precision of the other probes, demonstrating the strong complementarity. Allowing for spatial curvature does not resolve the tension with Planck. Using the distance constraints from time-delay cosmography to anchor the type Ia supernova distance scale, we reduce the sensitivity of our H0 inference to cosmological model assumptions. For six different cosmological models, our combined inference on H0 ranges from ∼73 to 78 km s−1 Mpc−1, which is consistent with the local distance ladder constraints.
MeetUp! A Corpus of Joint Activity Dialogues in a Visual Environment

Building computer systems that can converse about their visual environment is one of the oldest concerns of research in Artificial Intelligence and Computational Linguistics (see, for example, Winograd's 1972 SHRDLU system). Only recently, however, have methods from computer vision and natural language processing become powerful enough to make this vision seem more attainable. Pushed especially by developments in computer vision, many data sets and collection environments have recently been published that bring together verbal interaction and visual processing. Here, we argue that these datasets tend to oversimplify the dialogue part, and we propose a task---MeetUp! ---that requires both visual and conversational grounding, and that makes stronger demands on representations of the discourse. MeetUp! is a two-player coordination game where players move in a visual environment, with the objective of finding each other. To do so, they must talk about what they see, and achieve mutual understanding. We describe a data collection and show that the resulting dialogues indeed exhibit the dialogue phenomena of interest, while also challenging the language & vision aspect.
Eco-driving with Learning Model Predictive Control

We present a predictive cruise controller which iteratively improves the fuel economy of a vehicle traveling along the same route every day. Our approach uses historical data from previous trip iterations to improve vehicle performance while guaranteeing a desired arrival time. The proposed predictive cruise controller is based on the recently developed Learning Model Predictive Control (LMPC) framework, which is extended in this paper to include time constraints. Moreover, we reformulate the modified LMPC with time constraint into a computationally tractable form. Our method is presented in detail, applied to the predictive cruise control problem, and validated through simulations.
On the universal central extension of superelliptic affine Lie algebras of degree m

The goal of this paper is to explicitly describe in terms of generators and relations the universal central extension of the infinite dimensional Lie algebra, $\mathfrak{g} \otimes \mathbb{C}[t,t^{-1},u]$ with finite dimensional simple Lie algebra $\mathfrak{g}$ and $u^m=p(t)$.
Quantum meronomic frames

Composite quantum systems can be decomposed into subsystems in many different inequivalent ways. We call a particular decomposition a meronomic reference frame for the system. We apply the ideas of quantum reference frames to characterize meronomic frames, identify tasks that require such frames to accomplish, and show how asymmetric quantum states can be used to embody meronomic frame information.
Accurate Congenital Heart Disease Model Generation for 3D Printing

3D printing has been widely adopted for clinical decision making and interventional planning of Congenital heart disease (CHD), while whole heart and great vessel segmentation is the most significant but time-consuming step in model generation for 3D printing. While various automatic whole heart and great vessel segmentation frameworks have been developed in the literature, they are ineffective when applied to medical images in CHD, which have significant variations in heart structure and great vessel connections. To address the challenge, we leverage the power of deep learning in processing regular structures and that of graph algorithms in dealing with large variations, and propose a framework that combines both for whole heart and great vessel segmentation in CHD. Particularly, we first use deep learning to segment the four chambers and myocardium followed by blood pool, where variations are usually small. We then extract the connection information and apply graph matching to determine the categories of all the vessels. Experimental results using 68 3D CT images covering 14 types of CHD show that our method can increase Dice score by 11.9% on average compared with the state-of-the-art whole heart and great vessel segmentation method in normal anatomy. The segmentation results are also printed out using 3D printers for validation.
A Neural Architecture for Designing Truthful and Efficient Auctions

Auctions are protocols to allocate goods to buyers who have preferences over them, and collect payments in return. Economists have invested significant effort in designing auction rules that result in allocations of the goods that are desirable for the group as a whole. However, for settings where participants' valuations of the items on sale are their private information, the rules of the auction must deter buyers from misreporting their preferences, so as to maximize their own utility, since misreported preferences hinder the ability for the auctioneer to allocate goods to those who want them most. Manual auction design has yielded excellent mechanisms for specific settings, but requires significant effort when tackling new domains. We propose a deep learning based approach to automatically design auctions in a wide variety of domains, shifting the design work from human to machine. We assume that participants' valuations for the items for sale are independently sampled from an unknown but fixed distribution. Our system receives a data-set consisting of such valuation samples, and outputs an auction rule encoding the desired incentive structure. We focus on producing truthful and efficient auctions that minimize the economic burden on participants. We evaluate the auctions designed by our framework on well-studied domains, such as multi-unit and combinatorial auctions, showing that they outperform known auction designs in terms of the economic burden placed on participants.
Torus spectroscopy of the Gross-Neveu-Yukawa quantum field theory: Free Dirac versus chiral Ising fixed point

We establish the universal torus low-energy spectra at the free Dirac fixed point and at the strongly coupled {\em chiral Ising} fixed point and their subtle crossover behaviour in the Gross-Neuveu-Yukawa field theory with ${n_\text{D}=4}$ component Dirac spinors in $D=(2+1)$ dimensions. These fixed points and the field theories are directly relevant for the long-wavelength physics of certain interacting Dirac systems, such as repulsive spinless fermions on the honeycomb lattice or $\pi$-flux square lattice. The torus spectrum has been shown previously to serve as a characteristic fingerprint of relativistic fixed points and is a powerful tool to discriminate quantum critical behaviour in numerical simulations. Here we use a combination of exact diagonalization and quantum Monte Carlo simulations of strongly interacting fermionic lattice models, to compute the critical energy spectrum on finite-size clusters with periodic boundaries and extrapolate them to the thermodynamic limit. Additionally, we compute the torus spectrum analytically using the perturbative expansion in ${\epsilon = 4 - D}$, which is in good agreement with the numerical results, thereby validating the presence of the chiral Ising fixed point in the lattice models at hand. We show that the strong interaction between the spinor field and the scalar order-parameter field strongly influences the critical torus spectrum. Building on these results we are able to address the subtle crossover physics of the low-energy spectrum flowing from the chiral Ising fixed point to the Dirac fixed point, and analyze earlier flawed attempts to extract Fermi velocity renormalizations from the low-energy spectrum.
Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction

Understanding the dynamics of an environment, such as the movement of humans and vehicles, is crucial for agents to achieve long-term autonomy in urban environments. This requires the development of methods to capture the multi-modal and probabilistic nature of motion patterns. We present Kernel Trajectory Maps (KTM) to capture the trajectories of movement in an environment. KTMs leverage the expressiveness of kernels from non-parametric modelling by projecting input trajectories onto a set of representative trajectories, to condition on a sequence of observed waypoint coordinates, and predict a multi-modal distribution over possible future trajectories. The output is a mixture of continuous stochastic processes, where each realisation is a continuous functional trajectory, which can be queried at arbitrarily fine time steps.
New constraints on neutrino electric millicharge from elastic neutrino-electron scattering and coherent elastic neutrino-nucleus scattering

The neutrino electromagnetic properties have been studied since this particle was first postulated by Wolfgang Pauli in 1930. In different extensions of the Standard Model of Particle Physics (SMPP), the neutrino has electric millicharge and charge radius, as well as electric and magnetic dipole moments, the latter being the most studied property in the literature. However, the possibility that neutrino be a millicharged particle has also been a subject of study in different theoretical and experimental works. Additionally, several experimental constraints on the neutrino electric millicharge (NEM) have been reported from reactors, accelerators, and astrophysical measurements. In this work, we first carry out a statistical analysis using data from reactor neutrino experiments, which include elastic neutrino-electron scattering (ENES) processes, in order to obtain both individual and combined limits on the neutrino electric millicharge. Then we perform a similar calculation, but this time employing data from several future experimental proposals involving coherent elastic neutrino-nucleus scattering (CE$\nu$NS). In the first case, the limit of NEM achieved from the combined results of different experiments is $q_{\nu} \lesssim 1.5\times 10^{-12} e$ at $90\%$ C.L. , and in the second scenario the bound corresponds to $q_{\nu} \lesssim 3.5\times 10^{-13} e$ ($90\%$ C.L.). The outcomes indicate CE$\nu$NS experimental proposals might be a suitable alternative to improve the current limits of NEM.
An algorithm for the selection of route dependent orientation information

Landmarks are important features of spatial cognition. Landmarks are naturally included in human route descriptions and in the past algorithms were developed to select the most salient landmarks at decision points and automatically incorporate them in route instructions. Moreover, it was shown that human route descriptions contain a significant amount of orientation information and that these orientation information support the acquisition of survey knowledge. Thus, there is a need to extend the landmarks selection in order to automatically select orientation information. In this work we present an algorithm for the computational selection of route dependent orientation information, which extends previous algorithms and includes a salience evaluation of orientation information for any location along the route. We implemented the algorithm and demonstrate the functionality on the basis of OpenStreetMap data.
A comparative study of multiple object detection using Haar-like feature selection and local binary patterns in several platforms

Object detection has been attracting much interest due to the wide spectrum of applications that use it. It has been driven by an increasing processing power available in software and hardware platforms. In this work we present a developed application for multiple objects detection based on Open CV libraries.The complexity-related aspects that were considered in the object detection using cascade classifier are described. Furthermore, we discuss the profiling and porting of the application into an embedded platform and compare the results with those obtained on traditional platforms. The proposed application deals with real-time systems implementation and the results give a metric able to select where the cases of object detection applications may be more complex and where it may be simpler.
Time-scale of twin-peak quasi-periodic oscillations and mass of accreting neutron stars

Einstein’s general relativity predicts that orbital motion of accreted gas approaching a neutron star (NS) in a NS low-mass X-ray binary (LMXB) system occurs on a time-scale proportional to the NS mass. Radiation of the gas accounts for most of the observed LMXBs variability. In more than a dozen of sources twin-peak quasi-periodic oscillations (QPOs) have been observed. Inspired by the expected proportionality between periods of orbital motion and NS mass we present a straightforward comparison among these sources. We investigate relations between QPO periods and their ratios and identify characteristic time-scales of QPOs associated with individual sources. These time-scales are likely determined by the relative mass of each NS. We show that the characteristic time-scale of the millisecond pulsar XTE J1807.4−294 is longer than for most other NS LMXBs. Consequently, models of QPOs that consider geodesic orbital frequencies imply that the X-ray pulsars’ mass has to be about $50{{\ \rm per\ cent}}$ higher than the average mass of other sources. Consideration of other X-ray pulsars indicates that the exceptionality of XTE J1807.4−294 cannot be related to NS magnetic field in any simple manner. We suggest that QPOs observed in this source can help to discriminate between the proposed versions of the NS equation of state.
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds

Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines. We propose a novel deep-learning method to learn stable and temporally coherent feature spaces for points clouds that change over time. We identify a set of inherent problems with these approaches: without knowledge of the time dimension, the inferred solutions can exhibit strong flickering, and easy solutions to suppress this flickering can result in undesirable local minima that manifest themselves as halo structures. We propose a novel temporal loss function that takes into account higher time derivatives of the point positions, and encourages mingling, i.e., to prevent the aforementioned halos. We combine these techniques in a super-resolution method with a truncation approach to flexibly adapt the size of the generated positions. We show that our method works for large, deforming point sets from different sources to demonstrate the flexibility of our approach.
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems

Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions. Recent studies show that adversarial examples can pose a threat to real-world security-critical applications: a "physical adversarial Stop Sign" can be synthesized such that the autonomous driving cars will misrecognize it as others (e.g., a speed limit sign). However, these image-space adversarial examples cannot easily alter 3D scans of widely equipped LiDAR or radar on autonomous vehicles. In this paper, we reveal the potential vulnerabilities of LiDAR-based autonomous driving detection systems, by proposing an optimization based approach LiDAR-Adv to generate adversarial objects that can evade the LiDAR-based detection system under various conditions. We first show the vulnerabilities using a blackbox evolution-based algorithm, and then explore how much a strong adversary can do, using our gradient-based approach LiDAR-Adv. We test the generated adversarial objects on the Baidu Apollo autonomous driving platform and show that such physical systems are indeed vulnerable to the proposed attacks. We also 3D-print our adversarial objects and perform physical experiments to illustrate that such vulnerability exists in the real world. Please find more visualizations and results on the anonymous website: this https URL.
2PBC J0658.0–1746: a hard X-ray eclipsing polar in the orbital period gap

The hard X-ray source 2PBC J0658.0–1746 was proposed as an eclipsing magnetic cataclysmic variable of the polar type, based on optical follow-ups. We present the first spectral and timing analysis at X-ray energies with XMM–Newton, complemented with archival X-ray, optical, infrared (IR) photometry, and spectroscopy. The X-ray emission shows bright and faint phases and total eclipses recurring every 2.38 h, consistent with optical properties. This firmly identifies 2PBC J0658.0–1746 as an eclipsing polar, the second hard X-ray selected in the orbital period gap. The X-ray orbital modulation changes from cycle-to-cycle and the X-ray flux is strongly variable over the years, implying a non-stationary mass accretion rate both on short and long time-scales. The X-ray eclipses allow to refine the orbital ephemeris with period 0.09913398(4) d, and to constrain the binary inclination $79^{\circ}\lesssim i \lesssim 90^{\circ}$ and the mass ratio 0.18$\lt M_2/M_{\mathrm{ WD}}\lt $0.40. A companion mass M$_{2}=0.2-0.25\rm \, M_{\odot }$ with a radius R$_{2}=0.24-0.26\rm \, R_{\odot }$ and spectral type ∼M4, at D$=209^{+3}_{-2}\rm \, pc$, is derived. A lower limit to the white dwarf mass of $\sim 0.6\, \rm \, M_{\odot }$ is obtained from the X-ray spectrum. An upper limit to the magnetic colatitude, $\beta \lesssim 50^{\circ}$, and a shift in azimuth, $\psi \sim 14^{\circ}$, of the main accreting pole are also estimated. The optical/IR spectral energy distribution shows large excess in the mid-IR due to lower harmonics of cyclotron emission. A high-state mass accretion rate $\rm \, \sim 0.4-1\times 10^{-10}\, M_{\odot }\, yr^{-1}$, lower than that of cataclysmic variables above the gap and close to that of systems below it, is estimated. With 2PBC J0658.0–1746, the number of hard X-ray-selected polars increases to 13 members, suggesting that they are not as rare as previously believed.
VER J2227+608: A Hadronic PeVatron Pulsar Wind Nebula?

We report the detection of GeV γ-ray emission from the very-high-energy γ-ray source VER J2227+608 associated with the “tail” region of supernova remnant (SNR) G106.3+2.7. The GeV γ-ray emission is extended and spatially coincident with molecular clouds traced by CO emission. The broadband GeV to TeV emission of VER J2227+608 can be well fitted by a single power-law function with an index of 1.90 ± 0.04, without obvious indication of spectral cutoff toward high energies. The pure leptonic model for the γ-ray emission can be marginally ruled out by the X-ray and TeV data. In the hadronic model, the low energy content of CRs and the hard γ-ray spectrum, in combination with the center bright source structure, suggest that VER J2227+608 may be powered by the Pulsar wind nebula instead of shocks of the SNR. And the cutoff energy of the proton distribution needs to be higher than ∼400 TeV, which makes it an attractive PeVatron candidate. Future observations by the upcoming Large High Altitude Air Shower Observatory and the Cherenkov Telescope Array in the north could distinguish these models and constrain the maximum energy of cosmic rays in SNRs.
A neural computational scheme for extracting optical flow from the Gabor phase differences of successive images

The authors propose a neurobiologically plausible representation of the Gabor phase information, and present a neural computation scheme for extracting visual motion information from the Gabor phase information. The scheme can compute visual motion accurately from a scene with illumination changes, while other neural schemes for optical flow must assume stable brightness. The computational tests on synthetic and natural image data showed that the scheme was robust to the natural scenes. An architecture is presented of a neural network system based on the Gabor phase representation of visual motion. <<ETX>>