Miracle-dz/UniFAR-FTUs huggingface dataset:
Deuteron-
α
 scattering: Separable versus nonseparable Faddeev approach

{\bf Background} Deuteron induced reactions are widely used to probe nuclear structure and astrophysical information. Those (d,p) reactions may be viewed as three-body reactions and described with Faddeev techniques. {\bf Purpose} Faddeev-AGS equations in momentum space have a long tradition of utilizing separable interactions in order to arrive at sets of coupled integral equations in one variable. However, it needs to be demonstrated that observables calculated based on separable interactions agree exactly with those based on nonseparable forces. {\bf Methods} Momentum space AGS equations are solved with separable and nonseparable forces as coupled integral equations. {\bf Results} Deuteron-alpha scattering is calculated via momentum space AGS equations using the CD-Bonn neutron-proton force and a Woods-Saxon type neutron(proton)-$^4$He force, for which the Pauli-forbidden S-wave bound state is projected out. Elastic as well as breakup observables are calculated and compared to results in which the interactions in the two-body sub-systems are represented by separable interactions derived in the Ernst-Shakin-Thaler (EST) framework. {\bf Conclusions} We find that the calculations based on the separable representation of the interactions and the original interactions give results that are in excellent agreement. Specifically, integrated cross sections and angular distributions for elastic scattering agree within $\approx$ 1\%, which is well below typical experimental errors. In addition, the five-fold differential cross sections corresponding to breakup of the deuteron agree extremely well.
A Survey of Maturity Models from Nolon to DevOps and Their Applications in Process Improvement

This paper traces the history of Maturity Models and their impact on Process Improvement from the early work of Shewhart to their current usage with DevOps. The history of modern process improvement can be traced at least to Shewhart. From his foundational process contributions and those of other innovators a variety of methods and tools to aid in process quality advancement were developed. This paper begins by reviewing those early steps and then focuses on the emergence of Maturity Models in the 1970s with initial approach by Nolan. The broad adoption of Maturity Models that followed through the success of the CMM and then the CMMI approaches is detailed. This then leads to a general survey of additional models developed for such areas as IT Service Management, ITIL, Project Management, Agile Development, DevOps, CERT, and MDM among others. Finally, this paper discusses the application of these models in the support of process improvement and their limitations. Readers of this paper can expect to gain an appreciation for the origins of these models and surrounding methods as well as an ability to conduct comparative analysis of such models to aid in their selection and application. Keywords: Process Improvement, Process Engineering, Maturity Models, Capability Maturity Models, CMM, CMMI, ITSM, ITIL, Agile, DevOps, History of Science, History of Computing, Software Engineering, Quality.
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification

Convolutional Neural Networks (CNNs) have had great success in many machine vision as well as machine audition tasks. Many image recognition network architectures have consequently been adapted for audio processing tasks. However, despite some successes, the performance of many of these did not translate from the image to the audio domain. For example, very deep architectures such as ResNet [1] and DenseNet [2], which significantly outperform VGG [3] in image recognition, do not perform better in audio processing tasks such as Acoustic Scene Classification (ASC). In this paper, we investigate the reasons why such powerful architectures perform worse in ASC compared to simpler models (e.g., VGG). To this end, we analyse the receptive field (RF) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models. Using our receptive field analysis, we adapt both ResNet and DenseNet, achieving state-of-theart performance and eventually outperforming the VGG-based models. We introduce systematic ways of adapting the RF in CNNs, and present results on three data sets that show how changing the RF over the time and frequency dimensions affects a model’s performance. Our experimental results show that very small or very large RFs can cause performance degradation, but deep models can be made to generalize well by carefully choosing an appropriate RF size within a certain range.
Embedding models for recommendation under contextual constraints

Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine recommendations, e.g. when a user specifies a price range or product category filter. The conventional approach, for both context-aware and standard models, is to retrieve items and apply the constraints as independent operations. The order in which these two steps are executed can induce significant problems. For example, applying constraints a posteriori can result in incomplete recommendations or low-quality results for the tail of the distribution (i.e., less popular items). As a result, the additional information that the constraint brings about user intent may not be accurately captured. In this paper we propose integrating the information provided by the contextual constraint into the similarity computation, by merging constraint application and retrieval into one operation in the embedding space. This technique allows us to generate high-quality recommendations for the specified constraint. Our approach learns constraints representations jointly with the user and item embeddings. We incorporate our methods into a matrix factorization model, and perform an experimental evaluation on one internal and two real-world datasets. Our results show significant improvements in predictive performance compared to context-aware and standard models.
Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels

The Gaussian Process (GP) framework flexibility has enabled its use in several data modeling scenarios. The setting where we have unavailable or uncertain inputs that generate possibly noisy observations is usually tackled by the well known Gaussian Process Latent Variable Model (GPLVM). However, the standard variational approach to perform inference with the GPLVM presents some expressions that are tractable for only a few kernel functions, which may hinder its general application. While other quadrature or sampling approaches could be used in that case, they usually are very slow and/or non-deterministic. In the present paper, we propose the use of the unscented transformation to enable the use of any kernel function within the Bayesian GPLVM. Our approach maintains the fully deterministic feature of tractable kernels and presents a simple implementation with only moderate computational cost. Experiments on dimensionality reduction and multistep-ahead prediction with uncertainty propagation indicate the feasibility of our proposal.
Cover Detection Using Dominant Melody Embeddings

Automatic cover detection -- the task of finding in an audio database all the covers of one or several query tracks -- has long been seen as a challenging theoretical problem in the MIR community and as an acute practical problem for authors and composers societies. Original algorithms proposed for this task have proven their accuracy on small datasets, but are unable to scale up to modern real-life audio corpora. On the other hand, faster approaches designed to process thousands of pairwise comparisons resulted in lower accuracy, making them unsuitable for practical use. In this work, we propose a neural network architecture that is trained to represent each track as a single embedding vector. The computation burden is therefore left to the embedding extraction -- that can be conducted offline and stored, while the pairwise comparison task reduces to a simple Euclidean distance computation. We further propose to extract each track's embedding out of its dominant melody representation, obtained by another neural network trained for this task. We then show that this architecture improves state-of-the-art accuracy both on small and large datasets, and is able to scale to query databases of thousands of tracks in a few seconds.
An Experimental Evaluation of Large Scale GBDT Systems

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we find it surprising that the existing systems manage the training dataset in different ways, but none of them have studied the impact of data management. To that end, this paper aims to study the pros and cons of different data management methods regarding the performance of distributed GBDT. We first introduce a quadrant categorization of data management policies based on data partitioning and data storage. Then we conduct an in-depth systematic analysis and summarize the advantageous scenarios of the quadrants. Based on the analysis, we further propose a novel distributed GBDT system named Vero, which adopts the unexplored composition of vertical partitioning and row-store and suits for many large-scale cases. To validate our analysis empirically, we implement different quadrants in the same code base and compare them under extensive workloads, and finally compare Vero with other state-of-the-art systems over a wide range of datasets. Our theoretical and experimental results provide a guideline on choosing a proper data management policy for a given workload.
Machine Reading Comprehension: a Literature Review

Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence. This article summarizes recent advances in MRC, mainly focusing on two aspects (i.e., corpus and techniques). The specific characteristics of various MRC corpus are listed and compared. The main ideas of some typical MRC techniques are also described.
An efficient gradient method with approximately optimal stepsize based on cubic regularization model for large-scale unconstrained optimization

A new type of stepsize, which was recently introduced by Liu et al. (Optimization 67(3):427-440, 2018), is called approximately optimal stepsize and is very efficient for gradient method. In this paper, we present an efficient gradient method with approximately optimal stepsize for large-scale unconstrained optimization, which combines the cubic regularization technique with a nonmonotone line search strategy. In the proposed method, if the objective function is not close to a quadratic on the line segment between the current and latest iterates, we construct a cubic regularization models to generate approximately optimal stepsizes for gradient method. In particular, when the curvature is non-positive, we construct two special cubic regularization models to generate approximately optimal stepsizes. Otherwise, we use quadratic models to generate approximately optimal stepsizes for gradient method. A modified strategy for updating the regularization parameter is given. The convergence of the proposed algorithm is established under the mild conditions. Numerical results indicated the proposed method is very promising.
Modelling the post-reionization neutral hydrogen (H i) 21-cm bispectrum

Measurements of the post-reionization 21-cm bispectrum $B_{{\rm H\,{\small I}}\, }(\boldsymbol {k_1},\boldsymbol {k_2},\boldsymbol {k_3})$ using various upcoming intensity mapping experiments hold the potential for determining the cosmological parameters at a high level of precision. In this paper, we have estimated the 21-cm bispectrum in the z range 1 ≤ z ≤ 6 using seminumerical simulations of the neutral hydrogen (H i) distribution. We determine the k and z range where the 21-cm bispectrum can be adequately modelled using the predictions of second-order perturbation theory, and we use this to predict the redshift evolution of the linear and quadratic H i bias parameters b1 and b2, respectively. The b1 values are found to decrease nearly linearly with decreasing z, and are in good agreement with earlier predictions obtained by modelling the 21-cm power spectrum $P_{{\rm H\,{\small I}}\, }(k)$. The b2 values fall sharply with decreasing z, becomes zero at z ∼ 3 and attains a nearly constant value b2 ≈ −0.36 at z < 2. We provide polynomial fitting formulas for b1 and b2 as functions of z. The modelling presented here is expected to be useful in future efforts to determine cosmological parameters and constrain primordial non-Gaussianity using the 21-cm bispectrum.
MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines

MultiWOZ 2.0 (Budzianowski et al., 2018) is a recently released multi-domain dialogue dataset spanning 7 distinct domains and containing over 10,000 dialogues. Though immensely useful and one of the largest resources of its kind to-date, MultiWOZ 2.0 has a few shortcomings. Firstly, there is substantial noise in the dialogue state annotations and dialogue utterances which negatively impact the performance of state-tracking models. Secondly, follow-up work (Lee et al., 2019) has augmented the original dataset with user dialogue acts. This leads to multiple co-existent versions of the same dataset with minor modifications. In this work we tackle the aforementioned issues by introducing MultiWOZ 2.1. To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset. This correction process results in changes to over 32% of state annotations across 40% of the dialogue turns. In addition, we fix 146 dialogue utterances by canonicalizing slot values in the utterances to the values in the dataset ontology. To address the second problem, we combined the contributions of the follow-up works into MultiWOZ 2.1. Hence, our dataset also includes user dialogue acts as well as multiple slot descriptions per dialogue state slot. We then benchmark a number of state-of-the-art dialogue state tracking models on the MultiWOZ 2.1 dataset and show the joint state tracking performance on the corrected state annotations. We are publicly releasing MultiWOZ 2.1 to the community, hoping that this dataset resource will allow for more effective models across various dialogue subproblems to be built in the future.
Smoothness and norm attainment of bounded bilinear operators between Banach spaces

We study the smoothness and the norm attainment of bounded bilinear operators between Banach spaces, using the concepts of Birkhoff-James orthogonality and semi-inner-products. In the finite-dimensional case, we characterize Birkhoff-James orthogonality of bilinear operators in terms of the norm attainment set. This yields a nice characterization of smoothness of bilinear operators between Banach spaces, in the finite-dimensional case. Without any restriction on the dimension of the space, we obtain a complete characterization of the norm attainment set of a bounded bilinear operator using semi-inner-products, which is particularly useful when the concerned Banach spaces are smooth.
The dwarf galaxy satellite system of Centaurus A

Dwarf galaxy satellite systems are essential probes to test models of structure formation, making it necessary to establish a census of dwarf galaxies outside of our own Local Group. We present deep FORS2 VI band images from the ESO Very Large Telescope (VLT) for 15 dwarf galaxy candidates in the Centaurus group of galaxies. We confirm nine dwarfs to be members of Cen A by measuring their distances using a Bayesian approach to determine the tip of the red giant branch luminosity. We have also fit theoretical isochrones to measure their mean metallicities. The properties of the new dwarfs are similar to those in the Local Group in terms of their sizes, luminosities, and mean metallicities. Within our photometric precision, there is no evidence of a metallicity spread, but we do observe possible extended star formation in several galaxies, as evidenced by a population of asymptotic giant branch stars brighter than the red giant branch tip. The new dwarfs do not show any signs of tidal disruption. Together with the recently reported dwarf galaxies by the complementary PISCeS survey, we study the luminosity function and 3D structure of the group. By comparing the observed luminosity function to the high-resolution cosmological simulation IllustrisTNG, we find agreement within a 90% confidence interval. However, Cen A seems to be missing its brightest satellites and has an overabundance of the faintest dwarfs in comparison to its simulated analogs. In terms of the overall 3D distribution of the observed satellites, we find that the whole structure is flattened along the line-of-sight, with a root-mean-square (rms) height of 130 kpc and an rms semi-major axis length of 330 kpc. Future distance measurements of the remaining dwarf galaxy candidates are needed to complete the census of dwarf galaxies in the Centaurus group.
Interplay between 
SU(Nf)
 chiral symmetry, 
U(1)A
 axial anomaly, and massless bosons

The standard wisdom on the origin of massless bosons in the spectrum of a Quantum Field Theory $(QFT)$ describing the interaction of gauge fields coupled to matter fields is based on two well known features: gauge symmetry, and spontaneous symmetry breaking of continuous global symmetries. However we will show in this article how the topological properties, that originate the $U(1)_A$ axial anomaly in a $QFT$ which describes the interaction of fermion matter fields and gauge bosons, are the basis of an alternative mechanism to generate massless bosons in the chiral limit, if the non-abelian $SU(N_f)_A$ chiral symmetry is fulfilled in the vacuum. We will also test our predictions with the results of a well known two-dimensional model, the two-flavour Schwinger model, which was analyzed by Coleman long ago, and will give a reliable answer to some of the questions he asked himself on the spectrum of the model in the strong-coupling (chiral) limit. We will also analyze what are the expectations for the $U(N)$ gauge-fermion model in two dimensions, and will discuss on the impact of our results in the chirally symmetric high temperature phase of $QCD$, which was present in the early universe, and is expected to be created in heavy-ion collision experiments.
Learning Landmarks from Unaligned Data using Image Translation

We introduce a method for learning landmark detectors from unlabelled video frames and unpaired labels. This allows us to learn a detector from a large collection of raw videos given only a few example annotations harvested from existing data or motion capture. We achieve this by formulating the landmark detection task as one of image translation, learning to map an image of the object to an image of its landmarks, represented as a skeleton. The advantage is that this translation problem can then be tackled by CycleGAN. However, we show that a naive application of CycleGAN confounds appearance and pose information, with suboptimal keypoint detection performance. We solve this problem by introducing an analytical and differentiable renderer for the skeleton image so that no appearance information can be leaked in the skeleton. Then, since cycle consistency requires to reconstruct the input image from the skeleton, we supply the appearance information thus removed by conditioning the generator with a second image of the same object (e.g. another frame from a video). Furthermore, while CycleGAN uses two cycle consistency constraints, we show that the second one is detrimental in this application and we discard it, significantly simplifying the model. We show that these modifications improve the quality of the learned detector leading to state-of-the-art unsupervised landmark detection performance in a number of challenging human pose and facial landmark detection benchmarks.
End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning

In this paper, a novel deep reinforcement learning (DRL)-based method is proposed to navigate the robot team through unknown complex environments, where the geometric centroid of the robot team aims to reach the goal position while avoiding collisions and maintaining connectivity. Decentralized robot-level policies are derived using a mechanism of centralized learning and decentralized executing. The proposed method can derive end-to-end policies, which map raw lidar measurements into velocity control commands of robots without the necessity of constructing obstacle maps. Simulation and indoor real-world unmanned ground vehicles (UGVs) experimental results verify the effectiveness of the proposed method.
Bi-chromatic adiabatic shells for atom interferometry

Free space atom-interferometry traditionally suffers from the large distances that atoms have to fall in order to achieve long interaction times. Trapped atom interferometry is emerging as a powerful way of achieving long interaction times in a reduced experimental volume. Here, we demonstrate bi-chromatic adiabatic magnetic shell traps as a novel tool for matterwave interferometry. We dress the magnetic hyperfine states of the F = 1 and F = 2 Rubidium 87 Bose–Einstein Condensates thus creating two independently controllable shell traps of which we use the ∣ F = 1 , m ¯ F = − 1 〉 and ∣ F = 2 , m ¯ F = 1 〉 adiabatic states. Using microwave pulses, we put atoms originally loaded into one of the two shell-traps into a superposition between the two shell traps. Since the two traps can be manipulated independently, their position and vertical curvature can be matched, thus creating a good starting point for an atom interferometer. This interferometer can be sensitive to spatially varying electric or magnetic fields, which could be DC or RF magnetic fields or microwaves. We demonstrate that the trap-matching afforded by the independent control of the shell traps allows for a tenfold increase in coherence times when compared to adiabatic potentials created by a single RF-frequency. For large-radius shells the atoms are confined to a 2D surface enabling highly sensitive imaging matterwave interferometers.
Can quantum many-body systems behave as strongly chaotic, being completely integrable ?

We study the paradigmatic Lieb-Liniger (LL) model belonging to the class of integrable quantum many-body systems, by considering its statistical properties in the many-body Hilbert space. We demonstrate that, for a fixed total momentum, the properties of both energy spectra fluctuations and many-body eigenstates follow the predictions of the random matrix theory. Specifically, for a finite number of bosons in a finite number of momentum levels and sufficiently strong interaction, the level spacing distribution manifests the crossover from Poisson to Wigner-Dyson statistics. In the latter situation, the many-body eigenstates can be treated as fully random, in spite of the deterministic nature of the Hamiltonian matrix elements. By studying the quench dynamics of an initially excited state of the non interacting Hamiltonian, we have discovered a remarkable relation between the thermodynamic entropy emerging after the relaxation of the system to equilibrium, and the diagonal entropy related to the initial state. Our semi-analytical predictions of the onset of statistical relaxation in the LL model are fully confirmed by numerical data.