Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian methods for fitting Baryon Acoustic Oscillations in the Lyman-α forest

We study and compare fitting methods for the Lyman-α (Lyα) forest 3D correlation function. We use the nested sampler PolyChord and the community code picca to perform a Bayesian analysis which we compare with previous frequentist analyses. By studying synthetic correlation functions, we find that the frequentist profile likelihood produces results in good agreement with a full Bayesian analysis. On the other hand, Maximum Likelihood Estimation with the Gaussian approximation for the uncertainties is inadequate for current data sets. We compute for the first time the full posterior distribution from the Lyα forest correlation functions measured by the extended Baryon Oscillation Spectroscopic Survey (eBOSS). We highlight the benefits of sampling the full posterior distribution by expanding the baseline analysis to better understand the contamination by Damped Lyα systems (DLAs). We make our improvements and results publicly available as part of the picca package.
Prevalence, Clinical and Etiological Profile of Acute Pancreatitis in India: A Single Center Study

Introduction: Acute pancreatitis is an inflammatory condition of pancreas caused by auto digestion of the gland by its digestive enzymes leading to destruction of the gland. The aim of the study is to assess different epidemiological factors associated with acute pancreatitis. Materials and Methods: Our study a cross-sectional descriptive study. This study includes a total of 69 patients admitted in the medicine department with acute pancreatitis during the year. A detailed history was taken and radiological, biochemical studies was done to evaluate the patients. Observation: there was male preponderance. Alcohol was the most common causative factor. Most of the patients were from age group 30-50.Plain radiograph of abdomen was not specific. USG whole abdomen and CECT abdomen was very sensitive. Serum amylase was elevated in all cases. Pain abdomen was most common symptom. Discussion: discrepancy in sex distribution may be due to difference in hospital admission rate in male and female. Amylase creatinine ratio is very helpful. CECT abdomen has very little advantage over usg in diagnosing acute pancreatitis.
Robust Inference and Verification of Temporal Logic Classifier-in-the-loop Systems

Autonomous systems embedded with machine learning modules often rely on deep neural networks for classifying different objects of interest in the environment or different actions or strategies to take for the system. Due to the non-linearity and high-dimensionality of deep neural networks, the interpretability of the autonomous systems is compromised. Besides, the machine learning methods in autonomous systems are mostly data-intensive and lack commonsense knowledge and reasoning that are natural to humans. In this paper, we propose the framework of temporal logic classifier-in-the-loop systems. The temporal logic classifiers can output different actions to take for an autonomous system based on the environment, such that the behavior of the autonomous system can satisfy a given temporal logic specification. Our approach is robust and provably-correct, as we can prove that the behavior of the autonomous system can satisfy a given temporal logic specification in the presence of (bounded) disturbances.
Dynamic lot size MIPs for multiple products and ELSPs with shortages,capacity and changeover limits

Scheduling multiple products with limited resources and varying demands remain a critical challenge formany industries. This work presents mixed integer programs(MIPs) that solve the Economic Lot SizingProblem (ELSP) and other Dynamic Lot-Sizing (DLS) models with multiple items. DLS systems are clas-sified, extended and formulated as MIPs. Especially, logical constraints are a key ingredient in succeedingin this endeavour. They were used to formulate the setup/changeover of items in the production line. Min-imising the holding, shortage and setup costs is the primaryobjective for ELSPs. This is achieved by findingan optimal production schedule taking into account the limited manufacturing capacity. Case studies for aproduction plants are used to demonstrate the functionality of the MIPs. Optimal DLS and ELSP solutionsare given for a set of test-instances. Insights into the runtime and solution quality are given.
Key developments in algorithmic randomness

The goal of this introductory survey is to present the major developments of algorithmic randomness with an eye toward its historical development. While two highly comprehensive books and one thorough survey article have been written on the subject, our goal is to provide an introduction to algorithmic randomness that will be both useful for newcomers who want to develop a sense of the field quickly and interesting for researchers already in the field who would like to see these results presented in chronological order.
The Threshold of Symmetry in Random Graphs with Specified Degree Sequences

We give sufficient conditions under which a random graph with a specified degree sequence is symmetric or asymmetric. In the case of bounded degree sequences, our characterisation captures the phase transition of the symmetry of the random graphs. This phase transition coincides with that of the graph connectivity. However, when the maximum degree is a growing function as the number of vertices tends to infinity, our results suggest that these two thresholds do not coincide any more
Towards Detection of Sheep Onboard a UAV

In this work we consider the task of detecting sheep onboard an unmanned aerial vehicle (UAV) flying at an altitude of 80 m. At this height, the sheep are relatively small, only about 15 pixels across. Although deep learning strategies have gained enormous popularity in the last decade and are now extensively used for object detection in many fields, state-of-the-art detectors perform poorly in the case of smaller objects. We develop a novel dataset of UAV imagery of sheep and consider a variety of object detectors to determine which is the most suitable for our task in terms of both accuracy and speed. Our findings indicate that a UNet detector using the weighted Hausdorff distance as a loss function during training is an excellent option for detection of sheep onboard a UAV.
LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter- and intra-observer variability. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. However, the current best solutions still suffer from a lack of robustness. In this work, we introduce an end-to-end multi-task network designed to improve the overall accuracy of cardiac segmentation while enhancing the estimation of clinical indices and reducing the number of outliers. Results obtained on a large open access dataset show that our method outperforms the current best performing deep learning solution and achieved an overall segmentation accuracy lower than the intra-observer variability for the epicardial border (i.e. on average a mean absolute error of 1.5mm and a Hausdorff distance of 5.1mm) with 11% of outliers. Moreover, we demonstrate that our method can closely reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.96 and a mean absolute error of 7.6ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.83 and an absolute mean error of 5.0%, producing scores that are slightly below the intra-observer margin. Based on this observation, areas for improvement are suggested.
Detecting Communities in Heterogeneous Multi-Relational Networks: A Message Passing based Approach

Community is a common characteristic of networks including social networks, biological networks, computer and information networks, to name a few. Community detection is a basic step for exploring and analysing these network data. Typically, homogenous network is a type of networks which consists of only one type of objects with one type of links connecting them. There has been a large body of developments in models and algorithms to detect communities over it. However, real-world networks naturally exhibit heterogeneous qualities appearing as multiple types of objects with multi-relational links connecting them. Those heterogeneous information could facilitate the community detection for its constituent homogeneous networks, but has not been fully explored. In this paper, we exploit heterogeneous multi-relational networks (HMRNet) and propose an efficient message passing based algorithm to simultaneously detect communities for all homogeneous networks. Specifically, an HMRNet is reorganized into a hierarchical structure with homogeneous networks as its layers and heterogeneous links connecting them. To detect communities in such an HMRNet, the problem is formulated as a maximum a posterior (MAP) over a factor graph. Finally a message passing based algorithm is derived to find a best solution of the MAP problem. Evaluation on both synthetic and real-world networks confirms the effectiveness of the proposed method.
Spin–phonon coupling and thermodynamic behaviour in YCrO3 and LaCrO3: inelastic neutron scattering and lattice dynamics

We report detailed temperature-dependent inelastic neutron scattering and ab initio lattice dynamics investigation of magnetic perovskites YCrO3 and LaCrO3. The magnetic neutron scattering from the Cr ions exhibits significant changes with temperature and dominates at low momentum transfer regime. Ab initio calculations performed including magnetic interactions show that the effect of magnetic interactions is very significant on the low- as well as high-energy phonon modes. We have also shown that the inelastic neutron spectrum of YCrO3 mimics the magnon spectrum from a G-type antiferromagnetic system, which is consistent with previously reported magnetic structure in the compound. The pressure-dependent ab initio lattice dynamics calculations are used to calculate the anisotropic thermal expansion behaviour in orthorhombic YCrO3, which is in excellent agreement with the available experimental data in the paramagnetic phase. We identify that the low energy anharmonic phonon modes involving Y vibrations contribute maximum to the thermal expansion behaviour.
Photometric and kinematic study of the three intermediate age open clusters NGC 381, NGC 2360, and Berkeley 68

We present UBVRcIc photometric study of three intermediate age open star clusters NGC 381, NGC 2360, and Berkeley 68 (Be 68). We examine the cluster membership of stars using recently released Gaia DR2 proper motions and obtain a total of 116, 332, and 264 member stars in these three clusters. The mean reddening of E(B - V) = 0.36+/-0.04, 0.08+/-0.03, and 0.52+/-0.04 mag are found in the direction of these clusters where we observe an anomalous reddening towards NGC 381. We fitted the solar metallicity isochrones to determine age and distance of the clusters which are found to be log(Age) = 8.65+/-0.05, 8.95+/-0.05, and 9.25+/-0.05 yr with the respective distance of 957+/-152, 982+/-132, and 2554+/-387 pc for the clusters NGC 381, NGC 2360, and Be 68. A two-stage power law in the mass function (MF) slope is observed in the cluster NGC 381, however, we observe only a single MF slope in the clusters NGC 2360 and Be68. To study a possible spatial variation in the slope of MF we estimate slopes separately in the inner and the outer regions of these clusters and notice a steeper slope in outer region. The dynamic study of these clusters reveals deficiency of low-mass stars in their inner regions suggesting the mass segregation process in all these clusters. The relaxation times of 48.5, 78.9, and 87.6 Myr are obtained for the clusters NGC 381, NGC 2360, and Be 68, respectively which are well below to their respective ages. This suggests that all the clusters are dynamically relaxed.
Multi-Variate Temporal GAN for Large Scale Video Generation

In this paper, we present a network architecture for video generation that models spatio-temporal consistency without resorting to costly 3D architectures. In particular, we elaborate on the components of noise generation, sequence generation, and frame generation. The architecture facilitates the information exchange between neighboring time points, which improves the temporal consistency of the generated frames both at the structural level and the detailed level. The approach achieves state-of-the-art quantitative performance, as measured by the inception score, on the UCF-101 dataset, which is in line with a qualitative inspection of the generated videos. We also introduce a new quantitative measure that uses downstream tasks for evaluation.
Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors

This work presents an intuitive method to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. To provide radiologists with tumor locations, we adopt a segmentation framework to classify CT volumes by checking if at least a sufficient number of voxels is segmented as tumors. To quantitatively analyze our method, we collect and voxelwisely label a new abdominal CT dataset containing 376 cases with both arterial and venous phases available for each case, in which 228 cases were diagnosed with PNETs while the remaining 148 cases are normal, which is currently the largest dataset for PNETs to the best of our knowledge. In order to incorporate rich knowledge from radiologists to our framework, we annotate dilated pancreatic duct as well, which is regarded as an abnormality indicator. Quantitatively, our approach outperforms state-of-the-art segmentation networks and achieves a sensitivity of 89.47% at a specificity of 81.08%, which indicates a potential direction to achieve a clinical impact related to cancer diagnosis by earlier tumor detection.
Quantitative structure of stable sets in arbitrary finite groups

We show that a $k$-stable set in a finite group can be approximated, up to given error $\epsilon>0$, by left cosets of a subgroup of index $\epsilon^{\text{-}O_k(1)}$. This improves the bound in a similar result of Terry and Wolf on stable arithmetic regularity in finite abelian groups, and leads to a quantitative account of work of the author, Pillay, and Terry on stable sets in arbitrary finite groups. We also prove an analogous result for finite stable sets of small tripling in arbitrary groups, which provides a quantitative version of recent work by Martin-Pizarro, Palacin, and Wolf. Our proofs use results on VC-dimension, and a finitization of model-theoretic techniques from stable group theory.
CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection

In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID). GFSID aims to discriminate a joint label space consisting of both existing intents which have enough labeled data and novel intents which only have a few examples for each class. To approach this problem, we propose a novel model, Conditional Text Generation with BERT (CG-BERT). CG-BERT effectively leverages a large pre-trained language model to generate text conditioned on the intent label. By modeling the utterance distribution with variational inference, CG-BERT can generate diverse utterances for the novel intents even with only a few utterances available. Experimental results show that CG-BERT achieves state-of-the-art performance on the GFSID task with 1-shot and 5-shot settings on two real-world datasets.
A Novel Greedy Kaczmarz Method For Solving Consistent Linear Systems

With a quite different way to determine the working rows, we propose a novel greedy Kaczmarz method for solving consistent linear systems. Convergence analysis of the new method is provided. Numerical experiments show that, for the same accuracy, our method outperforms the greedy randomized Kaczmarz method and the relaxed greedy randomized Kaczmarz method introduced recently by Bai and Wu [Z.Z. BAI AND W.T. WU, On greedy randomized Kaczmarz method for solving large sparse linear systems, SIAM J. Sci. Comput. , 40 (2018), pp. A592--A606; Z.Z. BAI AND W.T. WU, On relaxed greedy randomized Kaczmarz methods for solving large sparse linear systems, Appl. Math. Lett. , 83 (2018), pp. 21--26] in term of the computing time.
The $\ell^p$-metrization of functors with finite supports

Let $p\in[1,\infty]$ and $F:\mathbf{Set}\to\mathbf{Set}$ be a functor with finite supports in the category $\mathbf{Set}$ of sets. Given a non-empty metric space $(X,d_X)$, we introduce the distance $d^p_{FX}$ on the functor-space $FX$ as the largest distance such that for every $n\in\mathbb N$ and $a\in Fn$ the map $X^n\to FX$, $f\mapsto Ff(a)$, is non-expanding with respect to the $\ell^p$-metric $d^p_{X^n}$ on $X^n$. We prove that the distance $d^p_{FX}$ is a pseudometric if and only if the functor $F$ preserves singletons; $d^p_{FX}$ is a metric if $F$ preserves singletons and one of the following conditions holds: (1) the metric space $(X,d_X)$ is Lipschitz disconnected, (2) $p=1$, (3) the functor $F$ has finite degree, (4) $F$ preserves supports. We prove that for any Lipschitz map $f:(X,d_X)\to (Y,d_Y)$ between metric spaces the map $Ff:(FX,d^p_{FX})\to (FY,d^p_{FY})$ is Lipschitz with Lipschitz constant $\mathrm{Lip}(Ff)\le \mathrm{Lip}(f)$. If the functor $F$ is finitary, has finite degree (and preserves supports), then $F$ preserves uniformly continuous function, coarse functions, coarse equivalences, asymptotically Lipschitz functions, quasi-isometries (and continuous functions). For many dimension functions we prove the formula $\dim F^pX\le\mathrm{deg}(F)\cdot\dim X$. Using injective envelopes, we introduce a modification $\check d^p_{FX}$ of the distance $d^p_{FX}$ and prove that the functor $\check F^p:\mathbf{Dist}\to\mathbf{Dist}$, $\check F^p:(X,d_X)\mapsto (FX,\check d^p_{FX})$, in the category $\mathbf{Dist}$ of distance spaces preserves Lipschitz maps and isometries between metric spaces.
The collection Virtual Machine: an abstraction for multi-frontend multi-backend data analysis

Getting the best performance from the ever-increasing number of hardware platforms has been a recurring challenge for data processing systems. In recent years, the advent of data science with its increasingly numerous and complex types of analytics has made this challenge even more difficult. In practice, system designers are overwhelmed by the number of combinations and typically implement a single analytics type on one platform, leading to repeated implementation effort---and a plethora of semi-compatible tools for data scientists. In this paper, we propose the "Collection Virtual Machine" (or CVM)---an extensible compiler framework designed to keep the specialization process of data analytics systems tractable. It can capture at the same time the essence of a large span of low-level, hardware-specific implementation techniques as well as high-level operations of different types of analyses. At its core lies a language for defining nested, collection-oriented intermediate representations (IRs). Frontends produce programs in their IR flavors defined in that language, which get optimized through a series of rewritings (possibly changing the IR flavor multiple times) until the program is finally expressed in an IR of platform-specific operators. While reducing the overall implementation effort, this also improves the interoperability of both analyses and hardware platforms. We have used CVM successfully to build specialized backends for platforms as diverse as multi-core CPUs, RDMA clusters, and serverless computing infrastructure in the cloud and expect similar results for many more frontends and hardware platforms in the near future.
Certifying Provenance of Scientific Datasets with Self-sovereign Identity and Verifiable Credentials

In order to increase the value of scientific datasets and improve research outcomes, it is important that only trustworthy data is used. This paper presents mechanisms by which scientists and the organisations they represent can certify the authenticity of characteristics and provenance of any datasets they publish so that secondary users can inspect and gain confidence in the qualities of data they source. By drawing on data models and protocols used to provide self-sovereign ownership of identity and personal data to individuals, we conclude that providing self-sovereignty to data assets offers a viable approach for institutions to certify qualities of their datasets in a cryptography secure manner, and enables secondary data users to efficiently perform verification of the authenticity of such certifications. By building upon emerging standards for decentralized identification and cryptographically verifiable credentials, we envisage an infrastructure of tools being developed to foster adoption of metadata certification schemes, and improving the quality of information provided in support of shared data assets.
Dk Gravitational Instantons as Superpositions of Atiyah–Hitchin and Taub–NUT Geometries

We obtain $D_k$ ALF gravitational instantons by a gluing construction which captures, in a precise and explicit fashion, their interpretation as non-linear superpositions of the moduli space of centred charge two monopoles, equipped with the Atiyah-Hitchin metric, and $k$ copies of the Taub-NUT manifold. The construction proceeds from a finite set of points in euclidean space, reflection symmetric about the origin, and depends on an adiabatic parameter which is incorporated into the geometry as a fifth dimension. Using a formulation in terms of hyperKahler triples on manifolds with boundaries, we show that the constituent Atiyah-Hitchin and Taub-NUT geometries arise as boundary components of the 5-dimensional geometry as the adiabatic parameter is taken to zero.
Neural Machine Translation with Imbalanced Classes

We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. Classifiers are known to perform better with balanced class distributions during training. Since the Zipfian nature of languages causes imbalanced classes, we explore the effect of class imbalance on NMT. We analyze the effect of vocabulary sizes on NMT performance and reveal an explanation for 'why' certain vocabulary sizes are better than others.