Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Hierarchical Clustering for Directed Networks: An Axiomatic Approach

We provide a complete taxonomic characterization of robust hierarchical clustering methods for directed networks following an axiomatic approach. We begin by introducing three practical properties associated with the notion of robustness in hierarchical clustering: linear scale preservation, stability, and excisiveness. Linear scale preservation enforces imperviousness to change in units of measure whereas stability ensures that a bounded perturbation in the input network entails a bounded perturbation in the clustering output. Excisiveness refers to the local consistency of the clustering outcome. Algorithmically, excisiveness implies that we can reduce computational complexity by only clustering a subset of our data while theoretically guaranteeing that the same hierarchical outcome would be observed when clustering the whole dataset. In parallel to these three properties, we introduce the concept of representability, a generative model for describing clustering methods through the specification of their action on a collection of networks. Our main result is to leverage this generative model to give a precise characterization of all robust -- i.e., excisive, linear scale preserving, and stable -- hierarchical clustering methods for directed networks. We also address the implementation of our methods and describe an application to real data.
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI

To build a robust and practical content-based image retrieval (CBIR) system applicable to clinical brain MRI databases, we propose a new framework, disease-oriented image embedding with pseudo-scanner standardization (DI-PSS). It consists of two core techniques: data harmonization to absorb differences caused by different scanning environments and an algorithm to generate low-dimensional embeddings suitable for disease classification. Until now, there have been very few studies aimed at CBIR of brain MRI. Even in the harmonization of scanners, which is an important prerequisite technique for CBIR, only a limited number of studies have been conducted on T1-weighted MRI, which has collected a vast amount of clinical data. Recently proposed methods need to correctly estimate the domain (i.e., dataset, scanner) of each data in advance to remove environment-dependent information from low-dimensional embedding, which is not an easy task. With DI-PSS, each brain image is pseudo-transformed into a brain image taken with a given reference scanner. Then, 3D convolutioinal autoencoders (3D-CAE) trained with deep metric learning generate low-dimensional embeddings that better reflect the characteristics of the disease. In this study, DI-PSS reduced the variability of distance in low-dimensional embedding between Alzheimer’s disease (AD) and clinically normal (CN) patients, caused by differences in scanners and datasets, by 15.8-22.6% and 18.0-29.9%, respectively, compared to the baseline. This improved the ability of spectral clustering to classify AD and CN by 6.2% in average accuracy and 10.7% in macro-F1. Our method has the advantage of not requiring difficult domain prediction tasks in advance, and can effectively utilize the big data of T1-weighted MR images. Given the potential of the DI-PSS for harmonizing images scanned by MRI scanners that were not used to scan the training data, it is well suited for application to a large number of legacy MRIs captured in heterogeneous environments.
Systematic Generation of Conformance Tests for JavaScript

JavaScript implementations are tested for conformance to the ECMAScript standard using a large handwritten test suite. Not only in this a tedious approach, it also relies solely on the natural language specification for differentiating behaviors, while hidden implementation details can also affect behavior and introduce divergences. We propose to generate conformance tests through dynamic symbolic execution of polyfills, dropin replacements for newer JavaScript language features that are not yet widely supported. We then run these generated tests against multiple implementations of JavaScript, using a majority vote to identify the correct behavior. To facilitate test generation for polyfill code, we introduce a model for structured symbolic inputs that is suited to the dynamic nature of JavaScript. In our evaluation, we found 17 divergences in the widely used core-js polyfill and were able to increase branch coverage in interpreter code by up to 15%. Because polyfills are typically written even before standardization, our approach will allow to maintain and extend standardization test suites with reduced effort.
Toward the Understanding of Deep Text Matching Models for Information Retrieval

Semantic text matching is a critical problem in information retrieval. Recently, deep learning techniques have been widely used in this area and obtained significant performance improvements. However, most models are black boxes and it is hard to understand what happened in the matching process, due to the poor interpretability of deep learning. This paper aims at tackling this problem. The key idea is to test whether existing deep text matching methods satisfy some fundamental heuristics in information retrieval. Specifically, four heuristics are used in our study, i.e., term frequency constraint, term discrimination constraint, length normalization constraints, and TF-length constraint. Since deep matching models usually contain many parameters, it is difficult to conduct a theoretical study for these complicated functions. In this paper, We propose an empirical testing method. Specifically, We first construct some queries and documents to make them satisfy the assumption in a constraint, and then test to which extend a deep text matching model trained on the original dataset satisfies the corresponding constraint. Besides, a famous attribution based interpretation method, namely integrated gradient, is adopted to conduct detailed analysis and guide for feasible improvement. Experimental results on LETOR 4.0 and MS Marco show that all the investigated deep text matching methods, both representation and interaction based methods, satisfy the above constraints with high probabilities in statistics. We further extend these constraints to the semantic settings, which are shown to be better satisfied for all the deep text matching models. These empirical findings give clear understandings on why deep text matching models usually perform well in information retrieval. We believe the proposed evaluation methodology will be useful for testing future deep text matching models.
A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning

Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to apply on a large scale but also have poor robustness to irregular objects, e.g., tables, chairs, shelves, etc. In this paper, we propose a vision-based collision avoidance framework to solve the challenging problem. Our method attempts to estimate the depth and incorporate the semantic information from RGB data to obtain a new form of data, pseudo-laser data, which combines the advantages of visual information and laser information. Compared to traditional laser data that only contains the one-dimensional distance information captured at a certain height, our proposed pseudo-laser data encodes the depth information and semantic information within the image, which makes our method more effective for irregular obstacles. Besides, we adaptively add noise to the laser data during the training stage to increase the robustness of our model in the real world, due to the estimated depth information is not accurate. Experimental results show that our framework achieves state-of-the-art performance in several unseen virtual and real-world scenarios.
Ferroelectric Polarization Reversal vs Pump Spot Shape

It has recently been shown that the polarization of a ferroelectric can be strongly affected by light pumping or even transiently reversed by mid-IR pumping within a record short subpicosecond timescale. It was then suggested that the reversal of polarization is most likely prevented by the depolarizing field that appears during the process. To minimize the effect of the depolarizing field, I propose to create an elongated domain by choosing a corresponding pump spot shape. I calculate the polarization dynamics for a particular experimental setup with mid-IR pumping and show that the polarization reversal is possible in this case.
X-ray spectra, light curves and SEDs of blazars frequently observed by Swift

Blazars research is one of the hot topics of contemporary extragalactic astrophysics. That is because these sources are the most abundant type of extragalactic γ-ray sources and are suspected to play a central role in multimessenger astrophysics. We have used Swift$\_$xrtproc, a tool to carry out an accurate spectral and photometric analysis of the Swift-XRT data of all blazars observed by Swift at least 50 times between December 2004 and the end of 2020. We present a database of X-ray spectra, best-fit parameter values, count rates and flux estimations in several energy bands of over 31 000 X-ray observations and single snapshots of 65 blazars. The results of the X-ray analysis have been combined with other multifrequency archival data to assemble the broad-band Spectral Energy Distributions (SEDs) and the long-term light curves of all sources in the sample. Our study shows that large X-ray luminosity variability on different time-scales is present in all objects. Spectral changes are also frequently observed with a ‘harder-when-brighter’ or ‘softer-when-brighter’ behaviour depending on the SED type of the blazars. The peak energy of the synchrotron component (νpeak) in the SED of HBL blazars, estimated from the log-parabolic shape of their X-ray spectra, also exhibits very large changes in the same source, spanning a range of over two orders of magnitude in Mrk421 and Mrk501, the objects with the best data sets in our sample.
Main results of the TUS experiment on board the Lomonosov satellite

The TUS detector was the first space-based mission aimed for ultra-high-energy cosmic ray (UHECR) measurements. The detector was designed to register the fluorescent signal of extensive air showers (EAS) developing in the night atmosphere of Earth in the UV range of 300-400 nm. TUS was launched on board the Lomonosov satellite in April, 2016 and operated till December, 2017. Almost 90 thousand events were recorded during the mission, among them lightning discharges, meteors, transient luminous events, polar lights and anthropogenic signals. Some puzzling bright UV flashes in a clear sky far from possible artificial sources were also registered. Besides this, a number of EAS candidates were found in the TUS database. The majority of candidates analysed so far were recorded above populated areas near airports or similar objects, and the energy of the signals corresponds to at least 1 ZeV if they were generated by an UHECR, which does not allow one to consider these events as UHECRs. We briefly present the main results of the TUS experiment and discuss its importance for the development of the future orbital missions.
Cabibbo suppressed single pion production off the nucleon induced by antineutrinos

M. Benitez Galan, ∗ M. Rafi Alam, † and I. Ruiz Simo ‡ Departamento de F́ısica Atómica, Molecular y Nuclear, Facultad de Ciencias, Universidad de Granada, E-18071, Granada, Spain Department of Physics, Aligarh Muslim University, Aligarh-202 002, India Departamento de F́ısica Atómica, Molecular y Nuclear and Instituto Interuniversitario Carlos I de F́ısica Teórica y Computacional, Facultad de Ciencias, Universidad de Granada, E-18071, Granada, Spain
Force-feedback based Whole-body Stabilizer for Position-Controlled Humanoid Robots

This paper studies stabilizer design for position-controlled humanoid robots. Stabilizers are an essential part for position-controlled humanoids, whose primary objective is to adjust the control input sent to the robot to assist the tracking controller to better follow the planned reference trajectory. To achieve this goal, this paper develops a novel force-feedback based whole-body stabilizer that fully exploits the six-dimensional force measurement information and the whole-body dynamics to improve tracking performance. Relying on rigorous analysis of whole-body dynamics of position-controlled humanoids under unknown contact, the developed stabilizer leverages quadratic-programming based technique that allows cooperative consideration of both the center-of-mass tracking and contact force tracking. The effectiveness of the proposed stabilizer is demonstrated on the UBTECH Walker robot in the MuJoCo simulator. Simulation validations show a significant improvement in various scenarios as compared to commonly adopted stabilizers based on the zero-moment-point feedback and the linear inverted pendulum model.
MMChat: Multi-Modal Chat Dataset on Social Media

Incorporating multi-modal contexts in conversation is an important step for developing more engaging dialogue systems. In this work, we explore this direction by introducing MMChat: a large scale Chinese multi-modal dialogue corpus (32.4M raw dialogues and 120.84K filtered dialogues). Unlike previous corpora that are crowd-sourced or collected from fictitious movies, MMChat contains image-grounded dialogues collected from real conversations on social media, in which the sparsity issue is observed. Specifically, image-initiated dialogues in common communications may deviate to some non-image-grounded topics as the conversation proceeds. To better investigate this issue, we manually annotate 100K dialogues from MMChat and further filter the corpus accordingly, which yields MMChat-hf. We develop a benchmark model to address the sparsity issue in dialogue generation tasks by adapting the attention routing mechanism on image features. Experiments demonstrate the usefulness of incorporating image features and the effectiveness in handling the sparsity of image features.
Multi-Objective Recommendations: A Tutorial

Recommender systems (RecSys) have been well developed to assist user decision making. Traditional RecSys usually optimize a single objective (e.g., rating prediction errors or ranking quality) in the model. There is an emerging demand in multi-objective optimization recently in RecSys, especially in the area of multi-stakeholder and multi-task recommender systems. This article provides an overview of multi-objective recommendations, followed by the discussions with case studies. The document is considered as a supplementary material for our tutorial on multi-objective recommendations at ACM SIGKDD 2021.
Challenges for cognitive decoding using deep learning methods

In cognitive decoding, researchers aim to characterize a brain region's representations by identifying the cognitive states (e.g., accepting/rejecting a gamble) that can be identified from the region's activity. Deep learning (DL) methods are highly promising for cognitive decoding, with their unmatched ability to learn versatile representations of complex data. Yet, their widespread application in cognitive decoding is hindered by their general lack of interpretability as well as difficulties in applying them to small datasets and in ensuring their reproducibility and robustness. We propose to approach these challenges by leveraging recent advances in explainable artificial intelligence and transfer learning, while also providing specific recommendations on how to improve the reproducibility and robustness of DL modeling results.
A qualitative study of architectural design issues in DevOps

Software architecture is critical in succeeding with Development and Operations (DevOps). However, designing software architectures that enable and support DevOps (DevOps‐driven software architectures) is a challenge for organizations. We assert that one of the essential steps towards characterizing DevOps‐driven architectures is to understand architectural design issues raised in DevOps. At the same time, some of the architectural issues that emerge in the DevOps context (and their corresponding architectural practices or tactics) may stem from the context (i.e., domain) and characteristics of software organizations. To this end, we conducted a mixed‐methods study that consists of a qualitative case study of two teams in a company during their DevOps transformation and a content analysis of Stack Overflow and DevOps Stack Exchange posts to understand architectural design issues in DevOps. Our study found eight specific and contextual architectural design issues faced by the two teams and classified architectural design issues discussed in Stack Overflow and DevOps Stack Exchange into 11 groups. Our aggregated results reveal that the main characteristics of DevOps‐driven architectures are being loosely coupled and prioritizing deployability, testability, supportability, and modifiability over other quality attributes. Finally, we discuss some concrete implications for research and practice.
Soccer line mark segmentation with stochastic watershed transform

Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The ﬁrst step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the ﬁeld of play. Most existing proposals for line detection rely on edge detection and Hough transform, but optical distortion and extraneous edges cause inaccurate or spurious detections of line markings. We propose a novel strategy to automatically and accurately segment line markings based on a stochastic watershed transform that is robust to optical distortions, since it makes no assumptions about line straightness, and is unaﬀected by the presence of players or the ball in the ﬁeld of play. Firstly, the playing ﬁeld as a whole is segmented completely eliminating the stands and perimeter boards. Then the line markings are extracted. The strategy has been tested on a new and public database composed by 60 annotated images from matches in ﬁve stadiums. The results obtained have proven that the proposed segmentation algorithm allows successful and precise detection of most line mark pixels.
DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans

X-ray imaging in Digital Imaging and Communications in Medicine (DICOM) format is the most commonly used imaging modality in clinical practice, resulting in vast, non-normalized databases. This leads to an obstacle in deploying artificial intelligence (AI) solutions for analyzing medical images, which often requires identifying the right body part before feeding the image into a specified AI model. This challenge raises the need for an automated and efficient approach to classifying body parts from X-ray scans. Unfortunately, to the best of our knowledge, there is no open tool or framework for this task to date. To fill this lack, we introduce a DICOM Imaging Router that deploys deep convolutional neural networks (CNNs) for categorizing unknown DICOM X-ray images into five anatomical groups: abdominal, adult chest, pediatric chest, spine, and others. To this end, a large-scale X-ray dataset consisting of 16,093 images has been collected and manually classified. We then trained a set of state-of-the-art deep CNNs using a training set of 11,263 images. These networks were then evaluated on an independent test set of 2,419 images and showed superior performance in classifying the body parts. Specifically, our best performing model (i.e., MobileNet-V1) achieved a recall of 0.982 (95% CI, 0.977-0.988), a precision of 0.985 (95% CI, 0.975-0.989) and a F1-score of 0.981 (95% CI, 0.976-0.987), whilst requiring less computation for inference (0.0295 second per image). Our external validity on 1,000 X-ray images shows the robustness of the proposed approach across hospitals. These remarkable performances indicate that deep CNNs can accurately and effectively differentiate human body parts from X-ray scans, thereby providing potential benefits for a wide range of applications in clinical settings. The dataset, codes, and trained deep learning models from this study will be made publicly available from our project website at https://vindr.ai/.
Autoencoders as Tools for Program Synthesis

Recently there have been many advances in research on language modeling of source code. Applications range from code suggestion and completion to code summarization. However, complete program synthesis of industry-grade programming languages remains an open problem. In this work, we introduce and experimentally validate a variational autoencoder model for program synthesis of industry-grade programming languages. This model makes use of the inherent tree structure of code and can be used in conjunction with gradient free optimization techniques like evolutionary methods to generate programs that maximize a given fitness function, for instance, passing a set of test cases. A demonstration is avaliable at https://tree2tree.app
A Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission

We present a Python tool to generate a standard dataset from solar images that allows for user-defined selection criteria and a range of pre-processing steps. Our Python tool works with all image products from both the Solar and Heliospheric Observatory (SoHO) and Solar Dynamics Observatory (SDO) missions. We discuss a dataset produced from the SoHO mission's multi-spectral images which is free of missing or corrupt data as well as planetary transits in coronagraph images, and is temporally synced making it ready for input to a machine learning system. Machine-learning-ready images are a valuable resource for the community because they can be used, for example, for forecasting space weather parameters. We illustrate the use of this data with a 3-5 day-ahead forecast of the north-south component of the interplanetary magnetic field (IMF) observed at Lagrange point one (L1). For this use case, we apply a deep convolutional neural network (CNN) to a subset of the full SoHO dataset and compare with baseline results from a Gaussian Naive Bayes classifier.