Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Nakamoto Consensus, Revisited

In the Bitcoin white paper[1], Nakamoto proposed a very simple Byzantine fault tolerant consensus algorithm that is also known as Nakamoto consensus. Despite its simplicity, some existing analysis of Nakamoto consensus appears to be long and involved. In this technical report, we aim to make such analysis simple and transparent so that we can teach senior undergraduate students and graduate students in our institutions. This report is largely based on a 3-hour tutorial given by one of the authors in June[2].
A Framework for the Robust Evaluation of Sound Event Detection

This work defines a new framework for performance evaluation of polyphonic sound event detection (SED) systems, which overcomes the limitations of the conventional collar-based event decisions, event F-scores and event error rates. The proposed framework introduces a definition of event detection that is more robust against labelling subjectivity. It also resorts to polyphonic receiver operating characteristic (ROC) curves to deliver more global insight into system performance than F1-scores, and proposes a reduction of these curves into a single polyphonic sound detection score (PSDS), which allows system comparison independently from operating points (OPs). The presented method also delivers better insight into data biases and classification stability across sound classes. Furthermore, it can be tuned to varying applications in order to match a variety of user experience requirements. The benefits of the proposed approach are demonstrated by re-evaluating the baseline and two of the top-performing systems from DCASE 2019 Task 4.
Flying Unicorn: Developing a Game for a Quantum Computer

What is it like to create a game for a quantum computer? With its ability to perform calculations and processing in a distinctly different way than classical computers, quantum computing has the potential for becoming the next revolution in information technology. Flying Unicorn is a game developed for a quantum computer. It is designed to explore the properties of superposition and uncertainty. In this paper, we explore the development of the game, using Python Qiskit. We detail the usage of qubits and an implementation of Grover's search. Finally, we compare and contrast a classical implementation of the game against the quantum computing design, including execution and performance on a physical quantum computer at IBMQ.
A super-Earth and a mini-Neptune around Kepler-59

We characterize the radii and masses of the star and planets in the Kepler-59 system, as well as their orbital parameters. The star parameters are determined through a standard spectroscopic analysis, resulting in a mass of $1.359\pm 0.155\, \mathrm{M}_\odot$ and a radius of $1.367\pm 0.078\, \mathrm{R}_\odot$. The obtained planetary radii are $1.5\pm 0.1\, R_\oplus$ for the inner and $2.2\pm 0.1\, R_\oplus$ for the outer planet. The orbital parameters and the planetary masses are determined by the inversion of Transit Timing Variations (TTV) signals. We consider two different data sets: one provided by Holczer et al. (2016), with TTVs only for Kepler-59c, and the other provided by Rowe et al. (2015), with TTVs for both planets. The inversion method applies an algorithm of Bayesian inference (MultiNest) combined with an efficient N-body integrator (Swift). For each of the data set, we found two possible solutions, both having the same probability according to their corresponding Bayesian evidences. All four solutions appear to be indistinguishable within their 2-σ uncertainties. However, statistical analyses show that the solutions from Rowe et al. (2015) data set provide a better characterization. The first solution infers masses of $5. 3_{-2.1}^{+4.0}~M_{\mathrm{\oplus }}$ and $4. 6_{-2.0}^{+3.6}~M_{\mathrm{\oplus }}$ for the inner and outer planet, respectively, while the second solution gives masses of $3.0^{+0.8}_{-0.8}~M_{\mathrm{\oplus }}$ and $2.6^{+0.9}_{-0.8}~M_{\mathrm{\oplus }}$. These values point to a system with an inner super-Earth and an outer mini-Neptune. A dynamical study shows that the planets have almost co-planar orbits with small eccentricities (e < 0.1), close to the 3:2 mean motion resonance. A stability analysis indicates that this configuration is stable over million years of evolution.
Center-Outward R-Estimation for Semiparametric VARMA Models

Abstract We propose a new class of R-estimators for semiparametric VARMA models in which the innovation density plays the role of the nuisance parameter. Our estimators are based on the novel concepts of multivariate center-outward ranks and signs. We show that these concepts, combined with Le Cam’s asymptotic theory of statistical experiments, yield a class of semiparametric estimation procedures, which are efficient (at a given reference density), root-n consistent, and asymptotically normal under a broad class of (possibly non-elliptical) actual innovation densities. No kernel density estimation is required to implement our procedures. A Monte Carlo comparative study of our R-estimators and other routinely applied competitors demonstrates the benefits of the novel methodology, in large and small sample. Proofs, computational aspects, and further numerical results are available in the supplementary materials. Supplementary materials for this article are available online.
An LSM-based tuple compaction framework for Apache AsterixDB

Document database systems store self-describing semi-structured records, such as JSON, "as-is" without requiring the users to pre-define a schema. This provides users with the flexibility to change the structure of incoming records without worrying about taking the system offline or hindering the performance of currently running queries. However, the flexibility of such systems does not free. The large amount of redundancy in the records can introduce an unnecessary storage overhead and impact query performance. Our focus in this paper is to address the storage overhead issue by introducing a tuple compactor framework that infers and extracts the schema from self-describing semi-structured records during the data ingestion. As many prominent document stores, such as MongoDB and Couchbase, adopt Log Structured Merge (LSM) trees in their storage engines, our framework exploits LSM lifecycle events to piggyback the schema inference and extraction operations. We have implemented and empirically evaluated our approach to measure its impact on storage, data ingestion, and query performance in the context of Apache AsterixDB.
On the Difficulty of Warm-Starting Neural Network Training

In many real-world deployments of machine learning systems, data arrive piecemeal. These learning scenarios may be passive, where data arrive incrementally due to structural properties of the problem (e.g., daily financial data) or active, where samples are selected according to a measure of their quality (e.g., experimental design). In both of these cases, we are building a sequence of models that incorporate an increasing amount of data. We would like each of these models in the sequence to be performant and take advantage of all the data that are available to that point. Conventional intuition suggests that when solving a sequence of related optimization problems of this form, it should be possible to initialize using the solution of the previous iterate---to"warm start"the optimization rather than initialize from scratch---and see reductions in wall-clock time. However, in practice this warm-starting seems to yield poorer generalization performance than models that have fresh random initializations, even though the final training losses are similar. While it appears that some hyperparameter settings allow a practitioner to close this generalization gap, they seem to only do so in regimes that damage the wall-clock gains of the warm start. Nevertheless, it is highly desirable to be able to warm-start neural network training, as it would dramatically reduce the resource usage associated with the construction of performant deep learning systems. In this work, we take a closer look at this empirical phenomenon and try to understand when and how it occurs. Although the present investigation did not lead to a solution, we hope that a thorough articulation of the problem will spur new research that may lead to improved methods that consume fewer resources during training.
Sufficient Conditions for Exact Semi-definite Relaxation of Optimal Power Flow in Unbalanced Multiphase Radial Networks

This paper proves that in an unbalanced multi-phase network with a tree topology, the semi-definite programming relaxation of optimal power flow problems is exact when critical buses are not adjacent to each other. Here a critical bus either contributes directly to the cost function or is where an injection constraint is tight at optimality. Our result generalizes a sufficient condition for exact relaxation in single-phase tree networks to tree networks with arbitrary number of phases.
BOBBY2: Buffer Based Robust High-Speed Object Tracking

In this work, a novel high-speed single object tracker that is robust against non-semantic distractor exemplars is introduced; dubbed BOBBY2. It incorporates a novel exemplar buffer module that sparsely caches the target's appearance across time, enabling it to adapt to potential target deformation. As for training, an augmented ImageNet-VID dataset was used in conjunction with the one cycle policy, enabling it to reach convergence with less than 2 epoch worth of data. For validation, the model was benchmarked on the GOT-10k dataset and on an additional small, albeit challenging custom UAV dataset collected with the TU-3 UAV. We demonstrate that the exemplar buffer is capable of providing redundancies in case of unintended target drifts, a desirable trait in any middle to long term tracking. Even when the buffer is predominantly filled with distractors instead of valid exemplars, BOBBY2 is capable of maintaining a near-optimal level of accuracy. BOBBY2 manages to achieve a very competitive result on the GOT-10k dataset and to a lesser degree on the challenging custom TU-3 dataset, without fine-tuning, demonstrating its generalizability. In terms of speed, BOBBY2 utilizes a stripped down AlexNet as feature extractor with 63% less parameters than a vanilla AlexNet, thus being able to run at a competitive 85 FPS.
RTFM: Generalising to Novel Environment Dynamics via Reading

Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments. We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations. We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information. In addition, we propose txt2$\pi$, a model that captures three-way interactions between the goal, document, and observations. On RTFM, txt2$\pi$ generalises to new environments with dynamics not seen during training via reading. Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM. Through curriculum learning, txt2$\pi$ produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.
An ionized accretion disc wind in Hercules X-1

Hercules X-1 is one of the best-studied highly magnetized neutron star X-ray binaries with a wealth of archival data. We present the discovery of an ionized wind in its X-ray spectrum when the source is in the high state. The wind detection is statistically significant in most of the XMM–Newton observations, with velocities ranging from 200 to 1000 km s−1. Observed features in the iron K band can be explained by both wind absorption and a forest of iron emission lines. However, we also detect nitrogen, oxygen, and neon absorption lines at the same systematic velocity in the high-resolution Reflection Grating Spectrometer grating spectra. The wind must be launched from the accretion disc, and could be the progenitor of the ultraviolet absorption features observed at comparable velocities, but the latter likely originate at significantly larger distances from the compact object. We find strong correlations between the ionization level of the outflowing material and the ionizing luminosity as well as the superorbital phase. If the luminosity is driving the correlation, the wind could be launched by a combination of Compton heating and radiation pressure. If instead the superorbital phase is the driver for the variations, the observations are likely scanning the wind at different heights above the warped accretion disc. If this is the case, we can estimate the wind mass outflow rate, corrected for the limited launching solid angle, to be roughly 70 per cent of the mass accretion rate.
Deep GMRT 610 MHz observations of the ELAIS N1 field: catalogue and source counts

This is the first of a series of papers based on sensitive 610 MHz observations of the ELAIS N1 field, using the Giant Metrewave Radio Telescope. We describe the observations, processing and source catalogue extraction from a deep image with area of 1.86 deg2 and minimum noise of ∼7.1 μJy beam−1. We compile a catalogue of 4290 sources with flux densities in the range of 28.9 μJy– 0.503 Jy and derive the Euclidean-normalized differential source counts for sources with flux densities brighter than $\rm {35.5\, \mu Jy}$. Our counts show a flattening at 610 MHz flux densities below 1 mJy. Below the break the counts are higher than previous observations at this frequency, but generally consistent with recent models of the low-frequency source population. The radio catalogue is cross-matched against multiwavelength data leading to identifications for 92 per cent and reliable redshifts for 72 per cent of our sample, with 19 per cent of the redshifts based on spectroscopy. For the sources with redshifts, we use radio and X-ray luminosity, optical spectroscopy and mid-infrared colours to search for evidence of the presence of an active galactic nucleus (AGN). We compare our identifications to predictions of the flux density distributions of star-forming galaxies (SFGs) and AGN, and find a good agreement assuming the majority of the sources without redshifts are SFGs. We derive spectral index distributions for a sub-sample. The majority of the sources are steep spectra, with a median spectral index that steepens with frequency: $\mathrm{\alpha ^{610}_{325}\, =\, -0.80\, \pm \, 0.29}$, $\mathrm{\alpha ^{610}_{1400}\, =\, -0.83\, \pm \, 0.31}$, and $\mathrm{\alpha ^{610}_{5000}\, =\, -1.12\, \pm \, 0.15}$.
Implicit Context-aware Learning and Discovery for Streaming Data Analytics

The performance of machine learning model can be further improved if contextual cues are provided as input along with base features that are directly related to an inference task. In offline learning, one can inspect historical training data to identify contextual clusters either through feature clustering, or hand-crafting additional features to describe a context. While offline training enjoys the privilege of learning reliable models based on already-defined contextual features, online training for streaming data may be more challenging -- the data is streamed through time, and the underlying context during a data generation process may change. Furthermore, the problem is exacerbated when the number of possible context is not known. In this study, we propose an online-learning algorithm involving the use of a neural network-based autoencoder to identify contextual changes during training, then compares the currently-inferred context to a knowledge base of learned contexts as training advances. Results show that classifier-training benefits from the automatically discovered contexts which demonstrates quicker learning convergence during contextual changes compared to current methods.
JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation

Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. In this paper, we propose a Joint Spectral Convolutional Network (JSCN) for cross-domain recommendation. JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant user representation with a domain adaptive user mapping module. As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping. The domain adaptive user mapping module can help the incompatible domains to transfer the knowledge across each other. Extensive experiments on 24 Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with 9.2% improvement on recall and 36.4% improvement on MAP compared with state-of-the-art methods. Our code is available online 1.1https://github.com/JimLiu96/JSCN
Timing of open debridement for suspected infection of joint prosthesis: a report on 551 patients

Background The aim of this study was to evaluate the results of an ad hoc approach versus using a protocol, with regard to the recording persistent wound drainage after primary joint prosthesis and the effect on the salvage rate. Methods Charts of patients after primary total joint prostheses were studied. Two-hundred and fifty prostheses formed the retrospective group 1 (ad hoc approach) and 308 prostheses formed the prospectively followed group 2 (protocol group). Results Although the percentage of patients with persistent wound drainage in group 2 was almost twofold that of group 1, the number of debridements carried out was lower; the salvage rate in this group was higher. Conclusions Observation and treatment using a protocol yields a significant increase in the number of persistent wound drainages registered. Besides better registration, using a protocol enables more successful selection of patients in whom open debridement is not necessary.
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning

The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help mitigate such training obstacles, most data augmentation methods have been developed for natural images as in computer vision tasks such as CIFAR, not for medical images. This work helps to fills in this gap by proposing a method for generating new functional Magnetic Resonance Images (fMRI) with realistic brain morphology. This method is tested on a challenging task of predicting antidepressant treatment response from pre-treatment task-based fMRI and demonstrates a 26% improvement in performance in predicting response using augmented images. This improvement compares favorably to state-of-the-art augmentation methods for natural images. Through an ablative test, augmentation is also shown to substantively improve performance when applied before hyperparameter optimization. These results suggest the optimal order of operations and support the role of data augmentation method for improving predictive performance in tasks using fMRI.
Non-Additivity of Nonequilibrium Atom-Surface Forces

The motion-induced drag force acting on a particle moving within a planar cavity - a relevant configuration for many experimental investigations - is calculated. This drag force exhibits a markedly non-additive nature and can evaluate to about an order of magnitude larger than the corresponding additive approximation. Particular focus is placed on the nonequilibrium statistics of the interaction and on the interplay of the system's geometry with the different dissipative processes that simultaneously occur in the system.
Quantum many-body scars from virtual entangled pairs

We study weak ergodicity breaking in a one-dimensional, nonintegrable spin-1 XY model. We construct for it an exact, highly excited eigenstate, which despite its large energy density, can be represented analytically by a finite bond-dimension matrix product state (MPS) with area-law entanglement. Upon a quench to a finite Zeeman field, the state undergoes periodic dynamics with perfect many-body revivals, in stark contrast to other generic initial states which instead rapidly thermalize. This dynamics can be completely understood in terms of the evolution of entangled virtual spin-1/2 degrees of freedom, which in turn underpin the presence of an extensive tower of strong-eigenstate thermalization hypothesis (ETH)-violating many-body eigenstates. The resulting quantum many-body scars are therefore of novel origin. Our results provide important analytical insights into the nature and entanglement structure of quantum many-body scars.
Work sharing as a metric and productivity indicator for administrative workflows

Defining administrative workflow events as a nonlinear dynamics that assume a random ordered or disordered growth rate of information processing, a method has been proposed for large-scale administrative systems that structures hybrid system variables (continuous or discrete) as iterated and attracted to a fixed-point event at which for all possible metric spaces solutions, the modeling of variables from Lyapunov exponential stability point of view allows the projection of system performance to be oriented, that is, the relationship between the number of agents and the number of administrative services within an administrative workflow environment.
Detecting intracranial aneurysm rupture from 3D surfaces using a novel GraphNet approach

Intracranial aneurysm (IA) is a life-threatening blood spot in human's brain if it ruptures and causes cerebral hemorrhage. It is challenging to detect whether an IA has ruptured from medical images. In this paper, we propose a novel graph based neural network named GraphNet to detect IA rupture from 3D surface data. GraphNet is based on graph convolution network (GCN) and is designed for graph-level classification and node-level segmentation. The network uses GCN blocks to extract surface local features and pools to global features. 1250 patient data including 385 ruptured and 865 unruptured IAs were collected from clinic for experiments. The performance on randomly selected 234 test patient data was reported. The experiment with the proposed GraphNet achieved accuracy of 0.82, area-under-curve (AUC) of receiver operating characteristic (ROC) curve 0.82 in the classification task, significantly outperforming the baseline approach without using graph based networks. The segmentation output of the model achieved mean graph-node-based dice coefficient (DSC) score 0.88.