Miracle-dz/UniFAR-FTUs huggingface dataset:
Tackling the First COVID-19 Wave at the Cape Town Hospital of Hope: Why Was It Such a Positive Experience for Staff?

Background In contrast to alarming reports of exhaustion and burnout amongst healthcare workers in the first wave of the COVID-19 pandemic, we noticed surprisingly positive staff experiences of working in a COVID-19 field hospital in South Africa. The 862-bed Hospital of Hope was established at the Cape Town International Convention Centre specifically to cope with the effects of the first wave of the COVID-19 pandemic in Cape Town. Methods We aimed to systematically describe and assess the effects on staff and the local health system. A cross-sectional descriptive study design was employed using mixed methods including record reviews and interviews with key informants. Results Quantitative results confirmed high job satisfaction and low staff infection rates. The emerging themes from the qualitative data are grouped around a "bulls eye" of the common purpose of person-centredness, from both patient and staff perspectives, and include staff safety and support, rapid communication, continuous learning and adaptability, underpinned by excellent teamwork. The explanations for the positive feedback included good disaster planning, adequate resources, and an extraordinary responsiveness to the need. Conclusions The Hospital of Hope staff experience produced significant learnings for the design and management of routine health services outside of a disaster situation. The adaptability and responsiveness of the facility and its staff was largely a product of the unprecedented nature of the pandemic, but such approaches could benefit routine health services enormously, as individual hospitals and health facilities realize their place in a system that is more than the sum of its parts.
Critiquing-based Modeling of Subjective Preferences

Applications designed for entertainment and other non-instrumental purposes are challenging to optimize because the relationships between system parameters and user experience can be unclear. Ideally, we would crowdsource these design questions, but existing approaches are geared towards evaluation or ranking discrete choices and not for optimizing over continuous parameter spaces. In addition, users are accustomed to informally expressing opinions about experiences as critiques (e.g. it’s too cold, too spicy, too big), rather than giving precise feedback as an optimization algorithm would require. Unfortunately, it can be difficult to analyze qualitative feedback, especially in the context of quantitative modeling. In this article, we present collective criticism, a critiquing-based approach for modeling relationships between system parameters and subjective preferences. We transform critiques, such as “it was too easy/too challenging”, into censored intervals and analyze them using interval regression. Collective criticism has several advantages over other approaches: “too much/too little”-style feedback is intuitive for users and allows us to build predictive models for the optimal parameterization of the variables being critiqued. We present two studies where we model: These studies demonstrate the flexibility of our approach, and show that it produces robust results that are straightforward to interpret and inline with users’ stated preferences.
Variational Neural Temporal Point Process

A temporal point process is a stochastic process that predicts which type of events is likely to happen and when the event will occur given a history of a sequence of events. There are various examples of occurrence dynamics in the daily life, and it is important to train the temporal dynamics and solve two different prediction problems, time and type predictions. Especially, deep neural network based models have outperformed the statistical models, such as Hawkes processes and Poisson processes. However, many existing approaches overfit to specific events, instead of learning and predicting various event types. Therefore, such approaches could not cope with the modified relationships between events and fail to predict the intensity functions of temporal point processes very well. In this paper, to solve these problems, we propose a variational neural temporal point process (VNTPP). We introduce the inference and the generative networks, and train a distribution of latent variable to deal with stochastic property on deep neural network. The intensity functions are computed using the distribution of latent variable so that we can predict event types and the arrival times of the events more accurately. We empirically demonstrate that our model can generalize the representations of various event types. Moreover, we show quantitatively and qualitatively that our model outperforms other deep neural network based models and statistical processes on synthetic and real-world datasets.
Moment Matching Deep Contrastive Latent Variable Models

In the contrastive analysis (CA) setting, machine learning practitioners are specifically interested in discovering patterns that are enriched in a target dataset as compared to a background dataset generated from sources of variation irrelevant to the task at hand. For example, a biomedical data analyst may seek to understand variations in genomic data only present among patients with a given disease as opposed to those also present in healthy control subjects. Such scenarios have motivated the development of contrastive latent variable models to isolate variations unique to these target datasets from those shared across the target and background datasets, with current state of the art models based on the variational autoencoder (VAE) framework. However, previously proposed models do not explicitly enforce the constraints on latent variables underlying CA, potentially leading to the undesirable leakage of information between the two sets of latent variables. Here we propose the moment matching contrastive VAE (MM-cVAE), a reformulation of the VAE for CA that uses the maximum mean discrepancy to explicitly enforce two crucial latent variable constraints underlying CA. On three challenging CA tasks we find that our method outperforms the previous state-of-the-art both qualitatively and on a set of quantitative metrics.
Connecting Optimization and Generalization via Gradient Flow Path Length

Optimization and generalization are two essential aspects of machine learning. In this paper, we propose a framework to connect optimization with generalization by analyzing the generalization error based on the length of optimization trajectory under the gradient ﬂow algorithm after convergence. Through our approach, we show that, with a proper initialization, gradient ﬂow converges following a short path with an explicit length estimate. Such an estimate induces a length-based generalization bound, showing that short optimization paths after convergence are associated with good generalization, which also matches our numerical results. Our framework can be applied to broad settings. For example, we use it to obtain generalization estimates on three distinct machine learning models: underdeter-mined (cid:96) p linear regression, kernel regression, and overparameterized two-layer ReLU neural networks.
SAPIENTML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions

Automatic machine learning, or AutoML, holds the promise of truly democratizing the use of machine learning (ML), by substantially automating the work of data scientists. However, the huge combinatorial search space of candidate pipelines means that current AutoML techniques, generate sub-optimal pipelines, or none at all, especially on large, complex datasets. In this work we propose an AutoML technique SapientML, that can learn from a corpus of existing datasets and their human-written pipelines, and efficiently generate a high-quality pipeline for a predictive task on a new dataset. To combat the search space explosion of AutoML, SapientML employs a novel divide-and-conquer strategy realized as a three-stage program synthesis approach, that reasons on successively smaller search spaces. The first stage uses meta-learning to predict a set of plausible ML components to constitute a pipeline. In the second stage, this is then refined into a small pool of viable concrete pipelines using a pipeline dataflow model derived from the corpus. Dynamically evaluating these few pipelines, in the third stage, provides the best solution. We instantiate SapientML as part of a fully automated tool-chain that creates a cleaned, labeled learning corpus by mining Kaggle, learns from it, and uses the learned models to then synthesize pipelines for new predictive tasks. We have created a training corpus of 1,094 pipelines spanning 170 datasets, and evaluated SapientML on a set of 41 benchmark datasets, including 10 new, large, real-world datasets from Kaggle, and against 3 state-of-the-art AutoML tools and 4 baselines. Our evaluation shows that SapientML produces the best or comparable accuracy on 27 of the benchmarks while the second best tool fails to even produce a pipeline on 9 of the instances. This difference is amplified on the 10 most challenging benchmarks, where SapientML wins on 9 instances with the other tools failing to produce pipelines on 4 or more benchmarks.
Stateful Structural Operational Semantics

Compositionality of denotational semantics is an important concern in programming semantics. Mathematical operational semantics in the sense of Turi and Plotkin guarantees compositionality, but seen from the point of view of stateful computation it applies only to very fine-grained equivalences that essentially assume unrestricted interference by the environment between any two statements. We introduce the more restrictive stateful SOS rule format for stateful languages. We show that compositionality of two more coarse-grained semantics, respectively given by assuming read-only interference or no interference between steps, remains an undecidable property even for stateful SOS. However, further restricting the rule format in a manner inspired by the cool GSOS formats of Bloom and van Glabbeek, we obtain the streamlined and cool stateful SOS formats, which respectively guarantee compositionality of the two more abstract equivalences.
Gate-Tunable Transmon Using Selective-Area-Grown Superconductor-Semiconductor Hybrid Structures on Silicon

We present a gate-voltage tunable transmon qubit (gatemon) based on planar InAs nanowires that are selectively grown on a high resistivity silicon substrate using III-V buffer layers. We show that low loss superconducting resonators with an internal quality of $2\times 10^5$ can readily be realized using these substrates after the removal of buffer layers. We demonstrate coherent control and readout of a gatemon device with a relaxation time, $T_{1}\approx 700\,\mathrm{ns}$, and dephasing times, $T_2^{\ast}\approx 20\,\mathrm{ns}$ and $T_{\mathrm{2,echo}} \approx 1.3\,\mathrm{\mu s}$. Further, we infer a high junction transparency of $0.4 - 0.9$ from an analysis of the qubit anharmonicity.
A Review of Affective Generation Models

Affective computing is an emerging interdisciplinary field where computational systems are developed to analyze, recognize, and influence the affective states of a human. It can generally be divided into two subproblems: affective recognition and affective generation. Affective recognition has been extensively reviewed multiple times in the past decade. Affective generation, however, lacks a critical review. Therefore, we propose to provide a comprehensive review of affective generation models, as models are most commonly leveraged to affect others' emotional states. Affective computing has gained momentum in various fields and applications, thanks to the leap of machine learning, especially deep learning since 2015. With critical models introduced, this work is believed to benefit future research on affective generation. We conclude this work with a brief discussion on existing challenges.
Spanish and English Phoneme Recognition by Training on Simulated Classroom Audio Recordings of Collaborative Learning Environments

Audio recordings of collaborative learning environments contain a constant presence of cross-talk and background noise. Dynamic speech recognition between Spanish and English is required in these environments. To eliminate the standard requirement of large-scale ground truth, the thesis develops a simulated dataset by transforming audio transcriptions into phonemes and using 3D speaker geometry and data augmentation to generate an acoustic simulation of Spanish and English speech. The thesis develops a low-complexity neural network for recognizing Spanish and English phonemes (available at github.com/muelitas/keywordRec). When trained on 41 English phonemes, 0.099 PER is achieved on Speech Commands. When trained on 36 Spanish phonemes and tested on real recordings of collaborative learning environments, a 0.7208 LER is achieved. Slightly better than Google’s Speech-to-text 0.7272 LER, which used anywhere from 15 to 1,635 times more parameters and trained on 300 to 27,500 hours of real data as opposed to 13 hours of simulated audios.
Chandra Follow-up Observations of Swift-BAT-selected AGNs II

We present the combined Chandra and Swift-BAT spectral analysis of nine low-redshift (z ≤ 0.10), candidate heavily obscured active galactic nuclei (AGN) selected from the Swift-BAT 150 month catalog. We located soft (1−10 keV) X-ray counterparts to these BAT sources and joint fit their spectra with physically motivated models. The spectral analysis in the 1−150 keV energy band determined that all sources are obscured, with a line-of-sight column density N H ≥ 1022 cm−2 at a 90% confidence level. Four of these sources show significant obscuration with N H ≥ 1023 cm−2 and two additional sources are candidate Compton-thick Active Galactic Nuclei (CT-AGNs) with N H ≥ 1024 cm−2. These two sources, 2MASX J02051994–0233055 and IRAS 11058−1131, are the latest addition to the previous 3 CT-AGN candidates found using our strategy for soft X-ray follow-up of BAT sources. Here we present the results of our methodology so far, and analyze the effectiveness of applying different selection criteria to discover CT-AGN in the local Universe. Our selection criteria has an ∼20% success rate of discovering heavily obscured AGN whose CT nature is confirmed by follow-up NuSTAR observations. This is much higher than the ∼5% found in blind surveys.
The Transition Region between Brightest Cluster Galaxies and Intracluster Light in Galaxy Groups and Clusters

We take advantage of a state-of-art semianalytic model of galaxy formation, and the model presented by Contini & Gu, to investigate the mass distribution of brightest cluster galaxies (BCGs) and intracluster light (ICL) by addressing two points: (1) the region of transition between a BCG-dominated distribution and an ICL-dominated one, and (2) the relation between the total BCG+ICL mass and the ICL mass alone. We find the transition radius to be independent of both BCG+ICL and halo masses, with an average of 60 ± 40 kpc, in good agreement with previous observational measurements, but given the large scatter it can be considered as a sort of physical separation between the two components only on a cluster scale. From the analysis of the M ICL–M BCG+ICL relation, we construct a method able to extract the ICL mass directly from knowledge of the BCG+ICL mass. Given the large scatter in low-mass systems, such a method under/overpredicts the true value of the ICL in a significant way, up to a factor of three in the worst cases. On the other hand, for logMBCG+ICL>12 or logMHalo>14 (where masses are in units of M ⊙), the difference between the true value and the one extracted from the M ICL–M BCG+ICL relation ranges between ±30%. We therefore suggest this relation as a reliable test for observational works aiming to isolate the ICL from the BCG, for systems hosted by haloes on a cluster scale.
A Parkes “Murriyang” Search for Pulsars and Fast Transients in the Large Magellanic Cloud

We report on the latest results of a Parkes multibeam survey for pulsars and dispersed radio bursts in the Large Magellanic Cloud (LMC). We conducted both periodicity and single-pulse searches at a much larger range of trial dispersion measures (DMs) than previously searched. We detected 229 single pulses with signal-to-noise ratio (S/N) > 7 that were classified by the deep-learning network FETCH as being real (with >90% likelihood), of which nine were from the known giant-pulse-emitting pulsar PSR B0540−69. Two possibly repeating sources were detected with DMs suggesting that they lie within the LMC, but these require confirmation. Only three of the 220 unknown pulses had S/N > 8, and the DM distribution for these detected pulses follows an exponential falloff with increasing DM and does not show any excess of signals at DM values expected for the LMC. These features suggest that the detected pulses are not likely to be real, although they are visually compelling. We also report the discovery of a new pulsar (PSR J0556−67) in our periodicity search. This pulsar has a spin period of 791 ms, a DM of 71 cm−3 pc, an estimated 1400 MHz flux density of ∼0.12 mJy, and shows no evidence of binary motion. Future observations may be able to confirm whether any of the weak but promising candidates in our single-pulse and periodicity searches, including our two possible repeaters, are real or not.
Nature versus Nurture: Investigating the Effects of Measurement Uncertainties in the Assessment of Potential Trends between Planetary and Stellar Properties

Correlations between planetary and stellar properties, particularly age, can provide insight on planetary formation and evolution processes. However, the underlying source of such trends can be unclear, and measurement uncertainties and small sample sizes can leave doubt as to whether an observed trend truly exists. We use a Bayesian framework to examine how uncertainties in measured parameters influence the odds ratios of competing hypotheses for the source of an observed trend. We analyze three reported trends from the literature. In each application, while uncertainties do affect the numerical value of the odds ratios, our conclusions remain the same whether or not uncertainties are taken into account: hot Jupiter eccentricities are circularized over time, obliquities of hot Jupiter hosts are driven by stellar temperature, and there is not enough evidence to favor a trend of 2:1 orbital resonances with age over a chance relation. Updated samples for the 2:1 resonance and obliquity cases do not change the original conclusions. Simulated 2:1 resonance data show that sample size may be more important than measurement precision for drawing a firm conclusion. However, if 2:1 resonances get disrupted on a wide range of timescales, an age trend will be inherently difficult to confirm over a chance relation, even with a large sample. For some applications, full incorporation of measurement uncertainties may be too computationally expensive, making it preferable to use the framework without uncertainties and perform additional tests to examine the effects of highly uncertain measurements.
Comparing the latest ranking techniques: pros and cons of flexible skylines, regret minimization and skyline ranking queries

Long-established ranking approaches, such as top-k and skyline queries, have been thoroughly discussed and their drawbacks are well acknowledged. New techniques have been developed in recent years that try to combine traditional ones to overcome their limitations. In this paper we focus our attention on some of them: flexible skylines, regret minimization and skyline ranking queries, because, while these new methods are promising and have shown interesting results, a comparison between them is still not available. After a short introduction of each approach, we discuss analogies and differences between them with the advantages and disadvantages of every technique debated.
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Existing prior works have attempted to model video distribution by representing videos as 3D grids of RGB values, which impedes the scale of generated videos and neglects continuous dynamics. In this paper, we found that the recent emerging paradigm of implicit neural representations (INRs) that encodes a continuous signal into a parameterized neural network effectively mitigates the issue. By utilizing INRs of video, we propose dynamics-aware implicit generative adversarial network (DIGAN), a novel generative adversarial network for video generation. Specifically, we introduce (a) an INR-based video generator that improves the motion dynamics by manipulating the space and time coordinates differently and (b) a motion discriminator that efficiently identifies the unnatural motions without observing the entire long frame sequences. We demonstrate the superiority of DIGAN under various datasets, along with multiple intriguing properties, e.g., long video synthesis, video extrapolation, and non-autoregressive video generation. For example, DIGAN improves the previous state-of-the-art FVD score on UCF-101 by 30.7% and can be trained on 128 frame videos of 128x128 resolution, 80 frames longer than the 48 frames of the previous state-of-the-art method.
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation

Network pruning and knowledge distillation are two widely-known model compression methods that efficiently reduce computation cost and model size. A common problem in both pruning and distillation is to determine compressed architecture, i.e., the exact number of filters per layer and layer configuration, in order to preserve most of the original model capacity. In spite of the great advances in existing works, the determination of an excellent architecture still requires human interference or tremendous experimentations. In this paper, we propose an architecture slimming method that automates the layer configuration process. We start from the perspective that the capacity of the over-parameterized model can be largely preserved by finding the minimum number of filters preserving the maximum parameter variance per layer, resulting in a thin architecture. We formulate the determination of compressed architecture as a one-step orthogonal linear transformation, and integrate principle component analysis (PCA), where the variances of filters in the first several projections are maximized. We demonstrate the rationality of our analysis and the effectiveness of the proposed method through extensive experiments. In particular, we show that under the same overall compression rate, the compressed architecture determined by our method shows significant performance gain over baselines after pruning and distillation. Surprisingly, we find that the resulting layer-wise compression rates correspond to the layer sensitivities found by existing works through tremendous experimentations.
Decomposing Random Permutations into Order-Isomorphic Subpermutations

Two permutations $s$ and $t$ are $k$-similar if they can be decomposed into subpermutations $s^1, \ldots, s^k$ and $t^1, \ldots, t^k$ such that $s^i$ is order-isomorphic to $t^i$ for all $i$. Recently, Dudek, Grytczuk and Ruci\'nski posed the problem of determining the minimum $k$ for which two permutations chosen independently and uniformly at random are $k$-similar. We show that two such permutations are $O(n^{1/3}\log^{11/6}(n))$-similar with high probability, which is tight up to a polylogarithmic factor. Our result also generalises to simultaneous decompositions of multiple permutations.