Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic Scene Classification With Squeeze-Excitation Residual Networks

Acoustic scene classification (ASC) is a problem related to the field of machine listening whose objective is to classify/tag an audio clip in a predefined label describing a scene location (e. g. park, airport, etc.). Many state-of-the-art solutions to ASC incorporate data augmentation techniques and model ensembles. However, considerable improvements can also be achieved only by modifying the architecture of convolutional neural networks (CNNs). In this work we propose two novel squeeze-excitation blocks to improve the accuracy of a CNN-based ASC framework based on residual learning. The main idea of squeeze-excitation blocks is to learn spatial and channel-wise feature maps independently instead of jointly as standard CNNs do. This is usually achieved by combining some global grouping operators, linear operators and a final calibration between the input of the block and its learned relationships. The behavior of the block that implements such operators and, therefore, the entire neural network, can be modified depending on the input to the block, the established residual configurations and the selected non-linear activations. The analysis has been carried out using the TAU Urban Acoustic Scenes 2019 dataset presented in the 2019 edition of the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge. All configurations discussed in this document exceed the performance of the baseline proposed by the DCASE organization by 13% percentage points. In turn, the novel configurations proposed in this paper outperform the residual configurations proposed in previous works.
IPOD: An Industrial and Professional Occupations Dataset and its Applications to Occupational Data Mining and Analysis

Occupational data mining and analysis is an important task in understanding today's industry and job market. Various machine learning techniques are proposed and gradually deployed to improve companies' operations for upstream tasks, such as employee churn prediction, career trajectory modelling and automated interview. Job titles analysis and embedding, as the fundamental building blocks, are crucial upstream tasks to address these occupational data mining and analysis problems. In this work, we present the Industrial and Professional Occupations Dataset (IPOD), which consists of over 190,000 job titles crawled from over 56,000 profiles from Linkedin. We also illustrate the usefulness of IPOD by addressing two challenging upstream tasks, including: (i) proposing Title2vec, a contextual job title vector representation using a bidirectional Language Model (biLM) approach; and (ii) addressing the important occupational Named Entity Recognition problem using Conditional Random Fields (CRF) and bidirectional Long Short-Term Memory with CRF (LSTM-CRF). Both CRF and LSTM-CRF outperform human and baselines in both exact-match accuracy and F1 scores. The dataset and pre-trained embeddings are available at https://www.github. com/junhua/ipod.
Nature of protected zero-energy states in Penrose quasicrystals

The electronic spectrum of the Penrose rhombus quasicrystal exhibits a macroscopic fraction of exactly degenerate zero energy states. In contrast to other bipartite quasicrystals, such as the kite-and-dart one, these zero energy states cannot be attributed to a global mismatch $\Delta n$ between the number of sites in the two sublattices that form the quasicrystal. Here, we argue that these zero energy states are instead related to a local mismatch $\Delta n(\bf r)$. Although $\Delta n(\bf r)$ averages to zero, its staggered average over self-organized domains gives the correct number of zero energy states. Physically, the local mismatch is related to a hidden structure of nested self-similar domains that support the zero energy states. This allows us to develop a real space renormalization-group scheme, which yields the scaling law for the fraction of zero energy states, $Z$, versus size of their support domain, $N$, as $Z\propto N^{-\eta}$ with $\eta =1-\ln 2/\ln(1+\tau) \approx 0.2798$ (where $\tau$ is the golden ratio). It also reproduces the known total fraction of the zero energy states, $81-50\tau\approx 0.0983$. We also show that the exact degeneracy of these states is protected against a wide variety of local perturbations, such as irregular or random hopping amplitudes, magnetic field, random dilution of the lattice, etc. We attribute this robustness to the hidden domain structure and speculate about its underlying topological origin.
On the Limits to Multi-modal Popularity Prediction on Instagram: A New Robust, Efficient and Explainable Baseline

: Our global population contributes visual content on platforms like Instagram, attempting to express themselves and engage their audiences, at an unprecedented and increasing rate. In this paper, we revisit the popularity prediction on Instagram. We present a robust, efﬁcient, and explainable baseline for population-based popularity prediction, achieving strong ranking performance. We employ the latest methods in computer vision to maximise the information extracted from the visual modality. We use transfer learning to extract visual semantics such as concepts, scenes, and objects, allowing a new level of scrutiny in an extensive, explainable ablation study. We inform feature selection towards a robust and scalable model, but also illustrate feature interactions, offering new directions for further inquiry in computational social science. Our strongest models inform a lower limit to population-based predictability of popularity on Instagram. The models are immediately applicable to social media monitoring and inﬂuencer identiﬁcation.
Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media

Social media is a popular platform for information sharing. Any piece of information can be spread rapidly across the globe at lightning speed. The biggest challenge for social media platforms like Twitter is how to trust news shared on them when there is no systematic news verification process, which is the case for traditional media. Detecting false information, for example, detection of rumors is a non-trivial task, given the fast-paced social media environment. In this work, we proposed an ensemble model, which performs majority-voting scheme on a collection of predictions of neural networks using time-series vector representation of Twitter data for fast detection of rumors. Experimental results show that proposed neural network models outperformed classical machine learning models in terms of micro F1 score. When compared to our previous works the improvements are 12.5% and 7.9%, respectively.
Multi-IF : An Approach to Anomaly Detection in Self-Driving Systems

Autonomous driving vehicles (ADVs) are implemented with rich software functions and equipped with many sensors, which in turn brings broad attack surface. Moreover, the execution environment of ADVs is often open and complex. Hence, ADVs are always at risk of safety and security threats. This paper proposes a fast method called Multi-IF, using multiple invocation features of system calls to detect anomalies in self-driving systems. Since self-driving functions take most of the computation resources and upgrade frequently, Multi-IF is designed to work under such resource constraints and support frequent updates. Given the collected sequences of system calls, the combination of different syntax patterns is used to analyze and construct feature vectors of those sequences. By taking the feature vectors as inputs, one-class support vector machine is adopted to determine whether the current sequence of system calls is abnormal, which is trained with the feature vectors from the normal sequences. The evaluations on both simulated and real data prove that the proposed method is effective in identifying the abnormal behavior after minutes of feature extraction and training. Further comparisons with the existing methods on the ADFA-LD data set also validate that the proposed approach achieves a higher accuracy with less time overhead.
One-Shot Identity-Preserving Portrait Reenactment

We present a deep learning-based framework for portrait reenactment from a single picture of a target (one-shot) and a video of a driving subject. Existing facial reenactment methods suffer from identity mismatch and produce inconsistent identities when a target and a driving subject are different (cross-subject), especially in one-shot settings. In this work, we aim to address identity preservation in cross-subject portrait reenactment from a single picture. We introduce a novel technique that can disentangle identity from expressions and poses, allowing identity preserving portrait reenactment even when the driver's identity is very different from that of the target. This is achieved by a novel landmark disentanglement network (LD-Net), which predicts personalized facial landmarks that combine the identity of the target with expressions and poses from a different subject. To handle portrait reenactment from unseen subjects, we also introduce a feature dictionary-based generative adversarial network (FD-GAN), which locally translates 2D landmarks into a personalized portrait, enabling one-shot portrait reenactment under large pose and expression variations. We validate the effectiveness of our identity disentangling capabilities via an extensive ablation study, and our method produces consistent identities for cross-subject portrait reenactment. Our comprehensive experiments show that our method significantly outperforms the state-of-the-art single-image facial reenactment methods. We will release our code and models for academic use.
WISEA J083011.95+283716.0: A Missing Link Planetary-mass Object

We present the discovery of WISEA J083011.95+283716.0, the first Y-dwarf candidate identified through the “Backyard Worlds: Planet 9” citizen science project. We identified this object as a red, fast-moving source with a faint W2 detection in multiepoch AllWISE and unWISE images. We have characterized this object with Spitzer and Hubble Space Telescope’s (HST) follow-up imaging. With mid-infrared detections in Spitzer's ch1 and ch2 bands and flux upper limits in HST F105W and F125W filters, we find that this object is both very faint and has extremely red colors (ch1 − ch2 = 3.25 ± 0.23 mag, F125W − ch2 ≥ 9.36 mag), consistent with a Teff ∼ 300 K source, as estimated from the known Y-dwarf population. A preliminary parallax provides a distance of pc, leading to a slightly warmer temperature of ∼350 K. The extreme faintness and red HST and Spitzer colors of this object suggest that it may be a link between the broader Y-dwarf population and the coldest known brown dwarf WISE J0855−0714, and may highlight our limited knowledge of the true spread of Y-dwarf colors. We also present four additional “Backyard Worlds: Planet 9” late-T brown dwarf discoveries within 30 pc.
Differentiable Adaptive Computation Time for Visual Reasoning

This paper presents a novel attention-based algorithm for achieving adaptive computation called DACT, which, unlike existing ones, is end-to-end differentiable. Our method can be used in conjunction with many networks; in particular, we study its application to the widely know MAC architecture, obtaining a significant reduction in the number of recurrent steps needed to achieve similar accuracies, therefore improving its performance to computation ratio. Furthermore, we show that by increasing the maximum number of steps used, we surpass the accuracy of even our best non-adaptive MAC in the CLEVR dataset, demonstrating that our approach is able to control the number of steps without significant loss of performance. Additional advantages provided by our approach include considerably improving interpretability by discarding useless steps and providing more insights into the underlying reasoning process. Finally, we present adaptive computation as an equivalent to an ensemble of models, similar to a mixture of expert formulation. Both the code and the configuration files for our experiments are made available to support further research in this area.
Clustering by Constructing Hyper-Planes

As a ubiquitous method in the field of machine learning, clustering algorithm attracts a lot attention. Because only some basic information can be utilized, clustering data points into correct categories is a critical task especially when the cluster number is unknown. This paper presents an algorithm which can find the cluster number automatically. It firstly constructs hyper-planes based on the marginal of sample points. Then an adjacent relationship between data points is defined. Based on it, connective components are derived. According to a validity index proposed in this paper, the high-qualified connective components are selected as cluster centers. Meanwhile, the clusters’ number is also determined. Another contribution of this paper is that all the parameters in this algorithm can be set automatically. To evaluate its robustness, experiments on different kinds of benchmark datasets are carried out. They show that the performances are even better than some other methods’ best results which are selected manually.
Is SARS-CoV-2 Also an Enteric Pathogen With Potential Fecal–Oral Transmission? A COVID-19 Virological and Clinical Review

Abstract In as short as 3 months, COVID-19 has spread and ravaged the world in an unprecedented speed in modern history rivaling the 1918 flu pandemic. SARS-CoV-2, the culprit virus, is highly contagious and stable in the environment and predominantly transmits among humans via the respiratory route. Accumulating evidence suggest that this virus, like many of its related viruses, may also be an enteric virus that can spread via the fecal-oral route. Such a hypothesis would also contribute to the rapidity and proliferation of this pandemic. Here we briefly summarize what is known about this family of viruses and literature basis of the hypothesis that SARS-CoV-2 is capable of infecting the gastrointestinal tract and shedding in the environment for potential human-to-human transmission.
Comments on Economic Integration and Network Trade: A Comparison of East Asia  and the European Union

Prema-chandra Athukorala, Australian National University: This paper undertakes a comparative analysis of production networks in Europe and East Asia in the global context, with emphasis on determinants of export patterns and intra-regional linkages. The findings suggest that: exports of intermediate goods (used as a proxy for parts and components or “middle products” exchanged within production networks) are more sensitive to trade barriers than total goods exports; regionalization of production networks is primarily determined by greater sensitivity of intermediate goods trade cost associated with distance that overwhelms cost advantages arising from advances in transportation and communication technology; and the production network in these two regions are being guided by distinct factors with geographic distance and remoteness playing a more significant role in East Asia than in the EU. Overall, this is an interesting paper on an important subject. However, I would like to suggest that the authors pay attention to the following methodological imitations in their further research.
ROS

The combination of the explosive growth in digital data and the demand to preserve much of these data in the long term has made it imperative to find a more cost-effective way than HDD arrays and a more easily accessible way than tape libraries to store massive amounts of data. While modern optical discs are capable of guaranteeing more than 50-year data preservation without media replacement, individual optical discs’ lack of the performance and capacity relative to HDDs or tapes has significantly limited their use in datacenters. This article presents a Rack-scale Optical disc library System, or ROS in short, which provides a PB-level total capacity and inline accessibility on thousands of optical discs built within a 42U Rack. A rotatable roller and robotic arm separating and fetching discs are designed to improve disc placement density and simplify the mechanical structure. A hierarchical storage system based on SSDs, hard disks, and optical discs is proposed to effectively hide the delay of mechanical operation. However, an optical library file system (OLFS) based on FUSE is proposed to schedule mechanical operation and organize data on the tiered storage with a POSIX user interface to provide an illusion of inline data accessibility. We further optimize OLFS by reducing unnecessary user/kernel context switches inheriting from legacy FUSE framework. We evaluate ROS on a few key performance metrics, including operation delays of the mechanical structure and software overhead in a prototype PB-level ROS system. The results show that ROS stacked on Samba and FUSE as network-attached storage (NAS) mode almost saturates the throughput provided by underlying samba via 10GbE network for external users, as well as in this scenario provides about 53ms file write and 15ms read latency, exhibiting its inline accessibility. Besides, ROS is able to effectively hide and virtualize internal complex operational behaviors and be easily deployable in datacenters.
Genetic Analysis of Complex Traits Using SAS

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The Effects of Workplace Nature-Based Interventions on the Mental Health and Well-Being of Employees: A Systematic Review

Mental health in the workplace is a societal challenge with serious economical and human costs. Most prevalent mental disorders in the workforce (e.g., depression), however, are preventable. There is widespread agreement about the favorable effects of nature exposure and consequently, nature-based interventions (NBI) in the workplace have been proposed as a cost-effective approach to promote good health among employees. The objective of the present study was to systematically review scientific evidence on the effectiveness of NBI to promote mental health and well-being among actual employees in actual workplace settings. The review was conducted and presented in accordance with the PRISMA guidelines. The literature search was performed on five databases (PubMed, Embase, CENTRAL, CINHAL, and PsycINFO), hand-searching of field-specific journals, and the reference lists of retrieved papers over the past 5 years up to November (13th, 2018). Studies were eligible for inclusion if they (i) were randomized or nonrandomized controlled trials; (ii) comprised samples of actual employees; (iii) implemented a workplace-based intervention with exposure to nature; (iv) included comparison conditions that displayed a clear contrast to NBIs; and (v) investigated the quantitative effects on mental health or well-being. No restrictions on type of employees or workplace, publication period, or language of the publication were set. Risk of bias was assessed using the Cochrane’s RoB2 tool. Narrative synthesis was performed due to large heterogeneity in outcome variables. Of the 510 articles identified, 10 NBIs (nine papers) met the eligibility criteria. The outcomes were grouped in five categories: (i) mental health indices, (ii) cognitive ability, (iii) recovery and restoration, (iv) work and life satisfaction, and (v) psychophysiological indicators. Narrative synthesis indicates consistently positive effects on mental health indices and cognitive ability, while mixed results were found for the other outcome categories. Caution must be given when interpreting the current evidence in this emerging research field because of the diversity of NBIs and the overall high risk of bias in the individual studies. Although in this field often researchers have to balance scientific rigor and ecological validity, there is a need for large, well-designed and rigorously conducted trials grounded in contemporary theories.
Women Quotas vs. Men Quotas in Academia: Students Perceive Favoring Women as Less Fair Than Favoring Men

In this study, we analyze the free verbal associations to the stimuli women quotas and men quotas of 327 medical students. Women and men quotas are characterized by the same modus operandi (i.e., preferential treatment based on sex/gender). However, women quotas help a low-status group, whereas men quotas help a high-status group. In line with a support paradox, that is, the perception that support for women is less fair and less legitimate than support for men, we expected that students would reject women quotas in academia more vehemently than men quotas. Specifically, we hypothesized that students would have more negative and more emotional associations with women quotas than men quotas. As predicted, students had more negative associations with women quotas than with men quotas. However, students did not have more emotional associations with women quotas than with men quotas. In addition, we explored the semantic content of the free associations to identify specific concerns over each quota. Students perceived women quotas as counterproductive, derogatory, and unfair, whereas they perceived men quotas as beneficial and fair. Concerns over the negative perceptions of quota beneficiaries were associated more frequently with women quotas than men quotas. Potential factors underlying students’ perceptions of both quotas are discussed.