Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning

Few-shot learning aims to learn classifiers for new classes with only a few training examples per class. Most existing few-shot learning approaches belong to either metric-based meta-learning or optimization-based meta-learning category, both of which have achieved successes in the simplified "$k$-shot $N$-way" image classification settings. Specifically, the optimization-based approaches train a meta-learner to predict the parameters of the task-specific classifiers. The task-specific classifiers are required to be homogeneous-structured to ease the parameter prediction, so the meta-learning approaches could only handle few-shot learning problems where the tasks share a uniform number of classes. The metric-based approaches learn one task-invariant metric for all the tasks. Even though the metric-learning approaches allow different numbers of classes, they require the tasks all coming from a similar domain such that there exists a uniform metric that could work across tasks. In this work, we propose a hybrid meta-learning model called Meta-Metric-Learner which combines the merits of both optimization- and metric-based approaches. Our meta-metric-learning approach consists of two components, a task-specific metric-based learner as a base model, and a meta-learner that learns and specifies the base model. Thus our model is able to handle flexible numbers of classes as well as generate more generalized metrics for classification across tasks. We test our approach in the standard "$k$-shot $N$-way" few-shot learning setting following previous works and a new realistic few-shot setting with flexible class numbers in both single-source form and multi-source forms. Experiments show that our approach can obtain superior performance in all settings.
Cronies, procrastinators, and leaders: A conservation of resources perspective on employees’ responses to organizational cronyism

ABSTRACT This article draws from conservation of resources theory to detail a critical mediating role of organizational disidentification in the connection between employees’ perceptions of organizational cronyism and their procrastination, as well as a buffering role of leader–member exchange in this process. Three-wave, time-lagged data collected from employees across multiple industry sectors indicate that an important reason that beliefs about favouritism-based organizational practices escalate into enhanced procrastination behaviour is that employees feel embarrassed about their organizational membership. Yet this mediating role of organizational disidentification is less prominent to the extent that employees can draw from high-quality relationships with their immediate supervisor. For organizations, this study reveals a key mechanism – employees’ psychological distancing from their employing organization – by which dysfunctional organizational favouritism can escalate into a tendency to slack off in their job tasks, and it pinpoints how this risk can be contained by high-quality supervisor exchanges.
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging

Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon target-specific patterns. In this paper, we propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining the valuable source knowledge. Our experiments on POS tagging of social media texts (Tweets domain) demonstrate that our method achieves state-of-the-art performances on 3 commonly used datasets.
Combining Offline Models and Online Monte-Carlo Tree Search for Planning from Scratch

Planning in stochastic and partially observable environments is a central issue in artificial intelligence. One commonly used technique for solving such a problem is by constructing an accurate model firstly. Although some recent approaches have been proposed for learning optimal behaviour under model uncertainty, prior knowledge about the environment is still needed to guarantee the performance of the proposed algorithms. With the benefits of the Predictive State Representations~(PSRs) approach for state representation and model prediction, in this paper, we introduce an approach for planning from scratch, where an offline PSR model is firstly learned and then combined with online Monte-Carlo tree search for planning with model uncertainty. By comparing with the state-of-the-art approach of planning with model uncertainty, we demonstrated the effectiveness of the proposed approaches along with the proof of their convergence. The effectiveness and scalability of our proposed approach are also tested on the RockSample problem, which are infeasible for the state-of-the-art BA-POMDP based approaches.
75 Languages, 1 Model: Parsing Universal Dependencies Universally

We present UDify, a multilingual multi-task model capable of accurately predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages. By leveraging a multilingual BERT self-attention model pretrained on 104 languages, we found that fine-tuning it on all datasets concatenated together with simple softmax classifiers for each UD task can meet or exceed state-of-the-art UPOS, UFeats, Lemmas, (and especially) UAS, and LAS scores, without requiring any recurrent or language-specific components. We evaluate UDify for multilingual learning, showing that low-resource languages benefit the most from cross-linguistic annotations. We also evaluate for zero-shot learning, with results suggesting that multilingual training provides strong UD predictions even for languages that neither UDify nor BERT have ever been trained on.
360 Panorama Synthesis from a Sparse Set of Images with Unknown FOV

360 images represent scenes captured in all possible viewing directions. They enable viewers to navigate freely around the scene and thus provide an immersive experience. Conversely, conventional images represent scenes in a single viewing direction. These images are captured with a small or limited field of view. As a result, only some parts of the scenes are observed, and valuable information about the surroundings is lost. We propose a learning-based approach that reconstructs the scene in 360 x180 from conventional images. This approach first estimates the field of view of input images relative to the panorama. The estimated field of view is then used as the prior for synthesizing a high-resolution 360 panoramic output. Experimental results demonstrate that our approach outperforms alternative method and is robust enough to synthesize real-world data (e.g. scenes captured using smartphones).
EDGE: the mass–metallicity relation as a critical test of galaxy formation physics

We introduce the ‘Engineering Dwarfs at Galaxy Formation’s Edge’ (EDGE) project to study the cosmological formation and evolution of the smallest galaxies in the Universe. In this first paper, we explore the effects of resolution and sub-grid physics on a single low-mass halo ($M_{\rm halo}=10^{9}{\, \rm M}_\odot$), simulated to redshift z = 0 at a mass and spatial resolution of $\sim 20{\, \rm M}_\odot$ and ∼3 pc. We consider different star formation prescriptions, supernova feedback strengths, and on-the-fly radiative transfer (RT). We show that RT changes the mode of galactic self-regulation at this halo mass, suppressing star formation by causing the interstellar and circumgalactic gas to remain predominantly warm (∼104 K) even before cosmic reionization. By contrast, without RT, star formation regulation occurs only through starbursts and their associated vigorous galactic outflows. In spite of this difference, the entire simulation suite (with the exception of models without any feedback) matches observed dwarf galaxy sizes, velocity dispersions, V-band magnitudes, and dynamical mass-to-light-ratios. This is because such structural scaling relations are predominantly set by the host dark matter halo, with the remaining model-to-model variation being smaller than the observational scatter. We find that only the stellar mass–metallicity relation differentiates the galaxy formation models. Explosive feedback ejects more metals from the dwarf, leading to a lower metallicity at a fixed stellar mass. We conclude that the stellar mass–metallicity relation of the very smallest galaxies provides a unique constraint on galaxy formation physics.
Convolutional Self-Attention Networks

Self-attention networks (SANs) have drawn increasing interest due to their high parallelization in computation and flexibility in modeling dependencies. SANs can be further enhanced with multi-head attention by allowing the model to attend to information from different representation subspaces. In this work, we propose novel convolutional self-attention networks, which offer SANs the abilities to 1) strengthen dependencies among neighboring elements, and 2) model the interaction between features extracted by multiple attention heads. Experimental results of machine translation on different language pairs and model settings show that our approach outperforms both the strong Transformer baseline and other existing models on enhancing the locality of SANs. Comparing with prior studies, the proposed model is parameter free in terms of introducing no more parameters.
Paid, Owned and Earned Media: A Qualitative Comparative Analysis revealing Attributes Influencing Consumer's Brand Attitude in Social Media

This paper examines how companies can use paid media (referring to sponsored posts), owned media (company posts) and earned media (influencer post) to create a positive brand attitude. Based on the advertising value model, this paper takes a configurational approach and uses fuzzy set qualitative comparative analysis (fsQCA). The analysis reveals a typology of five types of media, which influence consumers’ brand attitude positively. We contribute to research by providing a typology of paid, owned and earned media, which can guide companies to create a positive brand attitude.
Re-Identification Supervised Texture Generation

The estimation of 3D human body pose and shape from a single image has been extensively studied in recent years. However, the texture generation problem has not been fully discussed. In this paper, we propose an end-to-end learning strategy to generate textures of human bodies under the supervision of person re-identification. We render the synthetic images with textures extracted from the inputs and maximize the similarity between the rendered and input images by using the re-identification network as the perceptual metrics. Experiment results on pedestrian images show that our model can generate the texture from a single image and demonstrate that our textures are of higher quality than those generated by other available methods. Furthermore, we extend the application scope to other categories and explore the possible utilization of our generated textures.
ICPMS based multiplexed bioassay: Principles, approaches and progresses

Abstract Recent developments in multiplex bioassay around the theme of precision diagnostics have gained great focus. Inductively coupled plasma mass spectrometry (ICPMS) is emerging as a powerful platform for multiplex bioassay due to its high sensitivity, wide dynamic range, particularly its reliable multiplex sensing ability. This review discusses the progress being made in the multiplex bioassay applicating ICPMS, mainly emphasizes on varieties of biomarkers and methodological innovations. Also, we summarized limitations and proposed prospects to shed new light on the emergence of more powerful and application-oriented analytical methods.
Can Experience be Trusted? Investigating the Effect of Experience on Decision Biases in Crowdworking Platforms

Companies increasingly involve the crowd for collective decision making and, to aggregate the decisions, they commonly average the scores. By ignoring crowdworkers’ different levels of experience and decision biases, this method may not favor the best outcome. Alternatively, decisions can be weighted in favor of the more experienced judges in the crowd. However, previous research is inconclusive as to whether more experienced individuals are any better at avoiding decision biases. To answer this question, we conduct online crowd-based experiments with a range of treatments, comparing the anchoring effect of individuals with different levels of experience. Results indicate that not only does greater experience not protect crowdworkers from the anchoring effect but it increases their confidence in their decision, compared to less experienced individuals, even if they are wrong. Our findings provide valuable insights for both researchers and practitioners interested in improving the effectiveness of crowdworking decision-making. \\
Search for four-top-quark production in the single-lepton and opposite-sign dilepton final states in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for four-top-quark production, $t\bar{t}t\bar{t}$, is presented. It is based on proton-proton collision data with a center-of-mass energy $\sqrt{s}$ = 13 TeV collected by the ATLAS detector at the Large Hadron Collider during the year 2015 and 2016, corresponding to an integrated luminosity of $36.1$ fb$^{-1}$. Data are analyzed in both the single-lepton and opposite-sign dilepton channels, characterized by the presence of one or two isolated electrons or muons with high-transverse momentum and multiple jets. A data-driven method is used to estimate the dominant background from top-quark pair production in association with jets. No significant excess above the Standard Model expectation is observed. The result is combined with the previous same-sign dilepton and multilepton searches carried out by ATLAS and an observed (expected) upper limit of 5.3 (2.1) times the four-top-quark Standard Model cross section is obtained at 95% confidence level. Additionally, upper limit on anomalous four-top-quark production cross section is set in the context of an effective field theory model.
Definability in the group of infinitesimals of a compact Lie group

We show that for $G$ a simple compact Lie group, the infinitesimal subgroup $G^{00}$ is bi-intepretable with a real closed valued field. We deduce that for $G$ an infinite definably compact group definable in an o-minimal expansion of a field, $G^{00}$ is bi-interpretable with the disjoint union of a (possibly trivial) $\mathbb{Q}$-vector space and finitely many (possibly zero) real closed valued fields. We also describe the isomorphisms between such infinitesimal subgroups, and along the way prove that every {\em definable} field in a real closed convexly valued field $R$ is definably isomorphic to $R$.
Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring

Despite deep end-to-end learning methods have shown their superiority in removing non-uniform motion blur, there still exist major challenges with the current multi-scale and scale-recurrent models: 1) Deconvolution/upsampling operations in the coarse-to-fine scheme result in expensive runtime; 2) Simply increasing the model depth with finer-scale levels cannot improve the quality of deblurring. To tackle the above problems, we present a deep {hierarchical multi-patch network} inspired by Spatial Pyramid Matching to deal with blurry images via a fine-to-coarse hierarchical representation. To deal with the performance saturation w.r. t. depth, we propose a stacked version of our multi-patch model. Our proposed basic multi-patch model achieves the state-of-the-art performance on the GoPro dataset while enjoying a 40$\times$ faster runtime compared to current multi-scale methods. With 30ms to process an image at 1280$\times$720 resolution, it is the first real-time deep motion deblurring model for 720p images at 30fps. For stacked networks, significant improvements (over 1.2dB) are achieved on the GoPro dataset by increasing the network depth. Moreover, by varying the depth of the stacked model, one can adapt the performance and runtime of the same network for different application scenarios.
The Use of Psychoacoustics in Marine Mammal Conservation in the United States: From Science to Management and Policy

Underwater sound generated from human activities has been long recognized to cause adverse effects on marine mammals, ranging from auditory masking to behavioral disturbance to hearing impairment. In certain instances, underwater sound has led to physical injuries and mortalities. Research efforts to assess these impacts began approximately four decades ago with behavioral observations of large whales exposed to seismic surveys and rapidly progressed into the diverse field that today includes studies of behavioral, auditory, and physiological responses of marine mammals exposed to anthropogenic sound. Findings from those studies have informed the manner in which impact assessments have been and currently are conducted by regulatory agencies in the United States. They also have led to additional questions and identified information needed to understand more holistically the impacts of underwater sound, such as population- and species-level effects, long-term, chronic, and cumulative effects, and effects on taxa for which little or no information is known. Despite progress, the regulatory community has been slow to incorporate the best available science in marine mammal management and policy and often has relied on outdated and overly simplified methods in its impact assessments. To implement conservation measures effectively, regulatory agencies must be willing to adapt their regulatory scheme to ensure that the best available scientific information is incorporated accordingly.
Learning Metrics From Teachers: Compact Networks for Image Embedding

Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network distillation has been successfully applied to improve image classification, but has hardly been explored for metric learning. To do so, we propose two new loss functions that model the communication of a deep teacher network to a small student network. We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products and show that embeddings computed using small student networks perform significantly better than those computed using standard networks of similar size. Results on a very compact network (MobileNet-0.25), which can be used on mobile devices, show that the proposed method can greatly improve Recall@1 results from 27.5\% to 44.6\%. Furthermore, we investigate various aspects of distillation for embeddings, including hint and attention layers, semi-supervised learning and cross quality distillation. (Code is available at https://github.com/yulu0724/EmbeddingDistillation).
NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining

In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: “Suggestion Mining from Online Reviews and Forums”. We participated in both subtasks for domain specific and also cross-domain suggestion mining. We proposed a recurrent neural network architecture that employs Bi-LSTM layers and also self-attention mechanism. Our architecture tries to encode words via word representation using ELMo and ensembles multiple models to achieve better results. We highlight importance of pre-processing of user-generated samples and its contribution to overall results. We performed experiments with different setups of our proposed model involving weighting of prediction classes for loss function. Our best model achieved in official test evaluation score of 0.6816 for subtask A and 0.6850 for subtask B. In official results, we achieved 12th and 10th place in subtasks A and B, respectively.
Learning to Learn Relation for Important People Detection in Still Images

Humans can easily recognize the importance of people in social event images, and they always focus on the most important individuals. However, learning to learn the relation between people in an image, and inferring the most important person based on this relation, remains undeveloped. In this work, we propose a deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling and feature learning. In particular, we infer two types of interaction modules: the person-person interaction module that learns the interaction between people and the event-person interaction module that learns to describe how a person is involved in the event occurring in an image. We then estimate the importance relations among people from both interactions and encode the relation feature from the importance relations. In this way, POINT automatically learns several types of relation features in parallel, and we aggregate these relation features and the person's feature to form the importance feature for important people classification. Extensive experimental results show that our method is effective for important people detection and verify the efficacy of learning to learn relations for important people detection.
Opacities for photon splitting and pair creation in neutron star magnetospheres

Over the last four decades, persistent and flaring emission of magnetars observed by various telescopes has provided us with a suite of light curves and spectra in soft and hard X-rays, with no emission yet detected above around 1 MeV. Attenuation of such high-energy photons by magnetic pair creation and photon splitting is expected to be active in the magnetospheres of magnetars, possibly accounting for the paucity of gamma-rays in their signals. This paper explores polarization-dependent opacities for these two QED processes in static vacuum dipole magnetospheres of highly magnetized neutron stars, calculating attenuation lengths and determining escape energies, which are the maximum photon energies for transparency out to infinity. The numerical trajectory integral analysis in flat and curved space–times provides upper bounds of a few MeV or less to the visible energies for magnetars for locales proximate to the stellar surface. Photon splitting opacity alone puts constraints on the possible emission locales in their magnetospheres: regions within field loops of maximum altitudes $\, r_{{\rm max}}\sim 2\! -\! 4\,$ stellar radii are not commensurate with maximum detected energies of around 250 keV. These constraints apply not only to magnetar flares but also to their quiescent hard X-ray tail emission. An exploration of photon splitting attenuation in the context of a resonant inverse Compton scattering model for the hard X-ray tails derives distinctive phase-resolved spectroscopic and polarimetric signatures, of significant interest for future MeV-band missions such as AMEGO and e-ASTROGAM.
Sharp moment-entropy inequalities and capacity bounds for log-concave distributions.

We show that the uniform distribution minimises entropy among all symmetric log-concave distributions with fixed variance. As consequences, we give new capacity bounds for additive noise channels with such noises. A number of related results, including for minimization of R\'enyi entropy of orders less than 1, are also developed.