Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Sub-sampling of Audio Feature Sequences for Automated Audio Captioning

Audio captioning is the task of automatically creating a textual description for the contents of a general audio signal. Typical audio captioning methods rely on deep neural networks (DNNs), where the target of the DNN is to map the input audio sequence to an output sequence of words, i.e. the caption. Though, the length of the textual description is considerably less than the length of the audio signal, for example 10 words versus some thousands of audio feature vectors. This clearly indicates that an output word corresponds to multiple input feature vectors. In this work we present an approach that focuses on explicitly taking advantage of this difference of lengths between sequences, by applying a temporal sub-sampling to the audio input sequence. We employ a sequence-to-sequence method, which uses a fixed-length vector as an output from the encoder, and we apply temporal sub-sampling between the RNNs of the encoder. We evaluate the benefit of our approach by employing the freely available dataset Clotho and we evaluate the impact of different factors of temporal sub-sampling. Our results show an improvement to all considered metrics.
Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects

We investigate the finite sample performance of sample splitting, cross-fitting and averaging for the estimation of the conditional average treatment effect. Recently proposed methods, so-called meta-learners, make use of machine learning to estimate different nuisance functions and hence allow for fewer restrictions on the underlying structure of the data. To limit a potential overfitting bias that may result when using machine learning methods, cross-fitting estimators have been proposed. This includes the splitting of the data in different folds to reduce bias and averaging over folds to restore efficiency. To the best of our knowledge, it is not yet clear how exactly the data should be split and averaged. We employ a Monte Carlo study with different data generation processes and consider twelve different estimators that vary in sample-splitting, cross-fitting and averaging procedures. We investigate the performance of each estimator independently on four different meta-learners: the doubly-robust-learner, R-learner, T-learner and X-learner. We find that the performance of all meta-learners heavily depends on the procedure of splitting and averaging. The best performance in terms of mean squared error (MSE) among the sample split estimators can be achieved when applying cross-fitting plus taking the median over multiple different sample-splitting iterations. Some meta-learners exhibit a high variance when the lasso is included in the ML methods. Excluding the lasso decreases the variance and leads to robust and at least competitive results.
Learning a Domain Classifier Bank for Unsupervised Adaptive Object Detection

In real applications, object detectors based on deep networks still face challenges of the large domain gap between the labeled training data and unlabeled testing data. To reduce the gap, recent techniques are proposed by aligning the image/instance-level features between source and unlabeled target domains. However, these methods suffer from the suboptimal problem mainly because of ignoring the category information of object instances. To tackle this issue, we develop a fine-grained domain alignment approach with a well-designed domain classifier bank that achieves the instance-level alignment respecting to their categories. Specifically, we first employ the mean teacher paradigm to generate pseudo labels for unlabeled samples. Then we implement the class-level domain classifiers and group them together, called domain classifier bank, in which each domain classifier is responsible for aligning features of a specific class. We assemble the bare object detector with the proposed fine-grained domain alignment mechanism as the adaptive detector, and optimize it with a developed crossed adaptive weighting mechanism. Extensive experiments on three popular transferring benchmarks demonstrate the effectiveness of our method and achieve the new remarkable state-of-the-arts.
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

This paper seeks to establish a framework for directing a society of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems. What makes it challenging to use a decentralized approach to collectively optimize a central objective is the difficulty in characterizing the equilibrium strategy profile of non-cooperative games. To overcome this challenge, we design a mechanism for defining the learning environment of each agent for which we know that the optimal solution for the global objective coincides with a Nash equilibrium strategy profile of the agents optimizing their own local objectives. The society functions as an economy of agents that learn the credit assignment process itself by buying and selling to each other the right to operate on the environment state. We derive a class of decentralized reinforcement learning algorithms that are broadly applicable not only to standard reinforcement learning but also for selecting options in semi-MDPs and dynamically composing computation graphs. Lastly, we demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
A framework for constructing a huge name disambiguation dataset: algorithms, visualization and human collaboration

We present a manually-labeled Author Name Disambiguation(AND) Dataset called WhoisWho, which consists of 399,255 documents and 45,187 distinct authors with 421 ambiguous author names. To label such a great amount of AND data of high accuracy, we propose a novel annotation framework where the human and computer collaborate efficiently and precisely. Within the framework, we also propose an inductive disambiguation model to classify whether two documents belong to the same author. We evaluate the proposed method and other state-of-the-art disambiguation methods on WhoisWho. The experiment results show that: (1) Our model outperforms other disambiguation algorithms on this challenging benchmark. (2) The AND problem still remains largely unsolved and requires more in-depth research. We believe that such a large-scale benchmark would bring great value for the author name disambiguation task. We also conduct several experiments to prove our annotation framework could assist annotators to make accurate results efficiently and eliminate wrong label problems made by human annotators effectively.
Gas and dust from metal-rich AGB stars

Context. Stars evolving through the asymptotic giant branch (AGB) phase provide significant feedback to their host system, which is both gas enriched in nuclear-burning products, and dust formed in their winds, which they eject into the interstellar medium. Therefore, AGB stars are an essential ingredient for the chemical evolution of the Milky Way and other galaxies. Aims. We study AGB models with super-solar metallicities to complete our vast database, so far extending from metal-poor to solar-chemical compositions. We provide chemical yields for masses in the range 1−8 M⊙ and metallicities Z = 0.03 and Z = 0.04. We also study dust production in this metallicity domain. Methods. We calculated the evolutionary sequences from the pre-main sequence through the whole AGB phase. We followed the variation of the surface chemical composition to calculate the chemical yields of the various species and model dust formation in the winds to determine the dust production rate and the total dust mass produced by each star during the AGB phase. Results. The physical and chemical evolution of the star is sensitive to the initial mass: M > 3 M⊙ stars experience hot bottom burning, whereas the surface chemistry of the lower mass counterparts is altered only by third dredge-up. The carbon-star phase is reached by 2.5−3.5 M⊙ stars of metallicity Z = 0.03, whereas all the Z = 0.04 stars (except the 2.5 M⊙) remain O-rich for the whole AGB phase. Most of the dust produced by metal-rich AGBs is in the form of silicate particles. The total mass of dust produced increases with the mass of the star, reaching ∼0.012 M⊙ for 8 M⊙ stars.
Over-The-Air Computation for Distributed Machine Learning

Motivated by various applications in distributed Machine Learning (ML) in massive wireless sensor networks, this paper addresses the problem of computing approximate values of functions over the wireless channel and provides examples of applications of our results to distributed training and ML-based prediction. The ``over-the-air'' computation of a function of data captured at different wireless devices has a huge potential for reducing the communication cost, which is needed for example for training of ML models. It is of particular interest to massive wireless scenarios because, as shown in this paper, its communication cost forntraining scales more favorable with the number of devices than that of traditional schemes that reconstruct all the data. We consider noisy fast-fading channels that pose major challenges to the ``over-the-air'' computation. As a result, function values are approximated from superimposed noisy signals transmitted by different devices. The fading and noise processes are not limited to Gaussian distributions, and are assumed to be in the more general class of sub-gaussian distributions. Our result does not assume necessarily independent fading and noise, thus allowing for correlations over time and between devices.
Sosed: a tool for finding similar software projects

In this paper, we present Sosed, a tool for discovering similar software projects. We use fastText to compute the embeddings of subto-kens into a dense space for 120,000 GitHub projects in 200 languages. Then, we cluster embeddings to identify groups of semantically similar subtokens that reflect topics in source code. We use a dataset of 9 million GitHub projects as a reference search base. To identify similar projects, we compare the distributions of clusters among their subtokens. The tool receives an arbitrary project as input, extracts subtokens in 16 most popular programming languages, computes cluster distribution, and finds projects with the closest distribution in the search base. We labeled subtoken clusters with short descriptions to enable Sosed to produce interpretable output. Sosed is available at https://github.com/JetBrains-Research/sosed/. The tool demo is available at https://www.youtube. com/watch? v=LYLkztCGRt8. The multi-language extractor of subtokens is available separately at https://github.com/JetBrains-Research/buckwheat/.
An Interstate Trips Analysis during COVID-19 in the United States

The worldwide outbreak of COVID-19 has posed a dire threat to the public. Human mobility has changed in various ways over the course of the pandemic. Despite current studies on common mobility metrics, research specifically on state-to-state mobility is very limited. By leveraging the mobile phone location data from over 100 million anonymous devices, we estimate the population flow between all states in the United States. We first analyze the temporal pattern and spatial differences of between-state flow from January 1, 2020 to May 15, 2020. Then, with repeated measures ANOVA and post-hoc analysis, we discern different time-course patterns of between-state population flow by pandemic severity groups. A further analysis shows moderate to high correlation between the flow reduction and the pandemic severity, the strength of which varies with different policies. This paper is promising in predicting imported cases.
Egocentric Action Recognition by Video Attention and Temporal Context

We present the submission of Samsung AI Centre Cambridge to the CVPR2020 EPIC-Kitchens Action Recognition Challenge. In this challenge, action recognition is posed as the problem of simultaneously predicting a single `verb' and `noun' class label given an input trimmed video clip. That is, a `verb' and a `noun' together define a compositional `action' class. The challenging aspects of this real-life action recognition task include small fast moving objects, complex hand-object interactions, and occlusions. At the core of our submission is a recently-proposed spatial-temporal video attention model, called `W3' (`What-Where-When') attention~\cite{perez2020knowing}. We further introduce a simple yet effective contextual learning mechanism to model `action' class scores directly from long-term temporal behaviour based on the `verb' and `noun' prediction scores. Our solution achieves strong performance on the challenge metrics without using object-specific reasoning nor extra training data. In particular, our best solution with multimodal ensemble achieves the 2$^{nd}$ best position for `verb', and 3$^{rd}$ best for `noun' and `action' on the Seen Kitchens test set.
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

This paper studies few-shot relation extraction, which aims at predicting the relation for a pair of entities in a sentence by training with a few labeled examples in each relation. To more effectively generalize to new relations, in this paper we study the relationships between different relations and propose to leverage a global relation graph. We propose a novel Bayesian meta-learning approach to effectively learn the posterior distribution of the prototype vectors of relations, where the initial prior of the prototype vectors is parameterized with a graph neural network on the global relation graph. Moreover, to effectively optimize the posterior distribution of the prototype vectors, we propose to use the stochastic gradient Langevin dynamics, which is related to the MAML algorithm but is able to handle the uncertainty of the prototype vectors. The whole framework can be effectively and efficiently optimized in an end-to-end fashion. Experiments on two benchmark datasets prove the effectiveness of our proposed approach against competitive baselines in both the few-shot and zero-shot settings.
Using Capsule Neural Network to predict Tuberculosis in lens-free microscopic images

Tuberculosis, caused by a bacteria called Mycobacterium tuberculosis, is one of the most serious public health problems worldwide. This work seeks to facilitate and automate the prediction of tuberculosis by the MODS method and using lens-free microscopy, which is easy to use by untrained personnel. We employ the CapsNet architecture in our collected dataset and show that it has a better accuracy than traditional CNN architectures.
A model-free tuning method for proportional-multi-resonant controllers

Resonant controllers are widely used in applications involving reference tracking and disturbance rejection of periodic signals. The controller design is typically performed by a trial-and-error approach or by means of time and resource-consuming analytic methods that require an accurate plant model, intricated mathematics and sophisticated tools. In this paper, we propose an easily implementable, model-free method for tuning a proportional-multi-resonant controller applicable to general linear time-invariant causal plants. Just like the Ziegler-Nichols methods, the proposed methodology consist in identifying one specific point of the plant's frequency response -- which is easily obtained in a relay with adjustable phase experiment -- and then designing the controller with simple tuning formulas and tables. The method is analyzed in detail for three examples, showing its practical appeal and wide applicability.
Deep Convolutional Neural Network for Identifying Seam-Carving Forgery

Seam carving is a representative content-aware image retargeting approach to adjust the size of an image. To preserve visually prominent content, seam-carving algorithms first calculate the connected path of pixels, referred to as the seam, according to a defined cost function and then adjust the size of an image by removing or duplicating repeatedly calculated seams. Seam carving is actively exploited to overcome diversity in the resolution of images between applications and devices; hence, detecting the distortion caused by seam carving has become important in image forensics. In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving forgery. To attain the ability to learn low-level features, we designed a convolutional neural network (CNN) architecture comprising five types of network blocks specialized in capturing local artifacts caused by seam carving. An ensemble module is further adopted to both enhance performance and comprehensively analyze the features in the local areas. To validate the effectiveness of our work, extensive experiments based on various CNN-based baselines were conducted. Compared to the baselines, our work exhibits state-of-the-art performance in terms of three-class classification (original, seam inserted, and seam removed). The experimental results also demonstrate that our model with the ensemble module is robust for various unseen cases.
DrugDBEmbed : Semantic Queries on Relational Database using Supervised Column Encodings

Traditional relational databases contain a lot of latent semantic information that have largely remained untapped due to the difficulty involved in automatically extracting such information. Recent works have proposed unsupervised machine learning approaches to extract such hidden information by textifying the database columns and then projecting the text tokens onto a fixed dimensional semantic vector space. However, in certain databases, task-specific class labels may be available, which unsupervised approaches are unable to lever in a principled manner. Also, when embeddings are generated at individual token level, then column encoding of multi-token text column has to be computed by taking the average of the vectors of the tokens present in that column for any given row. Such averaging approach may not produce the best semantic vector representation of the multi-token text column, as observed while encoding paragraphs or documents in natural language processing domain. With these shortcomings in mind, we propose a supervised machine learning approach using a Bi-LSTM based sequence encoder to directly generate column encodings for multi-token text columns of the DrugBank database, which contains gold standard drug-drug interaction (DDI) labels. Our text data driven encoding approach achieves very high Accuracy on the supervised DDI prediction task for some columns and we use those supervised column encodings to simulate and evaluate the Analogy SQL queries on relational data to demonstrate the efficacy of our technique.
NAPPO: Modular and scalable reinforcement learning in pytorch

Reinforcement learning (RL) has been very successful in recent years but, limited by its sample inefficiency, often requires large computational resources. While new methods are being investigated to increase the efficiency of RL algorithms it is critical to enable training at scale, yet using a code-base flexible enough to allow for method experimentation. Here, we present NAPPO, a pytorch-based library for RL which provides scalable proximal policy optimization (PPO) implementations in a simple, modular package. We validate it by replicating previous results on Mujoco and Atari environments. Furthermore, we provide insights on how a variety of distributed training schemes with synchronous and asynchronous communication patterns perform. Finally we showcase NAPPO by obtaining the highest to-date test performance on the Obstacle Tower Unity3D challenge environment. The full source code is available.
Pynsett: A programmable relation extractor

This paper proposes a programmable relation extraction method for the English language by parsing texts into semantic graphs. A person can define rules in plain English that act as matching patterns onto the graph representation. These rules are designed to capture the semantic content of the documents, allowing for flexibility and ad-hoc entities. Relation extraction is a complex task that typically requires sizeable training corpora. The method proposed here is ideal for extracting specialized ontologies in a limited collection of documents.
Covariate Distribution Aware Meta-learning

Meta-learning has proven to be successful at few-shot learning across the regression, classification and reinforcement learning paradigms. Recent approaches have adopted Bayesian interpretations to improve gradient based meta-learners by quantifying the uncertainty of the post-adaptation estimates. Most of these works almost completely ignore the latent relationship between the covariate distribution (p(x)) of a task and the corresponding conditional distribution p(y|x). In this paper, we identify the need to explicitly model the meta-distribution over the task covariates in a hierarchical Bayesian framework. We begin by introducing a graphical model that explicitly leverages very few samples drawn from p(x) to better infer the posterior over the optimal parameters of the conditional distribution (p(y|x)) for each task. Based on this model we provide an inference strategy and a corresponding meta-algorithm that explicitly accounts for the meta-distribution over task covariates. Finally, we demonstrate the significant gains of our proposed algorithm on a synthetic regression dataset.
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving

In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequality Theorem proving benchmark, specifically designed to test agents' generalization ability. INT is based on a procedure for generating theorems and proofs; this procedure's knobs allow us to measure 6 different types of generalization, each reflecting a distinct challenge characteristic to automated theorem proving. In addition, unlike prior benchmarks for learning-assisted theorem proving, INT provides a lightweight and user-friendly theorem proving environment with fast simulations, conducive to performing learning-based and search-based research. We introduce learning-based baselines and evaluate them across 6 dimensions of generalization with the benchmark. We then evaluate the same agents augmented with Monte Carlo Tree Search (MCTS) at test time, and show that MCTS can help to prove new theorems.
HANDLING HIGHLY CORRELATED GENES OF SINGLE-CELL RNA SEQUENCING DATA IN PREDICTION MODELS

Motivation: Selecting feature genes and predicting cells’ phenotype are typical tasks in the analysis of scRNA-seq data. Many algorithms were developed for these tasks, but high correlations among genes create challenges speciﬁcally in scRNA-seq analysis, which are not well addressed. Highly correlated genes lead to collinearity and unreliable model ﬁtting. Highly correlated genes compete with each other in feature selection, which causes underestimation of their importance. Most importantly, when a causal gene is highly correlated other genes, most algorithms select one of them in a data driven manner. The correlation structure among genes could change substantially. Hence, it is critical to build a prediction model based on causal genes but not their highly correlated genes. Results: To address the issues discussed above, we propose a grouping algorithm which can be integrated in prediction models. Using real benchmark scRNA-seq data sets and simulated cell phenotypes, we show our novel method signiﬁcantly outperform standard prediction models in the performance of both prediction and feature selection. Our algorithm report the whole group of correlated genes, which allow researchers to conduct additional studies to identify the causal genes from the group.