nils-herrmann/clean_scirepeval huggingface dataset:
High-redshift 21cm Cosmology in Canada

In the next few years, the 21cm line will enable direct observations of the Dark Ages, Cosmic Dawn, and Reionization, which represent previously unexplored periods in our cosmic history. With a combination of sky-averaged global signal measurements and spatial mapping surveys, the possible science return is enormous. This potentially includes (but is not limited to) constraints on first-generation galaxies (such as their typical masses and luminosities), constraints on cosmological parameters, a measurement of the Hubble parameter at z~15 to 20, the elimination of the optical depth nuisance parameter in the CMB, and searches for exotic phenomena such as baryon-dark matter couplings. To enable continued Canadian leadership in these science opportunities, we recommend 1) continued investments in 21cm experiments at all redshifts, 2) detailed analysis efforts of current data to overcome systematic effects, 3) new investments in preliminary experiments to explore the truly low-frequency sky as a stepping stone towards the Dark Ages, 4) new investments in line-intensity mapping experiments beyond the 21cm line, 5) continued theory support for 21cm cosmology, and 6) continued participation and knowledge transfer to next-generation international efforts such as the Square Kilometre Array.
Credible Sample Elicitation by Deep Learning, for Deep Learning

It is important to collect credible training samples $(x,y)$ for building data-intensive learning systems (e.g., a deep learning system). In the literature, there is a line of studies on eliciting distributional information from self-interested agents who hold a relevant information. Asking people to report complex distribution $p(x)$, though theoretically viable, is challenging in practice. This is primarily due to the heavy cognitive loads required for human agents to reason and report this high dimensional information. Consider the example where we are interested in building an image classifier via first collecting a certain category of high-dimensional image data. While classical elicitation results apply to eliciting a complex and generative (and continuous) distribution $p(x)$ for this image data, we are interested in eliciting samples $x_i \sim p(x)$ from agents. This paper introduces a deep learning aided method to incentivize credible sample contributions from selfish and rational agents. The challenge to do so is to design an incentive-compatible score function to score each reported sample to induce truthful reports, instead of an arbitrary or even adversarial one. We show that with accurate estimation of a certain $f$-divergence function we are able to achieve approximate incentive compatibility in eliciting truthful samples. We then present an efficient estimator with theoretical guarantee via studying the variational forms of $f$-divergence function. Our work complements the literature of information elicitation via introducing the problem of \emph{sample elicitation}. We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.
Model-based Behavioral Cloning with Future Image Similarity Learning

We present a visual imitation learning framework that enables learning of robot action policies solely based on expert samples without any robot trials. Robot exploration and on-policy trials in a real-world environment could often be expensive/dangerous. We present a new approach to address this problem by learning a future scene prediction model solely on a collection of expert trajectories consisting of unlabeled example videos and actions, and by enabling generalized action cloning using future image similarity. The robot learns to visually predict the consequences of taking an action, and obtains the policy by evaluating how similar the predicted future image is to an expert image. We develop a stochastic action-conditioned convolutional autoencoder, and present how we take advantage of future images for robot learning. We conduct experiments in simulated and real-life environments using a ground mobility robot with and without obstacles, and compare our models to multiple baseline methods.
Deep Multiphase Level Set for Scene Parsing

Recently, Fully Convolutional Network (FCN) seems to be the go-to architecture for image segmentation, including semantic scene parsing. However, it is difficult for a generic FCN to predict semantic labels around the object boundaries, thus FCN-based methods usually produce parsing results with inaccurate boundaries. Meanwhile, many works have demonstrate that level set based active contours are superior to the boundary estimation in sub-pixel accuracy. However, they are quite sensitive to initial settings. To address these limitations, in this paper we propose a novel Deep Multiphase Level Set (DMLS) method for semantic scene parsing, which efficiently incorporates multiphase level sets into deep neural networks. The proposed method consists of three modules, i.e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning. More specifically, recurrent FCNs learn multi-level representations of input images with different contexts. Adaptive multiphase level set drives the discriminative contour for each semantic class, which makes use of the advantages of both global and local information. In each time-step of the recurrent FCNs, deeply supervised learning is incorporated for model training. Extensive experiments on three public benchmarks have shown that our proposed method achieves new state-of-the-art performances. The source codes will be released at https://github.com/Pchank/DMLS-for-SSP.
Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach

Traditional sequence-to-sequence (seq2seq) models and other variations of the attention-mechanism such as hierarchical attention have been applied to the text summarization problem. Though there is a hierarchy in the way humans use language by forming paragraphs from sentences and sentences from words, hierarchical models have usually not worked that much better than their traditional seq2seq counterparts. This effect is mainly because either the hierarchical attention mechanisms are too sparse using hard attention or noisy using soft attention. In this paper, we propose a method based on extracting the highlights of a document; a key concept that is conveyed in a few sentences. In a typical text summarization dataset consisting of documents that are 800 tokens in length (average), capturing long-term dependencies is very important, e.g., the last sentence can be grouped with the first sentence of a document to form a summary. LSTMs (Long Short-Term Memory) proved useful for machine translation. However, they often fail to capture long-term dependencies while modeling long sequences. To address these issues, we have adapted Neural Semantic Encoders (NSE) to text summarization, a class of memory-augmented neural networks by improving its functionalities and proposed a novel hierarchical NSE that outperforms similar previous models significantly. The quality of summarization was improved by augmenting linguistic factors, namely lemma, and Part-of-Speech (PoS) tags, to each word in the dataset for improved vocabulary coverage and generalization. The hierarchical NSE model on factored dataset outperformed the state-of-the-art by nearly 4 ROUGE points. We further designed and used the first GPU-based self-critical Reinforcement Learning model.
Supersingular Curves With Small Non-integer Endomorphisms

We introduce a special class of supersingular curves over $\mathbb{F}_{p^2}$, characterized by the existence of non-integer endomorphisms of small degree. A number of properties of this set is proved. Most notably, we show that this set partitions into subsets in such a way that curves within each subset have small-degree isogenies between them, but curves in distinct subsets have no isogenies of degree $O(p^{1/4-\varepsilon})$ between them. Despite this, we show that isogenies between these curves can be computed efficiently, giving a technique for computing isogenies between certain prescribed curves that cannot be reasonably connected by searching on $\ell$-isogeny graphs.
Dynamic Mode Decomposition based feature for Image Classification

Irrespective of the fact that Machine learning has produced groundbreaking results, it demands an enormous amount of data in order to perform so. Even though data production has been in its all-time high, almost all the data is unlabelled, hence making them unsuitable for training the algorithms. This paper proposes a novel method11All codes and datasets used in this paper are available at: https://github.com/rahulvigneswaran/Dynamic-Mode-Decomposition-basedfeature-for-Image-Classification of extracting the features using Dynamic Mode Decomposition (DMD). The experiment is performed using data samples from Imagenet. The learning is done using SVM-linear, SVM-RBF, Random Kitchen Sink approach (RKS). The results have shown that DMD features with RKS give competing results.
Lung nodule segmentation via level set machine learning

Lung cancer has the highest mortality rate of all cancers in both men and women. The algorithmic detection, characterization, and diagnosis of abnormalities found in chest CT scan images can potentially aid radiologists by providing additional medical information to consider in their assessment. Lung nodule segmentation, i.e., the algorithmic delineation of the lung nodule surface, is a fundamental component of an automated nodule analysis pipeline. We introduce an extension of the vanilla level set image segmentation method where the velocity function is learned from data via machine learning regression methods, rather than manually designed. This mitigates the tedious design process of the velocity term from the standard method. We apply the method to image volumes of lung nodules from CT scans in the publicly available LIDC dataset, obtaining an average intersection over union score of 0.7185($\pm$0.1114).
Noncompliance in randomized control trials without exclusion restrictions

For randomized experiments with noncompliance, I propose a method to identify treatment effects without exclusion restrictions. It exploits a baseline survey that is commonly available in randomized experiments. I show the identification of the average treatment effect on the treated (ATT) and the local average treatment effect (LATE), assuming that a baseline variable maintains similar rank orders to the control outcome. I apply this strategy to a microcredit experiment with one-sided noncompliance to identify the ATT. I find that the instrumental variable (IV) estimate of log revenue is 2.2 times larger than my preferred estimate. Supplemental materials are available online.
Understanding the Expressive Power of Unhygienic Substitution in Metaprogramming via Combinatory Logic

Recent work on combinatory logic demonstrates a compositional translation from lambda calculus that gives meaning to open terms. As the meaning of open terms is a key difficulty in the study of metaprogramming, we investigate whether this idea can be extended to metaprogramming systems with hygienic and unhygienic substitution, specifically $\lambda_{e}^{\rightarrow \Box}$ and $\lambda_{\mathcal{S}}$. We conclude that there is quantitative evidence to support the claim that metaprogramming with unhygienic substitution is more powerful than metaprogramming with hygienic substitution.
The Nature of Human Settlement: Building an understanding of high performance city design

In an impending urban age where the majority of the world's population will live in cities, it is critical that we improve our understanding of the strengths and limitations of existing city designs to ensure they are safe, clean, can deliver health co-benefits and importantly, are sustainable into the future. To enable this, a systematic and efficient means of performing inter- and intra-city comparisons based on urban form is required. Until now, methods for comparing cities have been limited by scalability, often reliant upon non-standardised local input data that can be costly and difficult to obtain. To address this, we have developed a unique approach to determine the mix, distribution, and composition of neighbourhood types in cities based on dimensions of block size and regularity, sorted by a self-organising map. We illustrate the utility of the method to provide an understanding of the underlying city morphology by overlaying spatially standardised city metrics such as air pollution and transport activity across a set of 1667 global cities with populations exceeding 300,000. The unique approach reports associations between specific mixes of neighbourhood typologies and quantities of moving vehicles (r=0.97), impervious surfaces (r=0.86), and air pollution levels (aerosol optical depth r=0.58 and NO$_{2}$ r=0.57). What this illustrates, is that this unique approach can identify the characteristics and neighbourhood mixes of well-performing urban areas while also producing unique `city fingerprints' that can be used to provide new metrics, insights, and drive improvements in city design for the future.
The 'Paris-end' of town? Urban typology through machine learning

The confluence of recent advances in availability of geospatial information, computing power, and artificial intelligence offers new opportunities to understand how and where our cities differ or are alike. Departing from a traditional `top-down' analysis of urban design features, this project analyses millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics. A (novel) neural network-based framework is trained with imagery from the largest 1692 cities in the world and the resulting models are used to compare within-city locations from Melbourne and Sydney to determine the closest connections between these areas and their international comparators. This work demonstrates a new, consistent, and objective method to begin to understand the relationship between cities and their health, transport, and environmental consequences of their design. The results show specific advantages and disadvantages using each type of imagery. Neural networks trained with map imagery will be highly influenced by the mix of roads, public transport, and green and blue space as well as the structure of these elements. The colours of natural and built features stand out as dominant characteristics in satellite imagery. The use of street view imagery will emphasise the features of a human scaled visual geography of streetscapes. Finally, and perhaps most importantly, this research also answers the age-old question, ``Is there really a `Paris-end' to your city?''.
Carbon Chain Depletion of 2I/Borisov

The composition of comets in the solar system comes in multiple groups thought to encode information about their formation in different regions of the outer protosolar disk. The recent discovery of the second interstellar object, 2I/Borisov, allows for spectroscopic investigations into its gas content and a preliminary classification of it within the solar system comet taxonomies to test the applicability of planetesimal formation models to other stellar systems. We present spectroscopic and imaging observations from 2019 September 20 through October 26 from the Bok, MMT telescope (formerly the Multiple Mirror Telescope, Mount Hopkins, Arizona), and Large Binocular Telescopes. We identify CN in the comet’s spectrum and set precise upper limits on the abundance of C2 on all dates in October. We use a Haser model to convert our integrated fluxes to production rates and find Q(CN) = (1.1–1.9) ∗ 1024 mols s−1 increasing over 2019 October 1 to 26, consistent with contemporaneous observations. We set our lowest upper limit on a C2 production rate, Q(C2) < 1.6 ∗ 1023 mols s−1 on 2019 October 10. The measured upper limit ratio for that date Q(C2)/Q(CN) < 0.1 indicates that 2I/Borisov is strongly in the (carbon-chain) “depleted” taxonomic group if there is any C2 production at all. Most “depleted” comets are Jupiter-family comets (JFCs), perhaps indicating a similarity in formation conditions between the most depleted of the JFCs and 2I/Borisov. More work is needed to understand the applicability of our knowledge of solar system comet taxonomies onto interstellar objects and we discuss future work that could help to clarify the usefulness of the approach.
Identifying Candidate Spaces for Advert Implantation

Virtual advertising is an important and promising feature in the area of online advertising. It involves integrating adverts onto live or recorded videos for product placements and targeted advertisements. Such integration of adverts is primarily done by video editors in the post-production stage, which is cumbersome and time-consuming. Therefore, it is important to automatically identify candidate spaces in a video frame, wherein new adverts can be implanted. The candidate space should match the scene perspective, and also have a high quality of experience according to human subjective judgment. In this paper, we propose the use of a bespoke neural net that can assist the video editors in identifying candidate spaces. We benchmark our approach against several deep-learning architectures on a large-scale image dataset of candidate spaces of outdoor scenes. Our work is the first of its kind in this area of multimedia and augmented reality applications, and achieves the best results.
Meta Module Network for Compositional Visual Reasoning

Neural Module Network (NMN) exhibits strong interpretability and compositionality thanks to its handcrafted neural modules with explicit multi-hop reasoning capability. However, most NMNs suffer from two critical draw-backs: 1) scalability: customized module for specific function renders it impractical when scaling up to a larger set of functions in complex tasks; 2) generalizability: rigid pre-defined module inventory makes it difficult to generalize to unseen functions in new tasks/domains. To design a more powerful NMN architecture for practical use, we propose Meta Module Network (MMN) centered on a novel meta module, which can take in function recipes and morph into diverse instance modules dynamically. The instance modules are then woven into an execution graph for complex visual reasoning, inheriting the strong explainability and compositionality of NMN. With such a flexible instantiation mechanism, the parameters of instance modules are inherited from the central meta module, retaining the same model complexity as the function set grows, which promises better scalability. Meanwhile, as functions are encoded into the embedding space, unseen functions can be readily represented based on its structural similarity with previously observed ones, which ensures better generalizability. Experiments on GQA and CLEVR datasets validate the superiority of MMN over state-of-the-art NMN designs. Synthetic experiments on held-out unseen functions from GQA dataset also demonstrate the strong generalizability of MMN. Our code and model are released in Github1.
Metric Pose Estimation for Human-Machine Interaction Using Monocular Vision

The rapid growth of collaborative robotics in production requires new automation technologies that take human and machine equally into account. In this work, we describe a monocular camera based system to detect human-machine interactions from a bird's-eye perspective. Our system predicts poses of humans and robots from a single wide-angle color image. Even though our approach works on 2D color input, we lift the majority of detections to a metric 3D space. Our system merges pose information with predefined virtual sensors to coordinate human-machine interactions. We demonstrate the advantages of our system in three use cases.
Self-Paced Deep Regression Forests for Facial Age Estimation

Facial age estimation is an important and challenging problem in computer vision. Existing approaches usually employ deep neural networks (DNNs) to fit the mapping from facial features to age, even though there exist some noisy and confusing samples. We argue that it is more desirable to distinguish noisy and confusing facial images from regular ones, and alleviate the interference arising from them. To this end, we propose self-paced deep regression forests (SP-DRFs) -- a gradual learning DNNs framework for age estimation. As the model is learned gradually, from simplicity to complexity, it tends to emphasize more on reliable samples and avoid bad local minima. Moreover, the proposed capped-likelihood function helps to exclude noisy samples in training, rendering our SP-DRFs significantly more robust. We demonstrate the efficacy of SP-DRFs on Morph II and FG-NET datasets, where our model achieves state-of-the-art performance.
Riposte! A Large Corpus of Counter-Arguments

Constructive feedback is an effective method for improving critical thinking skills. Counter-arguments (CAs), one form of constructive feedback, have been proven to be useful for critical thinking skills. However, little work has been done for constructing a large-scale corpus of them which can drive research on automatic generation of CAs for fallacious micro-level arguments (i.e. a single claim and premise pair). In this work, we cast providing constructive feedback as a natural language processing task and create Riposte!, a corpus of CAs, towards this goal. Produced by crowdworkers, Riposte! contains over 18k CAs. We instruct workers to first identify common fallacy types and produce a CA which identifies the fallacy. We analyze how workers create CAs and construct a baseline model based on our analysis.
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech

Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still a tough problem. Tackling hate speech in the standard way of content deletion or user suspension may be charged with censorship and overblocking. One alternate strategy, that has received little attention so far by the research community, is to actually oppose hate content with counter-narratives (i.e. informed textual responses). In this paper, we describe the creation of the first large-scale, multilingual, expert-based dataset of hate-speech/counter-narrative pairs. This dataset has been built with the effort of more than 100 operators from three different NGOs that applied their training and expertise to the task. Together with the collected data we also provide additional annotations about expert demographics, hate and response type, and data augmentation through translation and paraphrasing. Finally, we provide initial experiments to assess the quality of our data.
Synthetic Fast Ion Diagnostics in Tokamaks: Comparing the Monte Carlo Test Particle Code ASCOT against Experiments

Measuring fast ions, most notably fusion alphas, in ITER and future reactors remains an issue that still lacks an adequate solution. Numerical simulations are invaluable in testing the potential and limitations of various proposed diagnostics. However, the validity of the numerical tools first has to be checked against results from existing tokamaks. In this contribution, various synthetic diagnostics for fast ions (collective Thomson scattering, neutral particle analyzer, neutron camera, infrared measurements, fast ion loss detector, and activation probe) from the orbit-following Monte Carlo code ASCOT are compared to measurements from several tokamaks (ASDEX Upgrade, DIII-D, and JET). Within the limitations of the physics included in the numerical model and availability of input data from experiments, the agreement between synthetic data and measurements is found to be quite good.