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

Line-intensity mapping of the 21cm line is a powerful probe of large scale structure at z<6, tracing large-scale structure via neutral hydrogen content that is found within galaxies. In principle, it enables cost-efficient surveys of the matter distribution up to z~6, unlocking orders of magnitude more modes for observational cosmology. Canada has been a traditional leader in this field, having led the first detections of the cosmological 21cm signal via cross-correlations with optical galaxy surveys and having constructed the Canadian Hydrogen Intensity Mapping Experiment (CHIME). The field is now entering a new era where data is abundant, allowing studies in how to overcome systematics to be tackled in an empirical, head-on fashion. In the next few years, this will produce the first detection of the 21cm auto power spectrum, which will pave the way towards a large suite of scientific possibilities. These potentially include precision measurements on the dark energy equation of state and other LCDM parameters, constraints on how HI mass traces dark matter, a detection of neutrino effects on large-scale structure, and the use of 21cm lensing to further constrain cosmology. To turn these promising directions into reality, we recommend a sustained program of investment in 21cm cosmology, starting with funding for the Canadian Hydrogen Observatory and Radio transient Detector (CHORD), followed by small-scale development efforts targeting next-generation hardware and sustained support for theory and technical staff support. Additionally, Canada should invest in complementary line-intensity mapping efforts (such as with CO or [CII] lines) and maintain participation in next-generation international efforts such as the Packed Ultra-wideband Mapping Array (PUMA) and the Square Kilometre Array (SKA).
Parallel Iterative Edit Models for Local Sequence Transduction

We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). Recent approaches are based on the popular encoder-decoder (ED) model for sequence to sequence learning. The ED model auto-regressively captures full dependency among output tokens but is slow due to sequential decoding. The PIE model does parallel decoding, giving up the advantage of modeling full dependency in the output, yet it achieves accuracy competitive with the ED model for four reasons: 1. predicting edits instead of tokens, 2. labeling sequences instead of generating sequences, 3. iteratively refining predictions to capture dependencies, and 4. factorizing logits over edits and their token argument to harness pre-trained language models like BERT. Experiments on tasks spanning GEC, OCR correction and spell correction demonstrate that the PIE model is an accurate and significantly faster alternative for local sequence transduction.
Challenges and Opportunities of Near-Term Quantum Computing Systems

The concept of quantum computing has inspired a whole new generation of scientists, including physicists, engineers, and computer scientists, to fundamentally change the landscape of information technology. With experimental demonstrations stretching back more than two decades, the quantum computing community has achieved a major milestone over the past few years: the ability to build systems that are stretching the limits of what can be classically simulated, and which enable cloud-based research for a wide range of scientists, thus increasing the pool of talent exploring early quantum systems. While such noisy near-term quantum computing systems fall far short of the requirements for fault-tolerant systems, they provide unique test beds for exploring the opportunities for quantum applications. Here, we highlight an IBM-specific perspective of the facets associated with these systems, including quantum software, cloud access, benchmarking quantum systems, error correction and mitigation in such systems, understanding the complexity of quantum circuits, and how early quantum applications can run on near-term quantum computers.
Correlations between Word Vector Sets

Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic textual similarity (STS) tasks. In contrast to commonly used geometric approaches, we treat a single word embedding as e.g. 300 observations from a scalar random variable. Using this paradigm, we first illustrate that similarities derived from elementary pooling operations and classic correlation coefficients yield excellent results on standard STS benchmarks, outperforming many recently proposed methods while being much faster and trivial to implement. Next, we demonstrate how to avoid pooling operations altogether and compare sets of word embeddings directly via correlation operators between reproducing kernel Hilbert spaces. Just like cosine similarity is used to compare individual word vectors, we introduce a novel application of the centered kernel alignment (CKA) as a natural generalisation of squared cosine similarity for sets of word vectors. Likewise, CKA is very easy to implement and enjoys very strong empirical results.
A framework for measuring weak-lensing magnification using the Fundamental Plane

Galaxy-galaxy lensing is an essential tool for probing dark matter halos and constraining cosmological parameters. While galaxy-galaxy lensing measurements usually rely on shear, weak-lensing magnification contains additional constraining information. Using the fundamental plane (FP) of elliptical galaxies to anchor the size distribution of a background population is one method that has been proposed for performing a magnification measurement. We present a formalism for using the FP residuals of elliptical galaxies to jointly estimate the foreground mass and background redshift errors for a stacked lens scenario. The FP residuals include information about weak-lensing magnification $\kappa$, and therefore foreground mass, since to first order, nonzero $\kappa$ affects galaxy size but not other FP properties. We also present a modular, extensible code that implements the formalism using emulated galaxy catalogs of a photometric galaxy survey. We find that combining FP information with observed number counts of the source galaxies constrains mass and photo-z error parameters significantly better than an estimator that includes number counts only. In particular, the improvement in the constraint on the mass is an order of magnitude better if FP residuals are included. Furthermore, we find that the mass estimator is robust to selection effects. The effective size noise for a foreground lens of mass $M_H=10^{14}M_\odot$, with a conservative selection function in size and surface brightness applied to the source population, is $\sigma_{\kappa,\mathrm{eff}}=0.183$. We discuss the improvements to our FP model necessary to make this formalism a practical companion to shear analyses in weak lensing surveys.
Commonsense Knowledge Base Completion with Structural and Semantic Context

Automatic KB completion for commonsense knowledge graphs (e.g.,  poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form text to represent nodes, resulting in orders of magnitude more nodes compared to conventional KBs ( ∼18x more nodes in ATOMIC compared to Freebase (FB15K-237)). Importantly, this implies significantly sparser graph structures — a major challenge for existing KB completion methods that assume densely connected graphs over a relatively smaller set of nodes.In this paper, we present novel KB completion models that can address these challenges by exploiting the structural and semantic context of nodes. Specifically, we investigate two key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densification and (2) transfer learning from pre-trained language models to knowledge graphs for enhanced contextual representation of knowledge. We describe our method to incorporate information from both these sources in a joint model and provide the first empirical results for KB completion on ATOMIC and evaluation with ranking metrics on ConceptNet. Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1.5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency. Further analysis on model predictions shines light on the types of commonsense knowledge that language models capture well.
Multi-Modal Machine Learning for Floods Detection in News, Social Media and Satellite Sequences

In this paper we present our methods for the MediaEval 2019 Mul-timedia Satellite Task, which is aiming to extract complementaryinformation associated with adverse events from Social Media andsatellites. For the first challenge, we propose a framework jointly uti-lizing colour, object and scene-level information to predict whetherthe topic of an article containing an image is a flood event or not.Visual features are combined using early and late fusion techniquesachieving an average F1-score of82.63,82.40,81.40and76.77. Forthe multi-modal flood level estimation, we rely on both visualand textual information achieving an average F1-score of58.48and46.03, respectively. Finally, for the flooding detection in time-based satellite image sequences we used a combination of classicalcomputer-vision and machine learning approaches achieving anaverage F1-score of58.82%
Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation

Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity. To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data towards a common arrangement such that semantic recognition suffers less from deformation. This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field. Yet adapting the receptive field does not quite reach the actual goal -- what really matters to the network is the "effective" receptive field (ERF), which reflects how much each pixel contributes. It is thus natural to design other approaches to adapt the ERF directly during runtime. 
In this work, we instantiate one possible solution as Deformable Kernels (DKs), a family of novel and generic convolutional operators for handling object deformations by directly adapting the ERF while leaving the receptive field untouched. At the heart of our method is the ability to resample the original kernel space towards recovering the deformation of objects. This approach is justified with theoretical insights that the ERF is strictly determined by data sampling locations and kernel values. We implement DKs as generic drop-in replacements of rigid kernels and conduct a series of empirical studies whose results conform with our theories. Over several tasks and standard base models, our approach compares favorably against prior works that adapt during runtime. In addition, further experiments suggest a working mechanism orthogonal and complementary to previous works.
The false myth of the rise in self-citations, and the impressively positive effect of bibliometric evaluations on the increase of the impact of Italian research

It has recently been claimed by Baccini and coauthors that due to ANVUR's bibliometric evaluations of individuals, departments, and universities, in Italy there has been a surge in self-citations in the last ten years, thus increasing the "inwardness" of Italian research more than has happened abroad. We have studied the database of Ioannidis et al. published on 12 August 2019 of the one hundred thousand most "highly cited" scientists, including about two thousand Italians, and we found that the problem of self-citations in relation to this scientific elite is not significant in Italy, while perhaps observing a small deviation in the low scores in the rankings. The effect indicated by Baccini et al. consequently, does not seem worrying for the scientific elite (we quantified it in 2 percent of the total of scientists of the "best" one hundred thousand), and is probably largely concentrated in the further less cited scientists. Evaluation agencies like ANVUR should probably exclude self-citations in future evaluations, for the noise introduced by the young researchers. The overall state of health of the Italian research system and the positive effect of the ANVUR assessments are demonstrated by the number of Italian researchers in the top one hundred thousand, which has increased by comparing the "career" databased of 22 years, with that of the "young" researchers in the "2017" database. Italy, looking at the elite researchers, not only is not the most indulgent in self-citations, but has shown the best improvements, proving that the introduction of ANVUR had a positive effect. Indeed, all countries apart from Italy have suffered a decline, even substantial (-20 percent on a national Japan scale), of the number of researchers present in the 2017 data sets compared to career data. Italy instead shows a +0.2 percent on a global basis and an impressive +11.53 percent on a national basis.
Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network

In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure. Different methods are evaluated to find the optimal way to insert conventional covariates into deep prediction networks. Correlation analysis between autoencoder latent codes and covariate features is used to examine how these predictors interact. We believe that similar approaches could also be used to introduce knowledge of genetic variants to such survival networks to improve outcome prediction by jointly analysing cardiac motion traits with inheritable risk factors.
The Shadow of a Spherically Accreting Black Hole

We explore a simple spherical model of optically thin accretion on a Schwarzschild black hole, and study the properties of the image as seen by a distant observer. We show that a dark circular region in the center—a shadow—is always present. The outer edge of the shadow is located at the photon ring radius , where is the gravitational radius of the accreting mass M. The location of the shadow edge is independent of the inner radius at which the accreting gas stops radiating. The size of the observed shadow is thus a signature of the spacetime geometry and it is hardly influenced by accretion details. We briefly discuss the relevance of these results for the Event Horizon Telescope image of the supermassive black hole in M87.
The evolution of rest-frame UV properties, Ly α EWs, and the SFR–stellar mass relation at z ∼ 2–6 for SC4K LAEs

We explore deep rest-frame UV to FIR data in the COSMOS field to measure the individual spectral energy distributions (SED) of the ∼4000 SC4K (Sobral et al.) Lyman α (Ly α) emitters (LAEs) at z ∼ 2–6. We find typical stellar masses of 109.3 ± 0.6 M⊙ and star formation rates (SFR) of SFR$_{\rm SED}=4.4^{+10.5}_{-2.4}$ M⊙ yr−1 and SFR$_{\rm Ly\,\alpha }=5.9^{+6.3}_{-2.6}$ M⊙ yr−1, combined with very blue UV slopes of $\beta =-2.1^{+0.5}_{-0.4}$, but with significant variations within the population. MUV and β are correlated in a similar way to UV-selected sources, but LAEs are consistently bluer. This suggests that LAEs are the youngest and/or most dust-poor subset of the UV-selected population. We also study the Ly α rest-frame equivalent width (EW0) and find 45 ‘extreme’ LAEs with EW0 > 240 Å (3σ), implying a low number density of (7 ± 1) × 10−7 Mpc−3. Overall, we measure little to no evolution of the Ly α EW0 and scale length parameter (w0), which are consistently high (EW$_0=140^{+280}_{-70}$ Å, $w_0=129^{+11}_{-11}$ Å) from z ∼ 6 to z ∼ 2 and below. However, w0 is anticorrelated with MUV and stellar mass. Our results imply that sources selected as LAEs have a high Ly α escape fraction (fesc,Ly α) irrespective of cosmic time, but fesc,Ly α is still higher for UV-fainter and lower mass LAEs. The least massive LAEs (<109.5 M⊙) are typically located above the star formation ‘main sequence’ (MS), but the offset from the MS decreases towards z ∼ 6 and towards 1010 M⊙. Our results imply a lack of evolution in the properties of LAEs across time and reveals the increasing overlap in properties of LAEs and UV-continuum selected galaxies as typical star-forming galaxies at high redshift effectively become LAEs.
Repeated gravitational lensing of gravitational waves in hierarchical black hole triples

We consider binary black holes (BBHs) in a hierarchical triple system where a more compact, less-massive binary is emitting detectable gravitational waves (GWs), and the tertiary is a supermassive BH at the center of a nuclear star cluster. As previous works have shown, the orbital motion of the outer binary can generate a detectable relativistic Doppler boost of the GWs emitted by the orbiting inner binary. We show here that for outer-binary orbits with a period of order one year, there can be a non-negligible probability for repeated gravitational lensing of the GWs emitted by the inner binary. Repeating gravitational lensing events could be detected by the LISA observatory as periodic GW amplitude spikes before the BBH enters the LIGO band. Such a detection would confirm the origin of some BBH mergers in nuclear star clusters. GW lensing also offers new testing grounds for strong gravity.
Universal upper bound on the inflationary energy scale from the trans-Planckian censorship conjecture

We study the constraint on the inflationary energy scale from the recently proposed Trans-Planckian Censorship Conjecture (TCC). We find a universal upper bound on the inflationary Hubble expansion rate $H_\text{inf}$ which is solely determined by the reheating temperature $T_\text{rh}$: $H_\text{inf}/M_\text{Pl}\lesssim T_0/T_\text{rh}$, where $T_0$ is the photon temperature today. The upper limit can be saturated by a post-inflationary oscillatory stage with the critical equation-of-state parameter $w\approx-1/3$, or by an inflation model with multiple stages. In the lowest reheating temperature required for big bang nucleosynthesis, the upper bound on the tensor-to-scalar ratio $r$ at the CMB scales is $r\lesssim10^{-8}$, which can be realized in many string-inspired inflation models.
On the size of the CO-depletion radius in the IRDC G351.77−0.51

An estimate of the degree of CO-depletion (fD) provides information on the physical conditions occurring in the innermost and densest regions of molecular clouds. A key parameter in these studies is the size of the depletion radius, i.e. the radius within which the C-bearing species, and in particular CO, are largely frozen on to dust grains. A strong depletion state (i.e. fD > 10, as assumed in our models) is highly favoured in the innermost regions of dark clouds, where the temperature is <20 K and the number density of molecular hydrogen exceeds a few × 104 cm−3. In this work, we estimate the size of the depleted region by studying the Infrared Dark Cloud (IRDC) G351.77−0.51. Continuum observations performed with the Herschel Space Observatory and the LArge APEX BOlometer CAmera, together with APEX C18O and C17O J = 2→1 line observations, allowed us to recover the large-scale beam- and line-of-sight-averaged depletion map of the cloud. We built a simple model to investigate the depletion in the inner regions of the clumps in the filament and the filament itself. The model suggests that the depletion radius ranges from 0.02 to 0.15 pc, comparable with the typical filament width (i.e. ∼0.1 pc). At these radii, the number density of H2 reaches values between 0.2 and 5.5 × 105 cm−3. These results provide information on the approximate spatial scales on which different chemical processes operate in high-mass star-forming regions and also suggest caution when using CO for kinematical studies in IRDCs.
Leveraging Vision Reconstruction Pipelines for Satellite Imagery

Reconstructing 3D geometry from satellite imagery is an important and growing topic of research. However, disparities exist between how this 3D reconstruction problem is handled in the remote sensing context and how multi-view reconstruction pipelines have been developed in the computer vision community. In this paper, we explore whether state-of-the-art reconstruction pipelines from the vision community can be applied to the satellite imagery. Along the way, we address several challenges adapting vision-based structure from motion and multi-view stereo methods. We show that vision pipelines can offer competitive speed and accuracy in the satellite context.
Gravitational Fluctuations as an Alternative to Inflation II. CMB Angular Power Spectrum

Power spectra always play an important role in the theory of inflation. In particular, the ability to reproduce the galaxy matter power spectrum P ( k ) and the CMB temperature angular power spectrum C l ’s to high accuracy is often considered a triumph of inflation. In our previous work, we presented an alternative explanation for the matter power spectrum based on nonperturbative quantum field-theoretical methods applied to Einstein’s gravity, instead of inflation models based on scalar fields. In this work, we review the basic concepts and provide further in-depth investigations. We first update the analysis with more recent data sets and error analysis, and then extend our predictions to the CMB angular spectrum coefficients C l , which we did not consider previously. Then we investigate further the potential freedoms and uncertainties associated with the fundamental parameters that are part of this picture, and show how recent cosmological data provides significant constraints on these quantities. Overall, we find good general consistency between theory and data, even potentially favoring the gravitationally-motivated picture at the largest scales. We summarize our results by outlining how this picture can be tested in the near future with increasingly accurate astrophysical measurements.
Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels

Many real-world video analysis applications require the ability to identify domain-specific events in video, such as interviews and commercials in TV news broadcasts, or action sequences in film. Unfortunately, pre-trained models to detect all the events of interest in video may not exist, and training new models from scratch can be costly and labor-intensive. In this paper, we explore the utility of specifying new events in video in a more traditional manner: by writing queries that compose outputs of existing, pre-trained models. To write these queries, we have developed Rekall, a library that exposes a data model and programming model for compositional video event specification. Rekall represents video annotations from different sources (object detectors, transcripts, etc.) as spatiotemporal labels associated with continuous volumes of spacetime in a video, and provides operators for composing labels into queries that model new video events. We demonstrate the use of Rekall in analyzing video from cable TV news broadcasts, films, static-camera vehicular video streams, and commercial autonomous vehicle logs. In these efforts, domain experts were able to quickly (in a few hours to a day) author queries that enabled the accurate detection of new events (on par with, and in some cases much more accurate than, learned approaches) and to rapidly retrieve video clips for human-in-the-loop tasks such as video content curation and training data curation. Finally, in a user study, novice users of Rekall were able to author queries to retrieve new events in video given just one hour of query development time.