nils-herrmann/clean_scirepeval huggingface dataset:
Flud: a hybrid crowd-algorithm approach for visualizing biological networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this article, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, a crowd-powered system that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Furthermore, we propose a novel hybrid approach for graph layout wherein crowd workers and a simulated annealing algorithm build on each other’s progress. A study of about 2,000 crowd workers on Amazon Mechanical Turk showed that the hybrid crowd–algorithm approach outperforms the crowd-only approach and state-of-the-art techniques when workers were asked to lay out complex networks that represent signaling pathways. Another study of seven participants with biological training showed that Flud layouts are more effective compared to those created by state-of-the-art techniques. We also found that the algorithmically generated suggestions guided the workers when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in layout design tasks beyond biology.
LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pre-trained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our pre-trained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR2, and improve the previous best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel model components and pre-training strategies significantly contribute to our strong results. Code and pre-trained models publicly available at: https://github.com/airsplay/lxmert
Controversy in Context

With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is. Prior works relied on Wikipedia's metadata and on content analysis of the articles pertaining to a concept in question. Here we show that the immediate textual context of a concept is strongly indicative of this property, and, using simple and language-independent machine-learning tools, we leverage this observation to achieve state-of-the-art results in controversiality prediction. In addition, we analyze and make available a new dataset of concepts labeled for controversiality. It is significantly larger than existing datasets, and grades concepts on a 0-10 scale, rather than treating controversiality as a binary label.
Predictions for the diffuse cosmic dipole at radio frequencies from reionization imprints

The cosmological reionization and thermal history, following the recombination epoch and the dark age, can be studied at radio frequencies through the tomographic view offered by the redshifted 21 cm line and the integrated information offered by the diffuse free-free emission, coupled to the Comptonization distortion, which is relevant at higher frequencies. For these types of signals, current theoretical predictions span a wide range of possibilities. The recent EDGES observations of the monopole disagree with the typical standard models and call, if confirmed, for non-standard physical processes and/or for an early population of extragalactic sources producing a remarkable radio background at high redshifts that is almost consistent with the ARCADE 2 claim of a significant excess of cosmic microwave background (CMB) absolute temperature at low frequency. These signatures can be observed both in global (or monopole) signal and fluctuations from very large to small angular scales. The peculiar motion of an observer with respect to an ideal reference frame, at rest with respect to the CMB, produces boosting effects in several observable quantities. They are remarkable in the anisotropy patterns at low multipoles, particularly in the dipole, with frequency spectral behaviours depending on the spectrum of the monopole emission, as previously studied in the context of CMB spectral distortions. We present here a novel investigation of this effect at radio frequencies, aimed at predicting the imprints expected in the redshifted 21 cm line signal and in the diffuse free-free emission plus the Comptonization distortion for several representative models. Furthermore, we consider the same type of signal, but as expected from the cosmological (CMB plus potential astrophysical signals) radio background determining the offset for 21 cm redshifted line. The combination of the four types of signal and their different relevance in the various frequency ranges is studied. This approach of linking monopole and anisotropy analyses, can be applied on all-sky or relatively wide sky coverage surveys as well as to a suitable set of sky patches. By relying only on the quality of interfrequency and relative data calibration, the approach in principle by-passes the need for precise absolute calibration, which is a critical point of current and future radio interferometric facilities.
A Parametric Bootstrap for the Mean Measure of Divergence

For more than 50 years the Mean Measure of Divergence (MMD) has been one of the most prominent tools used in anthropology for the study of non-metric traits. However, one of the problems, in anthropology including palaeoanthropology (more often there), is the lack of big enough samples or the existence of samples without sufficiently measured traits. Since 1969, with the advent of bootstrapping techniques, this issue has been tackled successfully in many different ways. Here, we present a parametric bootstrap technique based on the fact that the transformed θ, obtained from the Anscombe transformation to stabilize the variance, nearly follows a normal distribution with standard deviation $\sigma = 1 / \sqrt{N + 1/2}$ σ = 1 / N + 1 / 2 , where N is the size of the measured trait. When the probabilistic distribution is known, parametric procedures offer more powerful results than non-parametric ones. We profit from knowing the probabilistic distribution of θ to develop a parametric bootstrapping method. We explain it carefully with mathematical support. We give examples, both with artificial data and with real ones. Our results show that this parametric bootstrap procedure is a powerful tool to study samples with scarcity of data.
DirectPET: full-size neural network PET reconstruction from sinogram data

Neural network image reconstruction directly from measurement data is a relatively new field of research, which until now has been limited to producing small single-slice images (e.g., 1  ×  128  ×  128). We proposed a more efficient network design for positron emission tomography called DirectPET, which is capable of reconstructing multislice image volumes (i.e., 16  ×  400  ×  400) from sinograms.  Large-scale direct neural network reconstruction is accomplished by addressing the associated memory space challenge through the introduction of a specially designed Radon inversion layer. Using patient data, we compare the proposed method to the benchmark ordered subsets expectation maximization (OSEM) algorithm using signal-to-noise ratio, bias, mean absolute error, and structural similarity measures. In addition, line profiles and full-width half-maximum measurements are provided for a sample of lesions.  DirectPET is shown capable of producing images that are quantitatively and qualitatively similar to the OSEM target images in a fraction of the time. We also report on an experiment where DirectPET is trained to map low-count raw data to normal count target images, demonstrating the method’s ability to maintain image quality under a low-dose scenario.  The ability of DirectPET to quickly reconstruct high-quality, multislice image volumes suggests potential clinical viability of the method. However, design parameters and performance boundaries need to be fully established before adoption can be considered.
AI for Earth: Rainforest Conservation by Acoustic Surveillance

Saving rainforests is a key to halting adverse climate changes. In this paper, we introduce an innovative solution built on acoustic surveillance and machine learning technologies to help rainforest conservation. In particular, We propose new convolutional neural network (CNN) models for environmental sound classification and achieved promising preliminary results on two datasets, including a public audio dataset and our real rainforest sound dataset. The proposed audio classification models can be easily extended in an automated machine learning paradigm and integrated in cloud-based services for real world deployment.
The High-Energy Frontier of the Intensity Frontier: Closing the Dark Photon, Inelastic Dark Matter, and Muon g-2 Windows.

We study hidden sector and long-lived particles at past (CHARM and NuCal), present (NA62 and SeaQuest/DarkQuest), and future (LongQuest) experiments that are at the high-energy frontier of the intensity frontier. We focus on exploring the minimal vector portal and variere-lifetime particles (VLP). VLP models have mostly been devised to explain experimental anomalies while avoiding existing constraints, and we demonstrate that proton fixed-target experiments provide one of the most powerful probes for the sub-GeV to few GeV mass range of the VLP models, using inelastic dark matter (iDM) as an example. We consider an iDM model with small mass splitting that yields the observed dark matter (DM) relic abundance, and a scenario with a sizable mass splitting that can also explain the muon $g-2$ anomaly. We set strong limits based on the CHARM and NuCal experiments, which come close to excluding iDM as full-abundance thermal DM candidates in the MeV to GeV mass range, for the mass arrangements and small mass splittings we consider. We also study the future projections based on NA62 and SeaQuest/DarkQuest, and update the constraints of the minimal dark photon parameter space. We found that NuCal sets the only existing constraint in $\epsilon \sim 10^{-8} - 10^{-4}$ regime reaching $\sim$ 800 MeV in dark photon mass due to the resonant enhancement of the proton bremsstrahlung production. Finally, we propose LongQuest, a three-stage thorough retool of the SeaQuest experiment with short ($\lesssim$ 5 m), medium ($\sim$ 5 m), and long baseline ($\gtrsim$ 35 m) tracking stations/detectors, as a multi-purpose machine to explore dark sector particles with a wide range of couplings to the standard model sector.
Particle acceleration in a nearby galaxy cluster pair: the role of cluster dynamics

Diffuse radio emission associated with the intracluster medium (ICM) is observed in a number of merging galaxy clusters. It is currently believed that a fraction of the kinetic energy in mergers is channeled into nonthermal components, such as turbulence, cosmic rays, and magnetic fields, which may lead to the formation of giant synchrotron sources in the ICM.
Aims. Studying merging galaxy clusters in different evolutionary phases is fundamental for understanding the origin of radio emission in the ICM.
Methods. We observed the nearby galaxy cluster pair RXC J1825.3+3026 (z ∼ 0.065) and CIZA J1824.1+3029 (z ∼ 0.071) at 120 − 168 MHz with the LOw Frequency ARray (LOFAR) and made use of a deep (240 ks) XMM-Newton dataset to study the nonthermal and thermal properties of the system. RXC J1825.3+3026 is in a complex dynamical state, with a primary ongoing merger in the E-W direction and a secondary later stage merger with a group of galaxies in the SW, while CIZA J1824.1+3029 is dynamically relaxed. These two clusters are in a pre-merger phase.
Results. We report the discovery of a Mpc-scale radio halo with a low surface brightness extension in RXC J1825.3+3026 that follows the X-ray emission from the cluster center to the remnant of a galaxy group in the SW. This is among the least massive systems and the faintest giant radio halo known to date. In contrast to this, no diffuse radio emission is observed in CIZA J1824.1+3029, nor in the region between the pre-merger cluster pair. The power spectra of the X-ray surface brightness fluctuations of RXC J1825.3+3026 and CIZA J1824.1+3029 are in agreement with the findings for clusters exhibiting a radio halo and clusters where no radio emission has been detected, respectively.
 We provide quantitative support to the idea that cluster mergers play a crucial role in the generation of nonthermal components in the ICM.
Origin Of Tidal Structures In Modified Gravity.

The missing mass problem has not been solved decisively yet. Observations show that if gravity is to be modified, then the MOND theory is its excellent approximation on galactic scales. MOND suggests an adjustments of the laws of physics in the limit of low accelerations. Comparative simulations of interacting galaxies in MOND and Newtonian gravity with dark matter revealed two principal differences: 1) galaxies can have close flybys without ending in mergers in MOND because of weaker dynamical friction, and 2) tidal dwarf galaxies form very easily in MOND. When this is combined with the fact that many interacting galaxies are observed at high redshift, we obtain a new perspective on tidal features: they are often formed by non-merging encounters and tidal disruptions of tidal dwarf galaxies. Here we present the results from our self-consistent MOND $N$-body simulation of a close flyby of two galaxies similar to the Milky Way. It turns out that most types of the structures that are traditionally assigned to galaxy mergers can be formed by non-merging encounters, including tidal arms, bridges, streams, shells, disk warps, thick disks, and most probably also disks of satellites. The success of MOND in explaining the dynamics of galaxies hints us that this way of formation of tidal structures should be considered seriously.
Non-relativistic susceptibility and a dark matter application

When thermal rate equations are derived for the evolution of slow variables, it is often practical to parametrize the right-hand side with chemical potentials. To close the system, the chemical potentials are subsequently re-expressed in terms of the slow variables, which involves the consideration of a “susceptibility”. Here we study a non-relativistic situation in which chemical potentials are large compared with the temperature, as is relevant for late-time pair annihilations in dark matter freeze-out. An order-of-magnitude estimate and a lattice simulation are presented for a susceptibility dominated by bound states of stop-like mediators. After this “calibration”, the formalism is applied to a model with Majorana singlet dark matter, confirming that masses up to the multi-TeV domain are viable in the presence of sufficient (though not beyond a limit) mass degeneracy in the dark sector.
Isometric tensor network representation of string-net liquids

Recently, a class of tensor networks called isometric tensor network states (isoTNS) was proposed which generalizes the canonical form of matrix product states to tensor networks in higher dimensions. While this ansatz allows for efficient numerical computations, it remained unclear which phases admit an isoTNS representation. In this work, we show that two-dimensional string-net liquids, which represent a wide variety of topological phases including discrete gauge theories, admit an exact isoTNS representation. We further show that the isometric form can be preserved after applying a finite-depth local quantum circuit. Taken together, these results show that long-range entanglement by itself is not an obstruction to isoTNS representation and suggest that all two-dimensional gapped phases with gappable edges admit an isoTNS representation.
Basilisk: Bayesian hierarchical inference of the galaxy–halo connection using satellite kinematics – I. Method and validation

We present a Bayesian hierarchical inference formalism (Basilisk) to constrain the galaxy–halo connection using satellite kinematics. Unlike traditional methods, Basilisk does not resort to stacking the kinematics of satellite galaxies in bins of central luminosity, and does not make use of summary statistics, such as satellite velocity dispersion. Rather, Basilisk leaves the data in its raw form and computes the corresponding likelihood. In addition, Basilisk can be applied to flux-limited, rather than volume-limited samples, greatly enhancing the quantity and dynamic range of the data. And finally, Basilisk is the only available method that simultaneously solves for halo mass and orbital anisotropy of the satellite galaxies, while properly accounting for scatter in the galaxy–halo connection. Basilisk uses the conditional luminosity function to model halo occupation statistics, and assumes that satellite galaxies are a relaxed tracer population of the host halo’s potential with kinematics that obey the spherical Jeans equation. We test and validate Basilisk using mocks of varying complexity, and demonstrate that it yields unbiased constraints on the galaxy–halo connection and at a precision that rivals galaxy–galaxy lensing. In particular, Basilisk accurately recovers the full PDF of the relation between halo mass and central galaxy luminosity, and simultaneously constrains the orbital anisotropy of the satellite galaxies. Basilisk ’s inference is not affected by potential velocity bias of the central galaxies, or by slight errors in the inferred, radial profile of satellite galaxies that arise as a consequence of interlopers and sample impurity.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks

Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable modifications on graph topology or nodal features can significantly reduce the performances of GNNs. It is very challenging to design robust graph neural networks against poisoning attack and several efforts have been taken. Existing work aims at reducing the negative impact from adversarial edges only with the poisoned graph, which is sub-optimal since they fail to discriminate adversarial edges from normal ones. On the other hand, clean graphs from similar domains as the target poisoned graph are usually available in the real world. By perturbing these clean graphs, we create supervised knowledge to train the ability to detect adversarial edges so that the robustness of GNNs is elevated. However, such potential for clean graphs is neglected by existing work. To this end, we investigate a novel problem of improving the robustness of GNNs against poisoning attacks by exploring clean graphs. Specifically, we propose PA-GNN, which relies on a penalized aggregation mechanism that directly restrict the negative impact of adversarial edges by assigning them lower attention coefficients. To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph. Experimental results on four real-world datasets demonstrate the robustness of PA-GNN against poisoning attacks on graphs.
Reactive probabilistic programming

Synchronous modeling is at the heart of programming languages like Lustre, Esterel, or Scade used routinely for implementing safety critical control software, e.g., fly-by-wire and engine control in planes. However, to date these languages have had limited modern support for modeling uncertainty --- probabilistic aspects of the software's environment or behavior --- even though modeling uncertainty is a primary activity when designing a control system. In this paper we present ProbZelus the first synchronous probabilistic programming language. ProbZelus conservatively provides the facilities of a synchronous language to write control software, with probabilistic constructs to model uncertainties and perform inference-in-the-loop. We present the design and implementation of the language. We propose a measure-theoretic semantics of probabilistic stream functions and a simple type discipline to separate deterministic and probabilistic expressions. We demonstrate a semantics-preserving compilation into a first-order functional language that lends itself to a simple presentation of inference algorithms for streaming models. We also redesign the delayed sampling inference algorithm to provide efficient streaming inference. Together with an evaluation on several reactive applications, our results demonstrate that ProbZelus enables the design of reactive probabilistic applications and efficient, bounded memory inference.
Finding the right scale of a network: Efficient identification of causal emergence through spectral clustering

All networks can be analyzed at multiple scales. A higher scale of a network is made up of macro-nodes: subgraphs that have been grouped into individual nodes. Recasting a network at higher scales can have useful effects, such as decreasing the uncertainty in the movement of random walkers across the network while also decreasing the size of the network. However, the task of finding such a macroscale representation is computationally difficult, as the set of all possible scales of a network grows exponentially with the number of nodes. Here we compare various methods for finding the most informative scale of a network, discovering that an approach based on spectral analysis outperforms greedy and gradient descent-based methods. We then use this procedure to show how several structural properties of preferential attachment networks vary across scales. We describe how meso- and macroscale representations of networks can have significant benefits over their underlying microscale, which include properties such as increase in determinism, a decrease in degeneracy, a lower entropy rate of random walkers on the network, an increase in global network efficiency, and higher values for a variety of centrality measures than the microscale.