nils-herrmann/clean_scirepeval huggingface dataset:
Automated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: A 3D Analysis of SPECT/CT Images

To meet the current need for skeletal tumor-load estimation in castration-resistant prostate cancer (CRPC), we developed a novel approach based on adaptive bone segmentation. In this study, we compared the program output with existing estimates and with the radiological outcome. Seventy-six whole-body single-photon emission computed tomographies/x-ray computed tomography with 3,3-diphosphono-1,2-propanedicarboxylic acid from mCRPC patients were analyzed. The software identified the whole skeletal volume (SVol) and classified the voxels metastases (MVol) or normal bone (BVol). SVol was compared with the estimation of a commercial software. MVol was compared with manual assessment and with prostate specific antigen (PSA) levels. Counts/voxel were extracted from MVol and BVol. After six cycles of 223RaCl2-therapy every patient was re-evaluated as having progressive disease (PD), stable disease (SD), or a partial response (PR). SVol correlated with that of the commercial software (R = 0.99, p < 0.001). MVol correlated with the manually-counted lesions (R = 0.61, p < 0.001) and PSA (R = 0.46, p < 0.01). PD had a lower counts/voxel in MVol than PR/SD (715 ± 190 vs. 975 ± 215 and 1058 ± 255, p < 0.05 and p < 0.01) and BVol (PD 275 ± 60, PR 515 ± 188 and SD 528 ± 162 counts/voxel, p < 0.001). Segmentation-based tumor load correlated with radiological/laboratory indices. Uptake was linked with the clinical outcome, suggesting that metastases in PD patients have a lower affinity for bone-seeking radionuclides and might benefit less from bone-targeted radioisotope therapies.
NGTS-7Ab: an ultrashort-period brown dwarf transiting a tidally locked and active M dwarf

We present the discovery of NGTS-7Ab, a high-mass brown dwarf transiting an M dwarf with a period of 16.2 h, discovered as part of the Next Generation Transit Survey (NGTS). This is the shortest period transiting brown dwarf around a main or pre-main sequence star to date. The M star host (NGTS-7A) has an age of roughly 55 Myr and is in a state of spin–orbit synchronization, which we attribute to tidal interaction with the brown dwarf acting to spin-up the star. The host star is magnetically active and shows multiple flares across the NGTS and follow-up light curves, which we use to probe the flare–star-spot phase relation. The host star also has an M star companion at a separation of 1.13 arcsec with very similar proper motion and systemic velocity, suggesting that the NGTS-7 system is a hierarchical triple. The combination of tidal synchronisation and magnetic braking is expected to drive on-going decay of the brown dwarf orbit, with a remaining lifetime of only 5–10 Myr.
Unsupervised State Representation Learning in Atari

State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods. The code associated with this work is available at this https URL
Evaluating Protein Transfer Learning with TAPE

Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques. To facilitate progress in this field, we introduce the Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. We curate tasks into specific training, validation, and test splits to ensure that each task tests biologically relevant generalization that transfers to real-life scenarios. We bench-mark a range of approaches to semi-supervised protein representation learning, which span recent work as well as canonical sequence learning techniques. We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases. Despite this increase, in several cases features learned by self-supervised pretraining still lag behind features extracted by state-of-the-art non-neural techniques. This gap in performance suggests a huge opportunity for innovative architecture design and improved modeling paradigms that better capture the signal in biological sequences. TAPE will help the machine learning community focus effort on scientifically relevant problems. Toward this end, all data and code used to run these experiments are available at https://github.com/songlab-cal/tape.
PyRobot: An Open-source Robotics Framework for Research and Benchmarking

This paper introduces PyRobot, an open-source robotics framework for research and benchmarking. PyRobot is a light-weight, high-level interface on top of ROS that provides a consistent set of hardware independent mid-level APIs to control different robots. PyRobot abstracts away details about low-level controllers and inter-process communication, and allows non-robotics researchers (ML, CV researchers) to focus on building high-level AI applications. PyRobot aims to provide a research ecosystem with convenient access to robotics datasets, algorithm implementations and models that can be used to quickly create a state-of-the-art baseline. We believe PyRobot, when paired up with low-cost robot platforms such as LoCoBot, will reduce the entry barrier into robotics, and democratize robotics. PyRobot is open-source, and can be accessed via this https URL.
Anionic nanoparticle-lipid membrane interactions: the protonation of anionic ligands at the membrane surface reduces membrane disruption

Monolayer-protected gold nanoparticles (Au NPs) are promising biomedical tools with applications in diagnosis and therapy, thanks to their biocompatibility and versatility. Here we show how the NP surface functionalization can drive the mechanism of interaction with lipid membranes. In particular, we show that the spontaneous protonation of anionic carboxylic groups on the NP surface can make the NP-membrane interaction faster and less disruptive.
Predicting Patent Citations to Measure Economic Impact of Scholarly Research

A crucial goal of funding research and development has always been to advance economic development. On this basis, a considerable body of research undertaken with the purpose of determining what exactly constitutes economic impact and how to accurately measure that impact has been published. Numerous indicators have been used to measure economic impact, although no single indicator has been widely adapted. Based on patent data collected from Altmetrics we predict patent citations through various social media features using several classifcation models. Patents citing a research paper implies the potential it has for direct application in its feld. These predictions can be utilized by researchers in determining the practical applications for their work when applying for patents
Divisive Normalization from Wilson-Cowan Dynamics

Divisive Normalization and the Wilson-Cowan equations are influential models of neural interaction and saturation [Carandini and Heeger ; Wilson and Cowan Kybernetik 1973]. However, they have not been analytically related yet. In this work we show that Divisive Normalization can be obtained from the Wilson-Cowan model. Specifically, assuming that Divisive Normalization is the steady state solution of the Wilson-Cowan differential equation, we find that the kernel that controls neural interactions in Divisive Normalization depends on the Wilson-Cowan kernel but also has a signal-dependent contribution. A standard stability analysis of a Wilson-Cowan model with the parameters obtained from our relation shows that the Divisive Normalization solution is a stable node. This stability demonstrates the consistency of our steady state assumption, and is in line with the straightforward use of Divisive Normalization with time-varying stimuli. 
The proposed theory provides a physiological foundation (a relation to a dynamical network with fixed wiring among neurons) for the functional suggestions that have been done on the need of signal-dependent Divisive Normalization [e.g. in Coen-Cagli et al.,  2012]. Moreover, this theory explains the modifications that had to be introduced ad-hoc in Gaussian kernels of Divisive Normalization in [Martinez et al.  2019] to reproduce contrast responses. The proposed relation implies that the Wilson-Cowan dynamics also reproduces visual masking and subjective image distortion metrics, which up to now had been mainly explained via Divisive Normalization. Finally, this relation allows to apply to Divisive Normalization the methods which up to now had been developed for dynamical systems such as Wilson-Cowan networks.
Training on test data: Removing near duplicates in Fashion-MNIST

MNIST and Fashion MNIST are extremely popular for testing in the machine learning space. Fashion MNIST improves on MNIST by introducing a harder problem, increasing the diversity of testing sets, and more accurately representing a modern computer vision task. In order to increase the data quality of FashionMNIST, this paper investigates near duplicate images between training and testing sets. Near-duplicates between testing and training sets artificially increase the testing accuracy of machine learning models. This paper identifies near-duplicate images in Fashion MNIST and proposes a dataset with near-duplicates removed.
Jaccard Filtration and Stable Paths in the Mapper

The contributions of this paper are two-fold. We define a new filtration called the cover filtration built from a single cover based on a generalized Jaccard distance. We provide stability results for the cover filtration and show how the construction is equivalent to the Cech filtration under certain settings. We then develop a language and theory for stable paths within this filtration, inspired by ideas of persistent homology. We demonstrate how the filtration and paths can be applied to a variety of applications in which defining a metric is not obvious but a cover is readily available. 
We demonstrate the usefulness of this construction by employing it in the context of recommendation systems and explainable machine learning. We demonstrate a new perspective for modeling recommendation system data sets that does not require manufacturing a bespoke metric. This extends work on graph-based recommendation systems, allowing a topological perspective. For an explicit example, we look at a movies data set and we find the stable paths identified in our framework represent a sequence of movies constituting a gentle transition and ordering from one genre to another. 
For explainable machine learning, we apply the Mapper for model induction, providing explanations in the form of paths between subpopulations or observations. Our framework provides an alternative way of building a filtration from a single mapper that is then used to explore stable paths. As a direct illustration, we build a mapper from a supervised machine learning model trained on the FashionMNIST data set. We show that the stable paths in the cover filtration provide improved explanations of relationships between subpopulations of images.
Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks

In this paper, we propose a novel framework for approximating the explicit MPC law for linear parameter-varying systems using supervised learning. In contrast to most existing approaches, we not only learn the control policy, but also a “certificate policy”, that allows us to estimate the sub-optimality of the learned control policy online, during execution-time. We learn both these policies from data using supervised learning techniques, and also provide a randomized method that allows us to guarantee the quality of each learned policy, measured in terms of feasibility and optimality. This in turn allows us to bound the probability of the learned control policy of being infeasible or suboptimal, where the check is performed by the certificate policy. Since our algorithm does not require the solution of an optimization problem during runtime, it can be deployed even on resource-constrained systems. We illustrate the efficacy of the proposed framework on a vehicle dynamics control problem where we demonstrate a speedup of up to two orders of magnitude compared to online optimization with minimal performance degradation.
Solver Recommendation For Transport Problems in Slabs Using Machine Learning

The use of machine learning algorithms to address classification problems is on the rise in many research areas. The current study is aimed at testing the potential of using such algorithms to auto-select the best solvers for transport problems in uniform slabs. Three solvers are used in this work: Richardson, diffusion synthetic acceleration, and nonlinear diffusion acceleration. Three parameters are manipulated to create different transport problem scenarios. Five machine learning algorithms are applied: linear discriminant analysis, K-nearest neighbors, support vector machine, random forest, and neural networks. We present and analyze the results of these algorithms for the test problems, showing that random forest and K-nearest neighbors are potentially the best suited candidates for this type of classification problem.
Tidal Destruction in a Low-mass Galaxy Environment: The Discovery of Tidal Tails around DDO 44

We report the discovery of a ≳1° (∼50 kpc) long stellar tidal stream emanating from the dwarf galaxy DDO 44, a likely satellite of Local Volume galaxy NGC 2403 located ∼70 kpc in projection from its companion. NGC 2403 is a roughly Large Magellanic Cloud (LMC) stellar-mass galaxy 3 Mpc away, residing at the outer limits of the M81 group. We are mapping a large region around NGC 2403 as part of our Magellanic Analogs’ Dwarf Companions and Stellar Halos survey, reaching point-source depths (90% completeness) of (g, i) = (26.5, 26.2). Density maps of old, metal-poor RGB stars reveal tidal streams extending on two sides of DDO 44, with the streams directed toward NGC 2403. We estimate total luminosities of the original DDO 44 system (dwarf and streams combined) to be Mi,tot = −13.4 and Mg,tot = −12.6, with ∼25%–30% of the luminosity in the streams. Analogs of ∼LMC-mass hosts with massive tidally disrupting satellites are rare in the Illustris simulations, especially at large separations such as that of DDO 44. The few analogs that are present in the models suggest that even low-mass hosts can efficiently quench their massive satellites.
H0
 tension, swampland conjectures, and the epoch of fading dark matter

Motivated by the swampland dS conjecture, we consider a rolling scalar field as the source of dark energy. Furthermore, the swampland distance conjecture suggests that the rolling field will lead at late times to an exponentially light tower of states. Identifying this tower as residing in the dark sector suggests a natural coupling of the scalar field to the dark matter, leading to a continually reducing dark matter mass as the scalar field rolls in the recent cosmological epoch. The exponent in the distance conjecture, $\tilde{c}$, is expected to be an $\mathcal{O}(1)$ number. Interestingly, when we include the local measurement of $H_0$, our model prefers a non-zero value of the coupling $\tilde{c}$ with a significance of $2.8\sigma$ and a best-fit at $\tilde{c} \sim 0.3$. Modifying the recent evolution of the universe in this way improves the fit to data at the $2\sigma$ level compared to $\Lambda$CDM. This string-inspired model automatically reduces cosmological tensions in the $H_0$ measurement as well as $\sigma_8$.
Cosmological Simulations of Satellites around Isolated Dwarf Galaxies

We trace the cosmological origin of satellites around a dwarf galaxy using a very high-resolution (12 pc/h) cosmological hydrodynamic zoom simulation. To realistically describe the formation and evolution of small-mass stellar satellites, our model includes a full baryonic physics treatment including a recipe for UV self-shielding. We find that the majority of their star-forming gas is accreted after reionization, thus the survival of a mini-halo gas to reionization is not an important factor. Instead, the key factor seems to be the ability for a mini-halo to cool its recently accreted gas, which is more efficient in more massive halos. We find that halos in denser environments suffer more mergers, enabling them to grow their mass such that cooling of accreted gas can occur efficiently. Although the host galaxy is only a dwarf galaxy itself, we find that ram pressure is an efficient means by which accreted mini-halos lose their gas content, both by interacting with hot halo gas but also in direct collisions with the gas disk of the host. The satellites are also disrupted by the tidal forces near the center of the host galaxy. In summary, our results suggest that the characteristics of satellites are mainly determined by their ability to efficiently cool gas that is accreted in the redshift range of z = 3–5, prior to their infall into the host galaxy.
A Simple Phenomenological Emergent Dark Energy Model can Resolve the Hubble Tension

Motivated by the current status of cosmological observations and significant tensions in the estimated values of some key parameters assuming the standard ΛCDM model, we propose a simple but radical phenomenological emergent dark energy model where dark energy has no effective presence in the past and emerges at later times. Theoretically, in this phenomenological dark energy model with zero degrees of freedom (similar to a ΛCDM model), one can derive that the equation of state of dark energy increases from in the past to −1 in the future. We show that by setting a hard-cut 2σ lower bound prior for H0 associated with a 97.72% probability from recent local observations, this model can satisfy different combinations of cosmological observations at low and high redshifts (SNe Ia, baryon acoustic oscillation (BAO), Lyα BAO, and cosmic microwave background (CMB)) substantially better than the concordance ΛCDM model with and Δ DIC ∼ −35.38. If there are no substantial systematics in SN Ia, BAO, or Planck CMB data, and assuming the reliability of current local H0 measurements, there is a very high probability that with more precise measurements of the Hubble constant our proposed phenomenological model rules out the cosmological constant with decisive statistical significance and is a strong alternative to explain the combination of different cosmological observations. This simple phenomenologically emergent dark energy model can guide theoretically motivated dark energy model building activities.
Non-LTE analysis of K I in late-type stars

Older models of Galactic chemical evolution (GCE) predict [K/Fe] ratios as much as 1 dex lower than those inferred from stellar observations. Abundances of potassium are mainly based on analyses of the 7698 Å resonance line, and the discrepancy between GCE models and observations is in part caused by the assumption of local thermodynamic equilibrium (LTE) in spectroscopic analyses.
Aims. We study the statistical equilibrium of K I, focusing on the non-LTE effects on the 7698 Å line. We aim to determine how non-LTE abundances of potassium can improve the analysis of its chemical evolution, and help to constrain the yields of GCE models.
Methods. We construct a new model K I atom that employs the most up-to-date atomic data. In particular, we calculate and present inelastic e+K collisional excitation cross-sections from the convergent close-coupling (CCC) and the B-Spline R-matrix (BSR) methods, and H+K collisions from the two-electron model (LCAO). We constructed a fine, extended grid of non-LTE abundance corrections based on 1D MARCS models that span 4000 < Teff∕K < 8000, 0.50 < log g < 5.00, − 5.00 < [Fe/H] < + 0.50, and applied the corrections to potassium abundances extracted from the literature.
Results. In concordance with previous studies, we find severe non-LTE effects in the 7698 Å line. The line is stronger in non-LTE and the abundance corrections can reach approximately − 0.7 dex for solar-metallicity stars such as Procyon. We determine potassium abundances in six benchmark stars, and obtain consistent results from different optical lines. We explore the effects of atmospheric inhomogeneity by computing for the first time a full 3D non-LTE stellar spectrum of K I lines for a test star. We find that 3D modeling is necessary to predict a correct shape of the resonance 7698 Å line, but the line strength is similar to that found in 1D non-LTE.
Conclusions. Our non-LTE abundance corrections reduce the scatter and change the cosmic trends of literature potassium abundances. In the regime [Fe/H] ≲−1.0 the non-LTE abundances show a good agreement with the GCE model with yields from rotating massive stars. The reduced scatter of the non-LTE corrected abundances of a sample of solar twins shows that line-by-line differential analysis techniques cannot fully compensate for systematic LTE modelling errors; the scatter introduced by such errors introduces a spurious dispersion to K evolution.
Minimum Stein Discrepancy Estimators

When maximum likelihood estimation is infeasible, one often turns to score matching, contrastive divergence, or minimum probability flow to obtain tractable parameter estimates. We provide a unifying perspective of these techniques as minimum Stein discrepancy estimators, and use this lens to design new diffusion kernel Stein discrepancy (DKSD) and diffusion score matching (DSM) estimators with complementary strengths. We establish the consistency, asymptotic normality, and robustness of DKSD and DSM estimators, then derive stochastic Riemannian gradient descent algorithms for their efficient optimisation. The main strength of our methodology is its flexibility, which allows us to design estimators with desirable properties for specific models at hand by carefully selecting a Stein discrepancy. We illustrate this advantage for several challenging problems for score matching, such as non-smooth, heavy-tailed or light-tailed densities.
Catalog of Chromium, Cobalt, and Nickel Abundances in Globular Clusters and Dwarf Galaxies

We present measurements of the abundances of chromium, cobalt, and nickel in 4113 red giants, including 2277 stars in globular clusters (GCs), 1820 stars in the Milky Way’s dwarf satellite galaxies, and 16 field stars. We measured the abundances from mostly archival Keck/DEIMOS medium-resolution spectroscopy with a resolving power of R ∼ 6500 and a wavelength range of approximately 6500–9000 Å. The abundances were determined by fitting spectral regions that contain absorption lines of the elements under consideration. We used estimates of temperature, surface gravity, and metallicity that we previously determined from the same spectra. We estimated systematic error by examining the dispersion of abundances within mono-metallic GCs. The median uncertainties for [Cr/Fe], [Co/Fe], and [Ni/Fe] are 0.20, 0.20, and 0.13, respectively. Finally, we validated our estimations of uncertainty through duplicate measurements, and we evaluated the accuracy and precision of our measurements through comparison to high-resolution spectroscopic measurements of the same stars.