nils-herrmann/clean_scirepeval huggingface dataset:
Natural 
Z′
-portal Majorana dark matter in alternative U(1) extended standard model

We consider a non-exotic gauged U(1)$_X$ extension of the Standard Model (SM), where the U(1)$_X$ charge of a SM field is given by a linear combination of its hypercharge and Baryon-minus-Lepton ($B-L$) number. All the gauge and mixed gauge-gravitational anomalies are cancelled in this model with the introduction of three right-handed neutrinos (RHNs). Unlike the conventional minimal U(1)$_X$ model, where a universal U(1)$_X$ charge of $-1$ is assigned to three RHNs, we consider an alternative charge assignment, namely, two RHNs ($N_R^{1,2}$) have U(1)$_X$ charge $-4$ while one RHN ($N_R$) has a $+5$ charge. With a minimal extension of the Higgs sector, the three RHNs acquire their Majorana masses associated with U(1)$_X$ symmetry breaking. While $N_R^{1,2}$ have Yukawa coupling with the SM lepton doublets and play an essential role for the 'minimal seesaw' mechanism, $N_R$ is isolated from the SM particles due to its U(1)$_X$ charge and hence it is a natural candidate for the dark matter (DM) without invoking additional symmetries. In this model context, we investigate the $Z^\prime$-portal RHN DM scenario, where the RHN DM communicates with the SM particles through the U(1)$_X$ gauge boson ($Z^\prime$ boson). We identify a narrow parameter space by combining the constraints from the observed DM relic abundance, the results of the search for a $Z^\prime$ boson resonance at the Large Hadron Collider Run-2, and the gauge coupling perturbativity up to the Planck/Grand Unification scale. For a special choice of U(1)$_X$ charges for the SM fields allows us to extend the model to SU(5)$\times$U(1)$_X$ grand unification. In this scenario, the model parameter space is more severely constrained, which will be explored at future high energy collider experiments.
Visual SLAM with Network Uncertainty Informed Feature Selection

In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection is required such that reference points persist over long durations and the runtime and storage complexity of the algorithm remain consistent. We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates machine learning and neural network uncertainty into the feature selection pipeline. Our algorithm selects points which provide the highest reduction in Shannon entropy between the entropy of the current state, and the joint entropy of the state given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. This feature selection strategy generates a sparse map suitable for long-term localization, as each selected feature significantly reduces the uncertainty of the vehicle state and has been detected to be a static object (building, traffic sign, etc.) repeatedly with a high confidence. The KITTI odometry dataset is used to evaluate our method, and we also compare our results against ORB_SLAM2. Overall, SIVO performs comparably to ORB_SLAM2 (average of 0.17% translation error difference, 6.2 x 10^(-5) deg/m rotation error difference) while reducing the map size by 69%.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on "Stylized-ImageNet", a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation.
Quantum Gravity on the Computer: Impressions of a Workshop

Computer simulations allow us to explore non-perturbative phenomena in physics. This has the potential to help us understand quantum gravity. Finding a theory of quantum gravity is a hard problem, but, in the last several decades, many promising and intriguing approaches that utilize or might benefit from using numerical methods were developed. These approaches are based on very different ideas and assumptions, yet they face the common challenge to derive predictions and compare them to data. In March 2018, we held a workshop at the Nordic Institute for Theoretical Physics (NORDITA) in Stockholm gathering experts in many different approaches to quantum gravity for a workshop on “Quantum gravity on the computer”. In this article, we try to encapsulate some of the discussions held and talks given during this workshop and combine them with our own thoughts on why and how numerical approaches will play an important role in pushing quantum gravity forward. The last section of the article is a road map providing an outlook of the field and some intentions and goalposts that were debated in the closing session of the workshop. We hope that it will help to build a strong numerical community reaching beyond single approaches to combine our efforts in the search for quantum gravity.
Phat ELVIS: The inevitable effect of the Milky Way’s disc on its dark matter subhaloes

We introduce an extension of the ELVIS project to account for the effects of the Milky Way galaxy on its subhalo population. Our simulation suite, Phat ELVIS, consists of 12 high-resolution cosmological dark matter-only (DMO) zoom simulations of Milky Way-size ΛCDM haloes [Mv = (0.7−2) × 1012 M⊙] along with 12 re-runs with embedded galaxy potentials grown to match the observed Milky Way disc and bulge today. The central galaxy potential destroys subhalos on orbits with small pericentres in every halo, regardless of the ratio of galaxy mass to halo mass. This has several important implications. (1) Most of the Disc runs have no subhaloes larger than Vmax = 4.5 km s−1 within 20 kpc and a significant lack of substructure going back ∼8 Gyr, suggesting that local stream-heating signals from dark substructure will be rare. (2) The pericentre distributions of Milky Way satellites derived from Gaia data are remarkably similar to the pericentre distributions of subhaloes in the Disc runs, while the DMO runs drastically overpredict galaxies with pericentres smaller than 20 kpc. (3) The enhanced destruction produces a tension opposite to that of the classic ‘missing satellites’ problem: in order to account for ultra-faint galaxies known within 30 kpc of the Galaxy, we must populate haloes with Vpeak ≃ 7 km s−1 (M ≃ 3 × 107 M⊙ at infall), well below the atomic cooling limit of $V_\mathrm{peak}\simeq 16 \,{\rm km} \, {\rm s}^{-1}$ (M ≃ 5 × 108M⊙ at infall). (4) If such tiny haloes do host ultra-faint dwarfs, this implies the existence of ∼1000 satellite galaxies within 300 kpc of the Milky Way.
Major mergers between dark matter haloes – I. Predictions for size, shape, and spin

The structural properties of individual dark matter haloes, including shape, spin, concentration, and substructure, are linked to the halo's growth history, but the exact connection between the two is unclear. One open question, in particular, is the effect of major mergers on halo structure. We have performed a large set of simulations of binary equal-mass mergers between isolated haloes with various density profiles, to map out the relationship between the initial conditions and merger parameters and the structure of the final remnant. In this paper we describe our initial set-up and analysis methods, and report on the results for the size, shape, and spin of the merger remnant. The outcomes of mergers are most easily understood in terms of a scaled dimensionless energy parameter $\kappa$ and an angular momentum (or spin) parameter $\lambda$. We find that the axis ratio $c/a$ scales roughly linearly with energy $\kappa$ while the axis ratio $c/b$ scales linearly with spin $\lambda$. Qualitatively, mergers on radial orbits produce prolate remnants, while mergers on tangential orbits produce oblate remnants. The spin of the remnant can be predicted from angular momentum conservation, while the overall size changes as $\sim \kappa^{-5}$, as expected from self-similar scaling at constant mean density. We discuss potential cosmological applications for these simple patterns.
GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs

We present the results from three gravitational-wave searches for coalescing compact binaries with component masses above 1$\mathrm{M}_\odot$ during the first and second observing runs of the Advanced gravitational-wave detector network. During the first observing run (O1), from September $12^\mathrm{th}$, 2015 to January $19^\mathrm{th}$, 2016, gravitational waves from three binary black hole mergers were detected. The second observing run (O2), which ran from November $30^\mathrm{th}$, 2016 to August $25^\mathrm{th}$, 2017, saw the first detection of gravitational waves from a binary neutron star inspiral, in addition to the observation of gravitational waves from a total of seven binary black hole mergers, four of which we report here for the first time: GW170729, GW170809, GW170818 and GW170823. For all significant gravitational-wave events, we provide estimates of the source properties. The detected binary black holes have total masses between $18.6_{-0.7}^{+3.2}\mathrm{M}_\odot$, and $84.4_{-11.1}^{+15.8} \mathrm{M}_\odot$, and range in distance between $320_{-110}^{+120}$ Mpc and $2840_{-1360}^{+1400}$ Mpc. No neutron star - black hole mergers were detected. In addition to highly significant gravitational-wave events, we also provide a list of marginal event candidates with an estimated false alarm rate less than 1 per 30 days. From these results over the first two observing runs, which include approximately one gravitational-wave detection per 15 days of data searched, we infer merger rates at the 90% confidence intervals of $110\, -\, 3840$ $\mathrm{Gpc}^{-3}\,\mathrm{y}^{-1}$ for binary neutron stars and $9.7\, -\, 101$ $\mathrm{Gpc}^{-3}\,\mathrm{y}^{-1}$ for binary black holes assuming fixed population distributions, and determine a neutron star - black hole merger rate 90% upper limit of $610$ $\mathrm{Gpc}^{-3}\,\mathrm{y}^{-1}$.
Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition

Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior. In laparoscopic procedures one particular algorithm needed for such systems is the identification of surgical phases, for which the current state of the art is a model based on a CNN-LSTM. A number of previous works using models of this kind have trained them in a fully supervised manner, requiring a fully annotated dataset. Instead, our work confronts the problem of learning surgical phase recognition in scenarios presenting scarce amounts of annotated data (under 25% of all available video recordings). We propose a teacher/student type of approach, where a strong predictor called the teacher, trained beforehand on a small dataset of ground truth-annotated videos, generates synthetic annotations for a larger dataset, which another model - the student - learns from. In our case, the teacher features a novel CNN-biLSTM-CRF architecture, designed for offline inference only. The student, on the other hand, is a CNN-LSTM capable of making real-time predictions. Results for various amounts of manually annotated videos demonstrate the superiority of the new CNN-biLSTM-CRF predictor as well as improved performance from the CNN-LSTM trained using synthetic labels generated for unannotated videos. For both offline and online surgical phase recognition with very few annotated recordings available, this new teacher/student strategy provides a valuable performance improvement by efficiently leveraging the unannotated data.
NTX: An Energy-efficient Streaming Accelerator for Floating-point Generalized Reduction Workloads in 22 nm FD-SOI

Specialized coprocessors for Multiply-Accumulate (MAC) intensive workloads such as Deep Learning are becoming widespread in SoC platforms, from GPUs to mobile SoCs. In this paper we revisit NTX (an efficient accelerator developed for training Deep Neural Networks at scale) as a generalized MAC and reduction streaming engine. The architecture consists of a set of 32 bit floating-point streaming co-processors that are loosely coupled to a RISC-V core in charge of orchestrating data movement and computation. Post-layout results of a recent silicon implementation in 22 nm FD-SOI technology show the accelerator’s capability to deliver up to 20 Gflop/s at 1.25 GHz and 168 mW. Based on these results we show that a version of NTX scaled down to 14 nm can achieve a 3× energy efficiency improvement over contemporary GPUs at 10.4× less silicon area, and a compute performance of 1.4 Tflop/s for training large state-of-the-art networks with full floating-point precision. An extended evaluation of MAC-intensive kernels shows that NTX can consistently achieve up to 87% of its peak performance across general reduction workloads beyond machine learning. Its modular architecture enables deployment at different scales ranging from high-performance GPU-class to low-power embedded scenarios.
Discovering hierarchies using Imitation Learning from hierarchy aware policies

Learning options that allow agents to exhibit temporally higher order behavior has proven to be useful in increasing exploration, reducing sample complexity and for various transfer scenarios. Deep Discovery of Options (DDO) is a generative algorithm that learns a hierarchical policy along with options directly from expert trajectories. We perform a qualitative and quantitative analysis of options inferred from DDO in different domains. To this end, we suggest different value metrics like option termination condition, hinge value function error and KL-Divergence based distance metric to compare different methods. Analyzing the termination condition of the options and number of time steps the options were run revealed that the options were terminating prematurely. We suggest modifications which can be incorporated easily and alleviates the problem of shorter options and a collapse of options to the same mode.
Asynchronous Spatial Image Convolutions for Event Cameras

Spatial convolution is arguably the most fundamental of two-dimensional image processing operations. Conventional spatial image convolution can only be applied to a conventional image, that is, an array of pixel values (or similar image representation) that are associated with a single instant in time. Event cameras have serial, asynchronous output with no natural notion of an image frame, and each event arrives with a different timestamp. In this letter, we propose a method to compute the convolution of a linear spatial kernel with the output of an event camera. The approach operates on the event stream output of the camera directly without synthesising pseudoimage frames as is common in the literature. The key idea is the introduction of an internal state that directly encodes the convolved image information, which is updated asynchronously as each event arrives from the camera. The state can be read off as often as and whenever required for use in higher level vision algorithms for real-time robotic systems. We demonstrate the application of our method to corner detection, providing an implementation of a Harris corner-response “state” that can be used in real time for feature detection and tracking on robotic systems.
Proceedings of the fourth "international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques" (iTWIST'18)

The iTWIST workshop series aim at fostering collaboration between international scientific teams for developing new theories, applications and generalizations of low-complexity models. These events emphasize dissemination of ideas through both specific oral and poster presentations, as well as free discussions. For this fourth edition, iTWIST'18 gathered in CIRM, Marseille, France, 74 international participants and featured 7 invited talks, 16 oral presentations, and 21 posters. 
From iTWIST'18, the scientific committee has decided that the workshop proceedings will adopt the episcience.org philosophy, combined with arXiv.org: in a nutshell, "the proceedings are equivalent to an overlay page, built above arXiv.org; they add value to these archives by attaching a scientific caution to the validated papers." 
This means that all papers listed in the HTML page of this arxiv publication (see the menu on the right) have been thoroughly evaluated and approved by two independent reviewers, and authors have revised their work according to the comments provided by these reviewers.
Joint Mapping and Calibration via Differentiable Sensor Fusion

We leverage automatic differentiation (AD) and probabilistic programming to develop an end-to-end optimization algorithm for batch triangulation of a large number of unknown objects. Given noisy detections extracted from noisily geo-located street level imagery without depth information, we jointly estimate the number and location of objects of different types, together with parameters for sensor noise characteristics and prior distribution of objects conditioned on side information. The entire algorithm is framed as nested stochastic variational inference. An inner loop solves a soft data association problem via loopy belief propagation; a middle loop performs soft EM clustering using a regularized Newton solver (leveraging an AD framework); an outer loop backpropagates through the inner loops to train global parameters. We place priors over sensor parameters for different traffic object types, and demonstrate improvements with richer priors incorporating knowledge of the environment. 
We test our algorithm on detections of road signs observed by cars with mounted cameras, though in practice this technique can be used for any geo-tagged images. The detections were extracted by neural image detectors and classifiers, and we independently triangulate each type of sign (e.g. stop, traffic light). We find that our model is more robust to DNN misclassifications than current methods, generalizes across sign types, and can use geometric information to increase precision. Our algorithm outperforms our current production baseline based on k-means clustering. We show that variational inference training allows generalization by learning sign-specific parameters.
Effect of non-equilibrium ionization on derived physical conditions of the high-z intergalactic medium

Non-equilibrium ionization effects are important in cosmological hydrodynamical simulations but are computationally expensive. We study the effect of non-equilibrium ionization evolution and UV ionizing background (UVB) generated with different quasar spectral energy distribution (SED) on the derived physical conditions of the intergalactic medium at 2 ≤ z ≤ 6 using our post-processing tool ‘Code for Ionization and Temperature Evolution’ (cite). cite produces results matching well with self-consistent simulations more efficiently. The  He ii reionization progresses more rapidly in non-equilibrium model compared to equilibrium models. The redshift of  He ii reionization strongly depends on the quasar SED and occurs earlier for UVB models with flatter quasar SEDs. During this epoch, the normalization of temperature–density relation, T0(z), has a maximum while the slope, γ(z), has a minimum, but occurring at different redshifts. The T0 is higher in non-equilibrium models using UVB obtained with flatter quasar SEDs. While our models produce the observed median  He ii effective optical depth evolution and its scatter for equilibrium and non-equilibrium considerations, to explain the observed cumulative distributions we may need to consider fluctuating UVB. For a given UVB model, the redshift dependence of the  H i photoionization rate derived from the observed  H i effective optical depth (τeff, H i) for the equilibrium model is different from that for the non-equilibrium model. This may lead to different requirements on the evolution of ionizing emissivities of sources. We show that, in the absence of strong differential pressure smoothing effects, it is possible to recover the T0 and γ realized in non-equilibrium model from the equilibrium models generated by rescaling photoheating rates while producing the same τeff, H i.
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.
Prototype-based Neural Network Layers: Incorporating Vector Quantization

Neural networks currently dominate the machine learning community and they do so for good reasons. Their accuracy on complex tasks such as image classification is unrivaled at the moment and with recent improvements they are reasonably easy to train. Nevertheless, neural networks are lacking robustness and interpretability. Prototype-based vector quantization methods on the other hand are known for being robust and interpretable. For this reason, we propose techniques and strategies to merge both approaches. This contribution will particularly highlight the similarities between them and outline how to construct a prototype-based classification layer for multilayer networks. Additionally, we provide an alternative, prototype-based, approach to the classical convolution operation. Numerical results are not part of this report, instead the focus lays on establishing a strong theoretical framework. By publishing our framework and the respective theoretical considerations and justifications before finalizing our numerical experiments we hope to jump-start the incorporation of prototype-based learning in neural networks and vice versa.