nils-herrmann/clean_scirepeval huggingface dataset:
MIN: Co-Governing Multi-Identifier Network Architecture and Its Prototype on Operator’s Network

IP protocol is the core of TCP/IP network layer. However, since IP address and its Domain Name are allocated and managed by a single agency, there are risks of centralization. The semantic overload of IP address also reduces its scalability and mobility, which further hinders the security. This paper proposes a co-governing Multi-Identifier Network (MIN) architecture that constructs a network layer with parallel coexistence of multiple identifiers, including identity, content, geographic information, and IP address. On the management plane, we develop an efficient management system using consortium blockchain with voting consensus, so the network can simultaneously manage and support by hundreds or thousands of nodes with high throughput. On the data plane, we propose an algorithm merging hash table and prefix tree (HTP) for FIB, which avoids the false-negative error and can inter-translate different identifiers with tens of billions of entries. Further, we propose a scheme to transport IP packets using CCN as a tunnel for supporting progressive deployment. We deployed the prototype of MIN to the largest operators’ network in Mainland China, Hongkong and Macao, and demonstrated that the network can register identifier under co-governing consensus algorithm, support VoD service very well.
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization

This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, designed for global optimization of expensive functions where concurrent function evaluations are useful. POAP consists of three components: a worker pool capable of function evaluations, strategies to propose evaluations or other actions, and a controller that mediates the interaction between the workers and strategies. pySOT is a collection of synchronous and asynchronous surrogate optimization strategies, implemented in the POAP framework. We support the stochastic RBF method by Regis and Shoemaker along with various extensions of this method, and a general surrogate optimization strategy that covers most Bayesian optimization methods. We have implemented many different surrogate models, experimental designs, acquisition functions, and a large set of test problems. We make an extensive comparison between synchronous and asynchronous parallelism and find that the advantage of asynchronous computation increases as the variance of the evaluation time or number of processors increases. We observe a close to linear speed-up with 4, 8, and 16 processors in both the synchronous and asynchronous setting.
Geometry and analysis of the Yang-Mills-Higgs-Dirac model.

The harmonic sections of the Kaluza-Klein model can be seen as a variant of harmonic maps with additional gauge symmetry. Geometrically, they are realized as sections of a fiber bundle associated to a principal bundle with a connection. In this paper, we investigate geometric and analytic aspects of a model that combines the Kaluza-Klein model with the Yang-Mills action and a Dirac action for twisted spinors. In dimension two we show that weak solutions of the Euler-Lagrange system are smooth. For a sequence of approximate solutions on surfaces with uniformly bounded energies we obtain compactness modulo bubbles, namely, energy identities and the no-neck property hold.
The Betti side of the double shuffle theory. III. Bitorsor structures

In the two first parts of the series, we constructed stabilizer subtorsors of a `twisted Magnus' torsor, studied their relations with the associator and double shuffle torsors, and explained their `de Rham' nature. In this paper, we make the associated bitorsor structures explicit and explain the `Betti' nature of the corresponding right torsors; we thereby complete one aim of the series. We study the discrete and pro-p versions of the `Betti' group of the double shuffle bitorsor.
Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data

Many common sequential data sources, such as source code and natural language, have a natural tree-structured representation. These trees can be generated by fitting a sequence to a grammar, yielding a hierarchical ordering of the tokens in the sequence. This structure encodes a high degree of syntactic information, making it ideal for problems such as grammar correction. However, little work has been done to develop neural networks that can operate on and exploit tree-structured data. In this paper we present the Tree-Transformer \textemdash{} a novel neural network architecture designed to translate between arbitrary input and output trees. We applied this architecture to correction tasks in both the source code and natural language domains. On source code, our model achieved an improvement of $25\%$ $\text{F}0.5$ over the best sequential method. On natural language, we achieved comparable results to the most complex state of the art systems, obtaining a $10\%$ improvement in recall on the CoNLL 2014 benchmark and the highest to date $\text{F}0.5$ score on the AESW benchmark of $50.43$.
On the existence of paradoxical motions of generically rigid graphs on the sphere

We interpret realizations of a graph on the sphere up to rotations as elements of a moduli space of curves of genus zero. We focus on those graphs that admit an assignment of edge lengths on the sphere resulting in a flexible object. Our interpretation of realizations allows us to provide a combinatorial characterization of these graphs in terms of the existence of particular colorings of the edges. Moreover, we determine necessary relations for flexibility between the spherical lengths of the edges. We conclude by classifying all possible motions on the sphere of the complete bipartite graph with $3+3$ vertices where no two vertices coincide or are antipodal.
Cross-context look at upper-division student difficulties with integration

We investigate upper-division student difficulties with direct integration in multiple contexts involving the calculation of a potential from a continuous distribution (e.g., mass, charge, or current). Integration is a tool that has been historically studied at several different points in the curriculum including introductory and upper-division levels. We build off of these prior studies and contribute additional data around student difficulties with multi-variable integration at two new points in the curriculum: middle-division classical mechanics, and upper-division magnetostatics. To facilitate comparisons across prior studies as well as the current work, we utilize an analytical framework that focuses on how students activate, construct, execute, and reflect on mathematical tools during physics problem solving (i.e., the ACER framework). Using a mixed-methods approach involving coded exam solutions and student problem-solving interviews, we identify and compare students' difficulties in these two different context and relate them to what has been found previously in other levels and contexts. We find that some of the observed student difficulties were persistent accross all three contexts (e.g., identifying integration as the appropriate tool, and expressing the difference vector), while other difficulties seemed to fade as students advanced through the curriculum (e.g., expressing differential line, area, and volume elements). We also identified new difficulties that appear in different contexts (e.g., interpreting and expressing the current density).
Learning Joint Acoustic-Phonetic Word Embeddings

Most speech recognition tasks pertain to mapping words across two modalities: acoustic and orthographic. In this work, we suggest learning encoders that map variable-length, acoustic or phonetic, sequences that represent words into fixed-dimensional vectors in a shared latent space; such that the distance between two word vectors represents how closely the two words sound. Instead of directly learning the distances between word vectors, we employ weak supervision and model a binary classification task to predict whether two inputs, one of each modality, represent the same word given a distance threshold. We explore various deep-learning models, bimodal contrastive losses, and techniques for mining hard negative examples such as the semi-supervised technique of self-labeling. Our best model achieves an $F_1$ score of 0.95 for the binary classification task.
Measurement of the ionization yield from nuclear recoils in liquid xenon between 0.3 -- 6 keV with single-ionization-electron sensitivity

Dual-phase xenon TPC detectors are a highly scalable and widely used technology to search for low-energy nuclear recoil signals from WIMP dark matter or coherent nuclear scattering of $\sim$MeV neutrinos. Such experiments expect to measure O(keV) ionization or scintillation signals from such sources. However, at $\sim1\,$keV and below, the signal calibrations in liquid xenon carry large uncertainties that directly impact the assumed sensitivity of existing and future experiments. In this work, we report a new measurement of the ionization yield of nuclear recoil signals in liquid xenon down to 0.3$\,$keV$\,\,$-- the lowest energy calibration reported to date -- at which energy the average event produces just 1.1~ionized~electrons. Between 2 and 6$\,$keV, our measurements agree with existing measurements, but significantly improve the precision. At lower energies, we observe a decreasing trend that deviates from simple extrapolations of existing data. We also study the dependence of ionization yield on the applied drift field in liquid xenon between 220V/cm and 6240V/cm, allowing these measurements to apply to a broad range of current and proposed experiments with different operating parameters.
Measuring the Clustering Strength of a Network via the Normalized Clustering Coefficient

In this paper, we propose a novel statistic of networks, the normalized clustering coefficient, which is a modified version of the clustering coefficient that is robust to network size, network density and degree heterogeneity under different network generative models. In particular, under the degree corrected block model (DCBM), the "in-out-ratio" could be inferred from the normalized clustering coefficient. Asymptotic properties of the proposed indicator are studied under three popular network generative models. The normalized clustering coefficient can also be used for networks clustering, network sampling as well as dynamic network analysis. Simulations and real data analysis are carried out to demonstrate these applications.
Deep Sensor Fusion for Real-Time Odometry Estimation

Cameras and 2D laser scanners, in combination, are able to provide low-cost, light-weight and accurate solutions, which make their fusion well-suited for many robot navigation tasks. However, correct data fusion depends on precise calibration of the rigid body transform between the sensors. In this paper we present the first framework that makes use of Convolutional Neural Networks (CNNs) for odometry estimation fusing 2D laser scanners and mono-cameras. The use of CNNs provides the tools to not only extract the features from the two sensors, but also to fuse and match them without needing a calibration between the sensors. We transform the odometry estimation into an ordinal classification problem in order to find accurate rotation and translation values between consecutive frames. Results on a real road dataset show that the fusion network runs in real-time and is able to improve the odometry estimation of a single sensor alone by learning how to fuse two different types of data information.
Anisotropies and non-Gaussianity of the cosmological gravitational wave background

The Stochastic Gravitational Wave Background (SGWB) is expected to be a key observable for Gravitational Wave (GW) interferometry. Its detection will open a new window on early universe cosmology and on the astrophysics of compact objects. Using a Boltzmann approach, we study the angular anisotropies of the GW energy density, which is an important tool to disentangle the different cosmological and astrophysical contributions to the SGWB. Anisotropies in the cosmological background are imprinted both at its production, and by GW propagation through the large-scale scalar and tensor perturbations of the universe. The first contribution is not present in the Cosmic Microwave Background (CMB) radiation (as the universe is not transparent to photons before recombination), causing an order one dependence of the anisotropies on frequency. Moreover, we provide a new method to characterize the cosmological SGWB through its possible deviation from a Gaussian statistics. In particular, the SGWB will become a new probe of the primordial non-Gaussianity of the large-scale cosmological perturbations.
Exact solutions by integrals of the non-stationary elliptic Calogero–Sutherland equation

We use generalized kernel functions to construct explicit solutions by integrals of the non-stationary Schrödinger equation for the Hamiltonian of the elliptic Calogero–Sutherland model (also known as elliptic Knizhnik–Zamolodchikov–Bernard equation). Our solutions provide integral representations of elliptic generalizations of the Jack polynomials.
How to make a giant bubble

Using mixtures of soap, water, and long chain polymers, free-floating soap bubbles can be formed with volumes approaching 100 m$^3$. Here we investigate how such thin films are created and maintained over time. We show how the extensional rheology is the most important factor in creating the bubble, and how polydispersity in molecular weight of the solvated polymers leads to better performance at lower concentrations. Additionally, using IR absorption, we measure soap film thickness profiles and film lifetimes. Although the initial thickness mostly depends on the choice of detergent, polymers can dramatically increase film lifetime at high molecular weights and high concentrations, although such high concentrations can inhibit the initial film formation. Thus, the ideal concentration of polymer additives for making giant bubbles requires a robust viscoelastic rheology during extension, and is aided by long film lifetimes during gravitational drainage and evaporation.
Indirect detection of composite asymmetric dark matter

Dark Matter can form bound states upon the emission of quanta of energy equal to the binding energy. The rate of this process is large for strongly-interacting Dark Matter, and further enhanced by long distance effects. The resulting monochromatic and diffuse $\gamma$-rays can be tested in indirect detection experiments. If Dark Matter has electroweak charge, indirect signals include multiple observable photon lines for masses in the TeV range. Else if it couples only via a dark photon portal, diffuse spectra from dwarf galaxies and CMB reionization set powerful limits for masses below a TeV. This mechanism provides a powerful means of probing Asymmetric Dark Matter today.
Deep learning dark matter map reconstructions from DES SV weak lensing data

We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network with a U-Net-based architecture on over 3.6 × 105 simulated data realizations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created dark energy survey science verification (DES SV) map as an approximation of the posterior mean P(κ|γ) of the convergence given observed shear. Our DeepMass1 method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realizations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean square error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering, with the optimal known power spectrum, still gives a worse MSE than our generalized method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.
A Markov Model for Non-lognormal Density Distributions in Compressive Isothermal Turbulence

Compressive isothermal turbulence is known to have a near lognormal density probability distribution function (PDF) with a width that scales with the sonic Mach number and nature of the turbulent driving (solenoidal versus compressive). However, the physical processes that mold the extreme high and low density structures in a turbulent medium can be different, with the densest structures being composed of strong shocks that evolve on shorter timescales than the low density fluid. The density PDF in a turbulent medium exhibits deviations from lognormal due to shocks, that increases with the sonic Mach number, which is often ignored in analytic models for turbulence and star formation. We develop a simple model for turbulence by treating it as a continuous Markov process, which explains both the density PDF and the transient timescales of structures as a function of density, using a framework developed in Scannapieco & Safarzadeh (). Our analytic model depends on only a single parameter, the effective compressive sonic Mach number, and successfully describes the non-lognormal behavior seen in both 1D and 3D simulations of supersonic and subsonic compressive isothermal turbulence. The model quantifies the non-lognormal distribution of density structures in turbulent environments, and has application to star-forming molecular clouds and star formation efficiencies.
LISA for cosmologists: Calculating the signal-to-noise ratio for stochastic and deterministic sources

We present the steps to forecast the sensitivity of the Laser Interferometer Space Antenna (LISA) to both a stochastic gravitational wave background and deterministic wave sources. We show how to use these expressions to estimate the precision with which LISA can determine parameters associated with these sources. Tools are included to enable easy calculation of the signal-to-noise ratio and draw sensitivity curves. Benchmark values are given for easy comparison and checking of methods in the case of two worked examples. The first benchmark is the threshold stochastic gravitational wave background $\Omega_{GW} h^2$ that LISA can observe. The second is the signal-to-noise ratio that LISA would observe for a binary black hole system identical to GW150914, radiating 4 years before merger.
How feedback shapes galaxies: an analytic model

We introduce a simple analytic model of galaxy formation that links the growth of dark matter haloes in a cosmological background to the build-up of stellar mass within them. The model aims to identify the physical processes that drive the galaxy-halo co-evolution through cosmic time. The model restricts the role of baryonic astrophysics to setting the relation between galaxies and their haloes. Using this approach, galaxy properties can be directly predicted from the growth of their host dark matter haloes. We explore models in which the effective star formation efficiency within haloes is a function of mass (or virial temperature) and independent of time. Despite its simplicity, the model reproduces self-consistently the shape and evolution of the cosmic star formation rate density, the specific star formation rate of galaxies, and the galaxy stellar mass function, both at the present time and at high redshifts. By systematically varying the effective star formation efficiency in the model, we explore the emergence of the characteristic shape of the galaxy stellar mass function. The origin of the observed double Schechter function at low redshifts is naturally explained by two efficiency regimes in the stellar to halo mass relation, namely, a stellar feedback regulated stage, and a supermassive black hole regulated stage. By providing a set of analytic differential equations, the model can be easily extended and inverted, allowing the roles and impact of astrophysics and cosmology to be explored and understood.
Stellar probes of dark sector-photon interactions

Electromagnetically neutral dark sector particles may directly couple to the photon through higher dimensional effective operators. Considering electric and magnetic dipole moment, anapole moment, and charge radius interactions, we derive constraints from stellar energy loss in the Sun, horizontal branch and red giant stars, as well as from cooling of the proto-neutron star of SN1987A. We provide the exact formula for in-medium photon-mediated pair production to leading order in the dark coupling, and compute the energy loss rates explicitly for the most important processes, including a careful discussion on resonances and potential double counting between the processes. Stringent limits for dark states with masses below $3\,$keV ($40\,$MeV) arise from red giant stars (SN1987A), implying an effective lower mass-scale of approximately $10^9\,$GeV ($10^7\,$GeV) for mass-dimension five, and $100\,$GeV ($2.5\,$TeV) for mass-dimension six operators as long as dark states stream freely; for the proto-neutron star, the trapping of dark states is also evaluated. Together with direct limits previously derived by us in Chu et al. (2018), this provides the first comprehensive overview of the viability of effective electromagnetic dark-state interactions below the GeV mass-scale.
Macroscopic dark matter constraints from bolide camera networks

Macroscopic dark matter (macros) are a broad class of alternative candidates to particle dark matter. These candidates would transfer energy primarily through elastic scattering, and this linear energy deposition would produce observable signals if a macro were to pass through the atmosphere. We produce constraints for low mass macros from the null observation of bolides formed by a passing macro, across two extensive networks of cameras built originally to observe meteorites. The parameter space that could be probed with planned upgrades to the existing array of cameras in one of these networks still currently in use, the Desert Fireball Network in Australia, is estimated.
Single-Particle Cryo-Electron Microscopy: Mathematical Theory, Computational Challenges, and Opportunities

In recent years, an abundance of new molecular structures have been elucidated using cryo-electron microscopy (cryo-EM), largely due to advances in hardware technology and data processing techniques. Owing to these exciting new developments, cryo-EM was selected by Nature Methods as the "Method of the Year 2015," and the Nobel Prize in Chemistry 2017 was awarded to three pioneers in the cryo-EM field: Jacques Dubochet, Joachim Frank, and Richard Henderson "for developing cryoelectron microscopy for the high-resolution structure determination of biomolecules in solution" .