nils-herrmann/clean_scirepeval huggingface dataset:
Backreaction on an infinite helical cosmic string

To understand the properties of a possible cosmic string network requires knowledge of the structures on long strings, which control the breaking off of smaller loops. These structures are influenced by backreaction due to gravitational wave emission. To gain an understanding of this process, we calculate the effect of gravitational backreaction on the "helical breather": an infinite cosmic string with a helical standing wave. We do the calculation analytically to first order in the amplitude of the oscillation. Our results for the rate of loss of length agree with the total power emitted from this system as calculated by Sakellariadou. We also find a rotation of the generators of the string that leads to an advancement of the phase of the oscillation in addition to that produced by the loss of length.
Crystallization for Coulomb and Riesz interactions as a consequence of the Cohn-Kumar conjecture

The Cohn-Kumar conjecture states that the triangular lattice in dimension 2, the 

 
 
 E
 8
 
 E_8
 

 lattice in dimension 8, and the Leech lattice in dimension 24 are universally minimizing in the sense that they minimize the total pair interaction energy of infinite point configurations for all completely monotone functions of the squared distance. This conjecture was recently proved by Cohn-Kumar-Miller-Radchenko-Viazovska in dimensions 8 and 24. We explain in this note how the conjecture implies the minimality of the same lattices for the Coulomb and Riesz renormalized energies as well as jellium and periodic jellium energies, hence settling the question of their minimization in dimensions 8 and 24.
Manifestations of Projection-Induced Memory: General Theory and the Tilted Single File

Over the years the field of non-Markovian stochastic processes and anomalous diffusion evolved from a specialized topic to mainstream theory, which transgressed the realms of physics to chemistry, biology and ecology. Numerous phenomenological approaches emerged, which can more or less successfully reproduce or account for experimental observations in condensed matter, biological and/or single-particle systems. However, as far as their predictions are concerned these approaches are not unique, often build on conceptually orthogonal ideas, and are typically employed on an ad hoc basis. It therefore seems timely and desirable to establish a systematic, mathematically unifying and clean approach starting from more fine-grained principles. Here we analyze projection-induced ergodic non-Markovian dynamics, both reversible as well as irreversible, using spectral theory. We investigate dynamical correlations between histories of projected and latent observables that give rise to memory in projected dynamics, and rigorously establish conditions under which projected dynamics is Markovian or renewal. A systematic metric is proposed for quantifying the degree of non-Markovianity. As a simple, illustrative but non-trivial example we study single file diffusion in a tilted box, which, for the first time, we solve exactly using the coordinate Bethe ansatz. Our results provide a solid foundation for a deeper and more systematic analysis of projection-induced non-Markovian dynamics and anomalous diffusion.
A Probabilistic Representation of Deep Learning

In this work, we introduce a novel probabilistic representation of deep learning, which provides an explicit explanation for the Deep Neural Networks (DNNs) in three aspects: (i) neurons define the energy of a Gibbs distribution; (ii) the hidden layers of DNNs formulate Gibbs distributions; and (iii) the whole architecture of DNNs can be interpreted as a Bayesian neural network. Based on the proposed probabilistic representation, we investigate two fundamental properties of deep learning: hierarchy and generalization. First, we explicitly formulate the hierarchy property from the Bayesian perspective, namely that some hidden layers formulate a prior distribution and the remaining layers formulate a likelihood distribution. Second, we demonstrate that DNNs have an explicit regularization by learning a prior distribution and the learning algorithm is one reason for decreasing the generalization ability of DNNs. Moreover, we clarify two empirical phenomena of DNNs that cannot be explained by traditional theories of generalization. Simulation results validate the proposed probabilistic representation and the insights into these properties of deep learning based on a synthetic dataset.
Once for All: Train One Network and Specialize it for Efficient Deployment

We address the challenging problem of efficient inference across many devices and resource constraints, especially on edge devices. Conventional approaches either manually design or use neural architecture search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally prohibitive (causing $CO_2$ emission as much as 5 cars' lifetime) thus unscalable. In this work, we propose to train a once-for-all (OFA) network that supports diverse architectural settings by decoupling training and search, to reduce the cost. We can quickly get a specialized sub-network by selecting from the OFA network without additional training. To efficiently train OFA networks, we also propose a novel progressive shrinking algorithm, a generalized pruning method that reduces the model size across many more dimensions than pruning (depth, width, kernel size, and resolution). It can obtain a surprisingly large number of sub-networks ($> 10^{19}$) that can fit different hardware platforms and latency constraints while maintaining the same level of accuracy as training independently. On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5x faster than MobileNetV3, 2.6x faster than EfficientNet w.r.t measured latency) while reducing many orders of magnitude GPU hours and $CO_2$ emission. In particular, OFA achieves a new SOTA 80.0% ImageNet top-1 accuracy under the mobile setting ($<$600M MACs). OFA is the winning solution for the 3rd Low Power Computer Vision Challenge (LPCVC), DSP classification track and the 4th LPCVC, both classification track and detection track. Code and 50 pre-trained models (for many devices & many latency constraints) are released at this https URL.
Fat inflatons, large turns and the η-problem

It is commonly believed that a successful period of inflation driven by a single or several scalar fields requires a specific hierarchy of masses given by Minf≪ H ≪ Mheavy, where Minf can correspond to several or a single light field and Mheavy corresponds to any heavy field that might be integrated out if it satisfies suitable conditions. This is at the heart of the so called η-problem in inflation, since large contributions to the masses of the inflatons might spoil the slow-roll conditions required for inflation. We show that, while this is an unavoidable conclusion in single field inflation, in multifield inflation, heavy fields as defined above, may be fully responsible for a successful period of what we call fat slow-roll inflation. Moreover we show that in this scenario, the turning rate of the inflationary trajectory, Ω/H, is larger than one. Thus, the η-problem is evaded with large turns in fat inflation. Depending on the perturbations' mass spectra, cosmological predictions will differ either slightly or largely with respect to those of the single field case. We illustrate this scenario in a concrete example in Type IIB string flux compactifications, where a probe D5-brane moving along the angular and radial directions in a warped throat drives fat D5-brane natural inflation. An instantaneous superplanckian decay constant can be defined, consistent with our low energy approximations. We compute the cosmological observables, which are consistent with Planck data, ameliorating the tension of single field natural inflation with observations. We also discuss fat inflation in the context of recently proposed swampland de Sitter conjectures.
Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing

We propose an iteration-free source separation algorithm based on Winner-Take-All (WTA) hash codes, which is a faster, yet accurate alternative to a complex machine learning model for single-channel source separation in a resource-constrained environment. We first generate random permutations with WTA hashing to encode the shape of the multidimensional audio spectrum to a reduced bitstring representation. A nearest neighbor search on the hash codes of an incoming noisy spectrum as the query string results in the closest matches among the hashed mixture spectra. Using the indices of the matching frames, we obtain the corresponding ideal binary mask vectors for denoising. Since both the training data and the search operation are bitwise, the procedure can be done efficiently in hardware implementations. Experimental results show that the WTA hash codes are discriminant and provide an affordable dictionary search mechanism that leads to a competent performance compared to a comprehensive model and oracle masking.
Learning Action Models from Disordered and Noisy Plan Traces

There is increasing awareness in the planning community that the burden of specifying complete domain models is too high, which impedes the applicability of planning technology in many real-world domains. Although there have many learning systems that help automatically learning domain models, most existing work assumes that the input traces are completely correct. A more realistic situation is that the plan traces are disordered and noisy, such as plan traces described by natural language. In this paper we propose and evaluate an approach for doing this. Our approach takes as input a set of plan traces with disordered actions and noise and outputs action models that can best explain the plan traces. We use a MAX-SAT framework for learning, where the constraints are derived from the given plan traces. Unlike traditional action models learners, the states in plan traces can be partially observable and noisy as well as the actions in plan traces can be disordered and parallel. We demonstrate the effectiveness of our approach through a systematic empirical evaluation with both IPC domains and the real-world dataset extracted from natural language documents.
A statistical framework for measuring the temporal stability of human mobility patterns

Despite the growing popularity of human mobility studies that collect GPS location data, the problem of determining the minimum required length of GPS monitoring has not been addressed in the current statistical literature. In this paper, we tackle this problem by laying out a theoretical framework for assessing the temporal stability of human mobility based on GPS location data. We define several measures of the temporal dynamics of human spatiotemporal trajectories based on the average velocity process, and on activity distributions in a spatial observation window. We demonstrate the use of our methods with data that comprise the GPS locations of 185 individuals over the course of 18 months. Our empirical results suggest that GPS monitoring should be performed over periods of time that are significantly longer than what has been previously suggested. Furthermore, we argue that GPS study designs should take into account demographic groups.
Gravitational-wave follow-up with CTA after the detection of GRBs in the TeV energy domain

The recent discovery of TeV emission from gamma-ray bursts (GRBs) by the MAGIC and H.E.S.S. Cherenkov telescopes confirmed that emission from these transients can extend to very high energies. The TeV energy domain reaches the most sensitive band of the Cherenkov Telescope Array (CTA). This newly anticipated, improved sensitivity will enhance the prospects of gravitational-wave follow-up observations by CTA to probe particle acceleration and high-energy emission from binary black hole and neutron star mergers, and stellar core-collapse events. Here we discuss the implications of TeV emission on the most promising strategies of choice for the gravitational-wave follow-up effort for CTA and Cherenkov telescopes more broadly. We find that TeV emission (i) may allow more than an hour of delay between the gravitational-wave event and the start of CTA observations; (ii) enables the use of CTA’s small size telescopes that have the largest field of view. We characterize the number of pointings needed to find a counterpart. (iii) We compute the annual follow-up time requirements and find that prioritization will be needed. (iv) Even a few telescopes could detect sufficiently nearby counterparts, raising the possibility of adding a handful of small-sized or medium-sized telescopes to the network at diverse geographic locations. (v) The continued operation of VERITAS/H.E.S.S./MAGIC would be a useful compliment to CTA’s follow-up capabilities by increasing the sky area that can be rapidly covered, especially in the United States and Australia, in which the present network of gravitational-wave detectors is more sensitive.
Probing the freeze-in mechanism in dark matter models with 
U(1)′
 gauge extensions

New gauge bosons at the MeV scale with tiny gauge couplings (so-called dark photons) can be responsible for the freeze-in production of dark matter and provide a clear target for present and future experiments. We study the effects of thermal mixing between dark photons and Standard Model gauge bosons and of the resulting plasmon decays on dark matter production before and after the electroweak phase transition. In the parameter regions preferred by the observed dark matter relic abundance, the dark photon is sufficiently long-lived to be probed with fixed-target experiments and light enough to induce direct detection signals. Indeed, current limits from XENON1T already constrain Dirac fermion dark matter in the GeV to TeV range produced via the freeze-in mechanism. We illustrate our findings for the case of a $U(1{)}_{B\ensuremath{-}L}$ gauge extension and discuss possible generalizations.
A White Dwarf with Transiting Circumstellar Material Far Outside Its Tidal Disruption Radius

We report the discovery of a white dwarf exhibiting deep, irregularly shaped transits indicative of disrupted planetary debris. Two prominent transits are seen in the 210-day-long Zwicky Transient Facility light curve of ZTF J013906.17+524536.89, each roughly 25 days long and causing $30{-}45\%$ dips in flux. The transits are separated by about $110$ days, much longer than the 4.5 hour orbital period observed in WD1145+017, the only other white dwarf with transiting circumstellar debris. An eccentricity $e>0.97$ is required assuming a 110-day orbital period and material that comes within the white dwarf's tidal disruption radius, suggesting an early phase of tidal disruption prior to complete disk filling. An optical spectrum of ZTF J0139+5245 reveals strong Balmer lines, establishing it as a DA white dwarf with $T_{\rm{eff}}=10{,}530\pm140$ K and $\log(g)=7.86\pm0.06$. The spectroscopic $T_{\rm{eff}}$ combined with broadband photometry suggests a high extinction towards ZTF J0139+5245. A Ca-II absorption feature at $3934$A is present in all three nights of spectra, but its association with ZTF J0139+5245 remains uncertain. In addition, high-speed time series photometry is suggestive of short-period (950 s) variability, consistent with ZZ Ceti pulsations.
Can dark neutrino interactions phase out the Hubble tension?

New interactions of neutrinos can stop them from free streaming even after the weak interaction freezeout. This results in a phase shift in the cosmic microwave background (CMB) acoustic peaks which can alleviate the Hubble tension. We demonstrate with Planck CMB and WiggleZ galaxy survey data that this acoustic phase shift, and thus solution to the Hubble tension, can be achieved for neutrinos interacting with dark matter without significantly affecting other observables and without changing the number of relativistic degrees of freedom. We predict potentially observable modification of the CMB B-modes.
Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from Their Dark Matter Using A Machine Trained on Hydrodynamic Simulations

We present a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simulations. As an example, we use the IllustrisTNG hydrodynamic simulation of a (75  h−1Mpc)3 volume to train our machine to predict e.g., stellar mass and star formation rate in a galaxy-sized halo based purely on its DM content. An extremely randomized tree (ERT) algorithm is used together with multiple novel improvements we introduce here such as a refined error function in machine training and two-stage learning. Aided by these improvements, our model demonstrates a significantly increased accuracy in predicting baryonic properties compared to prior attempts — in other words, the machine better mimics IllustrisTNG’s galaxy-halo correlation. By applying our machine to the MultiDark-Planck DM-only simulation of a large (1  h−1Gpc)3 volume, we then validate the pipeline that rapidly generates a galaxy catalogue from a DM halo catalogue using the correlations the machine found in IllustrisTNG. We also compare our galaxy catalogue with the ones produced by popular semi-analytic models (SAMs). Our so-called machine-assisted semi-simulation model (MSSM) is shown to be largely compatible with SAMs, and may become a promising method to transplant the baryon physics of galaxy-scale hydrodynamic calculations onto a larger-volume DM-only run. We discuss the benefits that machine-based approaches like this entail, as well as suggestions to raise the scientific potential of such approaches.
Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition.
Consistency of community structure in complex networks

The most widely used techniques for community detection in networks, including methods based on modularity, statistical inference, and information theoretic arguments, all work by optimizing objective functions that measure the quality of network partitions. There is a good case to be made, however, that one should not look solely at the single optimal community structure under such an objective function but rather at a selection of high-scoring structures. If one does this, one typically finds that the resulting structures show considerable variation, which could be taken as evidence that these community detection methods are unreliable, since they do not appear to give consistent answers. Here we argue that, upon closer inspection, the structures found are in fact consistent in a certain way. Specifically, we show that they can all be assembled from a set of underlying "building blocks," groups of network nodes that are usually found together in the same community. Different community structures correspond to different arrangements of blocks, but the blocks themselves are largely invariant. We propose an information theoretic method for discovering the building blocks in specific networks and demonstrate it with several example applications. We conclude that traditional community detection does in fact give a significant amount of insight into network structure.
Sufficient Representations for Categorical Variables

Many learning algorithms require categorical data to be transformed into real vectors before it can be used as input. Often, categorical variables are encoded as one-hot (or dummy) vectors. However, this mode of representation can be wasteful since it adds many low-signal regressors, especially when the number of unique categories is large. In this paper, we investigate simple alternative solutions for universally consistent estimators that rely on lower-dimensional real-valued representations of categorical variables that are "sufficient" in the sense that no predictive information is lost. We then compare preexisting and proposed methods on simulated and observational datasets.
Consistently estimating network statistics using Aggregated Relational Data

. Collecting complete network data is expensive, time-consuming, and often infeasible. Aggregated Relational Data (ARD), which capture information about a social network by asking a respondent questions of the form “How many people with characteris-tic X do you know?” provide a low-cost option when collecting complete network data is not possible. Rather than asking about connections between each pair of individuals directly, ARD collects the number of contacts the respondent knows with a given trait. Despite widespread use and a growing literature on ARD methodology, there is still no systematic understanding of when and why ARD should accurately recover features of the unobserved network. This paper provides such a characterization. Our ﬁrst contribution is to observe that ARD allows for consistent estimation of any model where cross-group link probabilities for a collection of (possibly unobserved) groups identiﬁes the model parameters. This covers a broad range of models, with rich unobserved heterogeneity, such as the Beta-model with node-speciﬁc unobserved eﬀects, the stochastic block model with unobserved community structure, and latent geometric space models with unobserved latent locations. Then, given consistent estimates of network model parameters, it is possible to simulate graphs from the ﬁtted distribution and analyze the distribution of network statistics. Our second contribution is to characterize conditions on a given network statistic, such as the degree of a node or its eigenvector centrality, under which simulated network statistics based on ARD will accurately estimate the network statistics of the unobserved graph.