nils-herrmann/clean_scirepeval huggingface dataset:
DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting

In this paper, we present DuTongChuan, a novel context-aware translation model for simultaneous interpreting. This model allows to constantly read streaming text from the Automatic Speech Recognition (ASR) model and simultaneously determine the boundaries of Information Units (IUs) one after another. The detected IU is then translated into a fluent translation with two simple yet effective decoding strategies: partial decoding and context-aware decoding. In practice, by controlling the granularity of IUs and the size of the context, we can get a good trade-off between latency and translation quality easily. Elaborate evaluation from human translators reveals that our system achieves promising translation quality (85.71% for Chinese-English, and 86.36% for English-Chinese), specially in the sense of surprisingly good discourse coherence. According to an End-to-End (speech-to-speech simultaneous interpreting) evaluation, this model presents impressive performance in reducing latency (to less than 3 seconds at most times). Furthermore, we successfully deploy this model in a variety of Baidu's products which have hundreds of millions of users, and we release it as a service in our AI platform.
The evolution of lying in well-mixed populations

Lies can have profoundly negative consequences for individuals, groups and even for societies. Understanding how lying evolves and when it proliferates is therefore of significant importance for our personal and societal well-being. To that effect, we here study the sender–receiver game in well-mixed populations with methods of statistical physics. We use the Monte Carlo method to determine the stationary frequencies of liars and believers for four different lie types. We consider altruistic white lies that favour the receiver at a cost to the sender, black lies that favour the sender at a cost to the receiver, spiteful lies that harm both the sender and the receiver, and Pareto white lies that favour both the sender and the receiver. We find that spiteful lies give rise to trivial behaviour, where senders quickly learn that their best strategy is to send a truthful message, while receivers likewise quickly learn that their best strategy is to believe the sender’s message. For altruistic white lies and black lies, we find that most senders lie while most receivers do not believe the sender’s message, but the exact frequencies of liars and non-believers depend significantly on the payoffs, and they also evolve non-monotonically before reaching the stationary state. Lastly, for Pareto white lies we observe the most complex dynamics, with the possibility of both lying and believing evolving with all frequencies between 0 and 1 in dependence on the payoffs. We discuss the implications of these results for moral behaviour in human experiments.
Kernels on fuzzy sets: an overview

This paper introduces the concept of kernels on fuzzy sets as a similarity measure for $[0,1]$-valued functions, a.k.a. \emph{membership functions of fuzzy sets}. 
We defined the following classes of kernels: the cross product, the intersection, the non-singleton and the distance-based kernels on fuzzy sets. 
Applicability of those kernels are on machine learning and data science tasks where uncertainty in data has an ontic or epistemistic interpretation.
Influencer identification in dynamical complex systems

The integrity and functionality of many real-world complex systems hinge on a small set of pivotal nodes, or influencers. In different contexts, these influencers are defined as either structurally important nodes that maintain the connectivity of networks, or dynamically crucial units that can disproportionately impact certain dynamical processes. In practice, identification of the optimal set of influencers in a given system has profound implications in a variety of disciplines. In this review, we survey recent advances in the study of influencer identification developed from different perspectives, and present state-of-the-art solutions designed for different objectives. In particular, we first discuss the problem of finding the minimal number of nodes whose removal would breakdown the network (i.e. the optimal percolation or network dismantle problem), and then survey methods to locate the essential nodes that are capable of shaping global dynamics with either continuous (e.g. independent cascading models) or discontinuous phase transitions (e.g. threshold models). We conclude the review with a summary and an outlook.
BTZ black hole with Korteweg–de Vries-type boundary conditions: Thermodynamics revisited

The thermodynamic properties of the Ba\~nados-Teitelboim-Zanelli (BTZ) black hole endowed with Korteweg-de Vries (KdV)-type boundary conditions are considered. This familiy of boundary conditions for General Relativity on AdS$_{3}$ is labeled by a non-negative integer $n$, and gives rise to a dual theory which possesses anisotropic Lifshitz scaling invariance with dynamical exponent $z=2n+1$. We show that from the scale invariance of the action for stationary and circularly symmetric spacetimes, an anisotropic version of the Smarr relation arises, and we prove that it is totally consistent with the previously reported anisotropic Cardy formula. The set of KdV-type boundary conditions defines an unconventional thermodynamic ensemble, which leads to a generalized description of the thermal stability of the system. Finally, we show that at the self-dual temperature $T_{s}= \frac{1}{2\pi}(\frac{1}{z})^{\frac{z}{z+1}}$, there is a Hawking-Page phase transition between the BTZ black hole and thermal AdS$_{3}$ spacetime.
Elliptic curves with large Tate-Shafarevich groups over $\mathbb{F}_q(t)$

Let $\mathbb{F}_q$ be a finite field of odd characteristic $p$. We exhibit elliptic curves over the rational function field $K = \mathbb{F}_q(t)$ whose Tate-Shafarevich groups are large. More precisely, we consider certain infinite sequences of explicit elliptic curves $E$, for which we prove that their Tate-Shafarevich group $\mathrm{III}(E)$ is finite and satisfies $|\mathrm{III}(E)| = H(E)^{1+o(1)}$ as $H(E)\to\infty$, where $H(E)$ denotes the exponential differential height of $E$. The elliptic curves in these sequences are pairwise neither isogenous nor geometrically isomorphic. We further show that the $p$-primary part of their Tate-Shafarevich group is trivial. The proof involves explicitly computing the $L$-functions of these elliptic curves, proving the BSD conjecture for them, and obtaining estimates on the size of the central value of their $L$-function.
Coercion, Consent, and Participation in Citizen Science

Throughout history, everyday people have contributed to science through a myriad of volunteer activities. This early participation required training and often involved mentorship from scientists or senior citizen scientists (or, as they were often called, gentleman scientists). During this learning process, participants learned how they and their data would be used both to advance science, and in some cases, advance the careers of professional collaborators. Modern, online citizen science, allows participation with just a few clicks, and people may participate without understanding what they are contributing to. Too often, they happily see what they are doing as the privilege of painting Tom Sawyer's fence without realizing they are actually being used as merely a means to a scientific end. This paper discusses the ethical dilemmas that plague modern citizen science, including: the issues of informed consent, such as not requiring logins; the issues of coercion inherent in mandatory classroom assignments requiring data submission; and the issues of using people merely as a means to an end that are inherent in technonationalism, and projects that do not provide utility to the users beyond the knowledge they helped. This work is tested within the context of astronomy citizen science.
Government as Network Catalyst: Accelerating Self-Organization in a Strategic Industry

Governments have long-standing interests in preventing market failures and enhancing innovation in strategic industries. Public policy regarding domestic technology is critical to both national security and economic prosperity. Governments often seek to enhance their global competitiveness by promoting private sector cooperative activity at the inter-organizational level. Research on network governance has illuminated the structure of boundary-spanning collaboration mainly for programs with immediate public or nonprofit objectives. Far less research has examined how governments might accelerate private sector cooperation to prevent market failures or to enhance innovation. The theoretical contribution of this research is to suggest that government programs might catalyze cooperative activity by accelerating the preferential attachment mechanism inherent in social networks. We analyze the long-term effects of a government program on the strategic alliance network of 451 organizations in the high-tech semiconductor industry between 1987 and 1999, using stochastic network analysis methods for longitudinal social networks.
Multilayer modeling of adoption dynamics in energy demand management.

Due to the emergence of new technologies, the whole electricity system is undergoing transformations on a scale and pace never observed before. The decentralization of energy resources and the smart grid have forced utility services to rethink their relationships with customers. Demand response (DR) seeks to adjust the demand for power instead of adjusting the supply. However, DR business models rely on customer participation and can only be effective when large numbers of customers in close geographic vicinity, e.g., connected to the same transformer, opt in. Here, we introduce a model for the dynamics of service adoption on a two-layer multiplex network: the layer of social interactions among customers and the power-grid layer connecting the households. While the adoption process-based on peer-to-peer communication-runs on the social layer, the time-dependent recovery rate of the nodes depends on the states of their neighbors on the power-grid layer, making an infected node surrounded by infectious ones less keen to recover. Numerical simulations of the model on synthetic and real-world networks show that a strong local influence of the customers' actions leads to a discontinuous transition where either none or all the nodes in the network are infected, depending on the infection rate and social pressure to adopt. We find that clusters of early adopters act as points of high local pressure, helping maintaining adopters, and facilitating the eventual adoption of all nodes. This suggests direct marketing strategies on how to efficiently establish and maintain new technologies such as DR schemes.
Opinion Formation under Antagonistic Influences

We study the opinion dynamics in a generalized voter model in which voters are additionally influenced by two antagonistic news sources, whose effect is to promote political polarization. We show that, as the influence of the news sources is increased, the mean time to reach consensus is anomalously long, the time to reach a politically polarized state is quite short, and the steady-state opinion distribution exhibits a transition from a near consensus state to a politically polarized state.
Multi-Frame Cross-Entropy Training for Convolutional Neural Networks in Speech Recognition

We introduce Multi-Frame Cross-Entropy training (MFCE) for convolutional neural network acoustic models. Recognizing that similar to RNNs, CNNs are in nature sequence models that take variable length inputs, we propose to take as input to the CNN a part of an utterance long enough that multiple labels are predicted at once, therefore getting cross-entropy loss signal from multiple adjacent frames. This increases the amount of label information drastically for small marginal computational cost. We show large WER improvements on hub5 and rt02 after training on the 2000-hour Switchboard benchmark.
Learning stabilizable nonlinear dynamics with contraction-based regularization

We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key contribution is a control-theoretic regularizer for dynamics fitting rooted in the notion of stabilizability, a constraint which guarantees the existence of robust tracking controllers for arbitrary open-loop trajectories generated with the learned system. Leveraging tools from contraction theory and statistical learning in reproducing kernel Hilbert spaces, we formulate stabilizable dynamics learning as a functional optimization with a convex objective and bi-convex functional constraints. Under a mild structural assumption and relaxation of the functional constraints to sampling-based constraints, we derive the optimal solution with a modified representer theorem. Finally, we utilize random matrix feature approximations to reduce the dimensionality of the search parameters and formulate an iterative convex optimization algorithm that jointly fits the dynamics functions and searches for a certificate of stabilizability. We validate the proposed algorithm in simulation for a planar quadrotor, and on a quadrotor hardware testbed emulating planar dynamics. We verify, both in simulation and on hardware, significantly improved trajectory generation and tracking performance with the control-theoretic regularized model over models learned using traditional regression techniques, especially when learning from small supervised datasets. The results support the conjecture that the use of stabilizability constraints as a form of regularization can help prune the hypothesis space in a manner that is tailored to the downstream task of trajectory generation and feedback control. This produces models that are not only dramatically better conditioned, but also data efficient.
The Massive Star-forming Regions Omnibus X-ray Catalog, Third Installment

We offer to the star formation community the third installment of the Massive Star-forming Regions (MSFRs) Omnibus X-ray Catalog (MOXC3), a compilation of X-ray point sources detected in 50 archival Chandra Advanced CCD Imaging Spectrometer observations of 14 Galactic MSFRs and surrounding fields. The MOXC3 MSFRs are NGC 2264, NGC 6193, RCW 108-IR, Aur OB1, DR15, NGC 6231, Berkeley 87, NGC 6357, AFGL 4029, h Per (NGC 869), NGC 281, Onsala 2S, G305, and RCW 49 (Wd 2); they have distances of 0.7–4.2 kpc. Most exhibit clumped or clustered young stellar populations; several contain at least two distinct massive young stellar clusters. The total MOXC3 catalog includes 27,923 X-ray point sources. We take great care to identify even the faintest X-ray point sources across these fields. This allows us to remove this point source light, revealing diffuse X-ray structures that pervade and surround MSFRs, often generated by hot plasmas from massive star feedback. As we found in MOXC1 and MOXC2, diffuse X-ray emission is traceable in all MOXC3 MSFRs; here we perform spectral fitting to investigate the origins of selected diffuse regions. Once again, MOXC3 shows the value of high spatial resolution X-ray studies of MSFRs enabled by Chandra.
Uncertainties in νp-process nucleosynthesis from Monte Carlo variation of reaction rates

It has been suggested that a νp-process can occur when hot, dense, and proton-rich matter is expanding within a strong flux of antineutrinos. In such an environment, proton-rich nuclides can be produced in sequences of proton captures and (n, p) reactions, where the free neutrons are created in situ by $\overline{\nu }_\mathrm{e}+\mathrm{p} {\rightarrow} \mathrm{n}+\mathrm{e}^+$ reactions. The detailed hydrodynamic evolution determines where the nucleosynthesis path turns off from N = Z line and how far up the nuclear chart it runs. In this work, the uncertainties on the final isotopic abundances stemming from uncertainties in the nuclear reaction rates were investigated in a large-scale Monte Carlo approach, simultaneously varying more than 10 000 reactions. A large range of model conditions was investigated because a definitive astrophysical site for the νp-process has not yet been identified. The present parameter study provides, for each model, identification of the key nuclear reactions dominating the uncertainty for a given nuclide abundance. As all rates appearing in the νp-process involve unstable nuclei, and thus only theoretical rates are available, the final abundance uncertainties are larger than those for nucleosynthesis processes closer to stability. Nevertheless, most uncertainties remain below a factor of 3 in trajectories with robust nucleosynthesis. More extreme conditions allow production of heavier nuclides but show larger uncertainties because of the accumulation of the uncertainties in many rates and because the termination of nucleosynthesis is not at equilibrium conditions. It is also found that the solar ratio of the abundances of 92Mo and 94Mo could be reproduced within uncertainties.
Modelling the Effects of Dark Matter Substructure on Globular Cluster Evolution with the Tidal Approximation

We present direct N-body simulations of tidally filling 30,000 M⊙ star clusters orbiting between 10 kpc and 100 kpc in galaxies with a range of dark matter substructure properties. The time-dependent tidal force is determined based on the combined tidal tensor of the galaxy’s smooth and clumpy dark matter components, the latter of which causes fluctuations in the tidal field that can heat clusters. The strength and duration of these fluctuations are sensitive to the local dark matter density, substructure fraction, sub-halo mass function, and the sub-halo mass-size relation. Based on the cold dark matter framework, we initially assume sub-halos are Hernquist spheres following a power-law mass function between 105 and 1011M⊙ and find that tidal fluctuations are too weak and too short to affect star cluster evolution. Treating sub-halos as point masses, to explore how denser sub-halos affect clusters, we find that only sub-halos with masses greater than 106M⊙ will cause cluster dissolution times to decrease. These interactions can also decrease the size of a cluster while increasing the velocity dispersion and tangential anisotropy in the outer regions via tidal heating. Hence increased fluctuations in the tidal tensor, especially fluctuations that are due to low-mass halos, do not necessarily translate into mass loss. We further conclude that the tidal approximation can be used to model cluster evolution in the tidal fields of cosmological simulations with a minimum cold dark matter sub-halo mass of 106M⊙, as the effect of lower-mass sub-halos on star clusters is negligible.
Evolution of dark matter microhalos through stellar encounters

In the cold dark matter scenario, the smallest dark matter halos may be earth mass or smaller. These microhalos would be the densest dark matter objects in the Universe, making their accurate characterization important for astrophysical dark matter detection efforts. Moreover, their properties are closely linked to the nature of dark matter and the physics of the early universe, making them valuable cosmological probes. Dark matter microhalos survive as subhalos within larger galactic halos, but due to their small size, they are susceptible to encounters with individual stars. We use a large number of $N$-body simulations to develop a framework that can predict the evolution of a microhalo's density profile due to stellar encounters. We find that there is a universal density profile for microhalos subjected to stellar encounters, and each encounter alters a microhalo's scale parameters in a way that is simply related to the energy the encounter injects. Our framework can rapidly and accurately characterize the impact of stellar encounters on whole ensembles of microhalos, making it a promising tool for understanding the populations of microhalos within galactic halos.
Gravitational wave signals of pseudo-Goldstone dark matter in the 
Z3
 complex singlet model

We study pseudo-Goldstone dark matter in the $\mathbb{Z}_{3}$ complex scalar singlet model. Because the direct detection spin-independent cross section is suppressed, such dark matter is allowed in a large mass range. Unlike in the original model stabilized by a parity, due to the cubic coupling of the singlet the $\mathbb{Z}_{3}$ model can accommodate first-order phase transitions that give rise to a stochastic gravitational wave signal potentially observable in future space-based detectors.
Survival of molecular gas in a stellar feedback-driven outflow witnessed with the MUSE TIMER project and ALMA

Stellar feedback plays a significant role in modulating star formation, redistributing metals, and shaping the baryonic and dark structure of galaxies – however, the efficiency of its energy deposition to the interstellar medium is challenging to constrain observationally. Here we leverage HST and ALMA imaging of a molecular gas and dust shell ($M_{\mathrm{ H}_2} \sim 2\times 10^{5}\, {\rm M}_{\odot }$) in an outflow from the nuclear star-forming ring of the galaxy NGC 3351, to serve as a boundary condition for a dynamical and energetic analysis of the outflowing ionized gas seen in our MUSE TIMER survey. We use starburst99 models and prescriptions for feedback from simulations to demonstrate that the observed star formation energetics can reproduce the ionized and molecular gas dynamics – provided a dominant component of the momentum injection comes from direct photon pressure from young stars, on top of supernovae, photoionization heating, and stellar winds. The mechanical energy budget from these sources is comparable to low luminosity active galactic neuclei, suggesting that stellar feedback can be a relevant driver of bulk gas motions in galaxy centres – although here ≲10−3 of the ionized gas mass is escaping the galaxy. We test several scenarios for the survival/formation of the cold gas in the outflow, including in situ condensation and cooling. Interestingly, the geometry of the molecular gas shell, observed magnetic field strengths and emission line diagnostics are consistent with a scenario where magnetic field lines aided survival of the dusty ISM as it was initially launched (with mass-loading factor ≲1) from the ring by stellar feedback. This system’s unique feedback-driven morphology can hopefully serve as a useful litmus test for feedback prescriptions in magnetohydrodynamical galaxy simulations.
Solar Angular Momentum Loss over the Past Several Millennia

The Sun and Sun-like stars lose angular momentum to their magnetized stellar winds. This braking torque is coupled to the stellar magnetic field, such that changes in the strength and/or geometry of the field modifies the efficiency of this process. Since the space age, we have been able to directly measure solar wind properties using in situ spacecraft. Furthermore, indirect proxies such as sunspot number, geomagnetic indices, and cosmogenic radionuclides, constrain the variation of solar wind properties on centennial and millennial timescales. We use near-Earth measurements of the solar wind plasma and magnetic field to calculate the torque on the Sun throughout the space age. Then, reconstructions of the solar open magnetic flux are used to estimate the time-varying braking torque during the last nine millennia. We assume a relationship for the solar mass-loss rate based on observations during the space age which, due to the weak dependence of the torque on mass-loss rate, does not strongly affect our predicted torque. The average torque during the last nine millennia is found to be 2.2 × 1030 erg, which is comparable to the average value from the last two decades. Our data set includes grand minima (such as the Maunder Minimum), and maxima in solar activity, where the torque varies from ∼1 to 5 × 1030 erg (averaged on decadal timescales), respectively. We find no evidence for any secular variation of the torque on timescales of less than 9000 yr.