nils-herrmann/clean_scirepeval huggingface dataset:
Speech: A Challenge to Digital Signal Processing Technology for Human-to-Computer Interaction

This software project based paper is for a vision of the near future in which computer interaction is characterized by natural face-to-face conversations with lifelike characters that speak, emote, and gesture. The first step is speech. The dream of a true virtual reality, a complete human-computer interaction system will not come true unless we try to give some perception to machine and make it perceive the outside world as humans communicate with each other. This software project is under development for listening and replying machine (Computer) through speech. The Speech interface is developed to convert speech input into some parametric form (Speech-to-Text) for further processing and the results, text output to speech synthesis (Text-to-Speech)
Quantum spectral curve for AdS_5/CFT_4

We present a new formalism, alternative to the old TBA-like approach, for solution of the spectral problem of planar N = 4 SYM. It takes a concise form of a non-linear matrix Riemann-Hilbert problem in terms of a few Q-functions. We demonstrate the formalism for two types of observables - local operators at weak coupling and cusped Wilson lines in a near BPS limit.
Can dark energy viscosity be detected with the Euclid survey

D. S. acknowledges support from the JAEDoc program with Grant No. JAEDoc074 and the Spanish MICINN under Project No. AYA2009-13936-C06-06. D. S. also acknowledges financial support from the Madrid Regional Government (CAM) under the program HEPHACOS P-ESP-00346, Consolider-Ingenio 2010 PAU (CSD2007-00060), as well as the European Union Marie Curie Network ‘‘UniverseNet’’ under Contract No. MRTN-CT-2006-035863. E. M. was supported by the Spanish MICINNs Juan de la Cierva programme (Grant No. JCI-2010-08112), by CICYT through Project No. FPA-2009 09017, by the Community of Madrid through the project HEPHACOS (Grant No. S2009/ESP- 1473) under Grant No. P-ESP-00346 and by the European Union FP7 ITN INVISIBLES (Marie Curie Actions, PITNGA-2011-289442). M. K. acknowledges funding by the Swiss NSF
Semantic-based Anomalous Pattern Discovery in Moving Object Trajectories

In this work, we investigate a novel semantic approach for pattern discovery in trajectories that, relying on ontologies, enhances object movement information with event semantics. The approach can be applied to the detection of movement patterns and behaviors whenever the semantics of events occurring along the trajectory is, explicitly or implicitly, available. In particular, we tested it against an exacting case scenario in maritime surveillance, i.e., the discovery of suspicious container transportations. 
The methodology we have developed entails the formalization of the application domain through a domain ontology, extending the Moving Object Ontology (MOO) described in this paper. Afterwards, movement patterns have to be formalized, either as Description Logic (DL) axioms or queries, enabling the retrieval of the trajectories that follow the patterns. 
In our experimental evaluation, we have considered a real world dataset of 18 Million of container events describing the deed undertaken in a port to accomplish the shipping (e.g., loading on a vessel, export operation). Leveraging events, we have reconstructed almost 300 thousand container trajectories referring to 50 thousand containers travelling along three years. We have formalized the anomalous itinerary patterns as DL axioms, testing different ontology APIs and DL reasoners to retrieve the suspicious transportations. 
Our experiments demonstrate that the approach is feasible and efficient. In particular, the joint use of Pellet and SPARQL-DL enables to detect the trajectories following a given pattern in a reasonable time with big size datasets.
Transponder delay effect in light time calculations for deep space navigation

During the last decade, the precision in the tracking of spacecraft has constantly improved. The discovery of few astrometric anomalies, such as the Pioneer and Earth flyby anomalies, stimulated further analysis of the operative modeling currently adopted in Deep Space Navigation (DSN). Our study shows that some traditional approximations lead to neglect tiny terms that could have consequences in the orbit determination of a probe in specific configurations such as during an Earth flyby. Therefore, we suggest here a way to improve the light time calculation used for probe tracking.
Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data

Modern machine learning methods are critical to the development of large-scale personalized learning systems that cater directly to the needs of individual learners. The recently developed SPARse Factor Analysis (SPARFA) framework provides a new statistical model and algorithms for machine learning-based learning analytics, which estimate a learner's knowledge of the latent concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and the latent concepts. SPARFA estimates these quantities given only the binary-valued graded responses to a collection of questions. In order to better interpret the estimated latent concepts, SPARFA relies on a post-processing step that utilizes user-defined tags (e.g., topics or keywords) available for each question. In this paper, we relax the need for user-defined tags by extending SPARFA to jointly process both graded learner responses and the text of each question and its associated answer(s) or other feedback. Our purely data-driven approach (i) enhances the interpretability of the estimated latent concepts without the need of explicitly generating a set of tags or performing a post-processing step, (ii) improves the prediction performance of SPARFA, and (iii) scales to large test/assessments where human annotation would prove burdensome. We demonstrate the efficacy of the proposed approach on two real educational datasets.
The dynamically extended mind

The extended mind hypothesis has stimulated much interest in cognitive science. However, its core claim, i.e. that the process of cognition can extend beyond the brain via the body and into the environment, has been heavily criticized. A prominent critique of this claim holds that when some part of the world is coupled to a cognitive system this does not necessarily entail that the part is also constitutive of that cognitive system. This critique is known as the “coupling-constitution fallacy”. In this paper we respond to this reductionist challenge by using an evolutionary robotics approach to create a minimal model of two acoustically coupled agents. We demonstrate how the interaction process as a whole has properties that cannot be reduced to the contributions of the isolated agents. We also show that the neural dynamics of the coupled agents has formal properties that are inherently impossible for those neural networks in isolation. By keeping the complexity of the model to an absolute minimum, we are able to illustrate how the coupling-constitution fallacy is in fact based on an inadequate understanding of the constitutive role of nonlinear interactions in dynamical systems theory.
An Improved Three-Weight Message-Passing Algorithm

We describe how the powerful "Divide and Concur" algorithm for constraint satisfaction can be derived as a special case of a message-passing version of the Alternating Direction Method of Multipliers (ADMM) algorithm for convex optimization, and introduce an improved message-passing algorithm based on ADMM/DC by introducing three distinct weights for messages, with "certain" and "no opinion" weights, as well as the standard weight used in ADMM/DC. The "certain" messages allow our improved algorithm to implement constraint propagation as a special case, while the "no opinion" messages speed convergence for some problems by making the algorithm focus only on active constraints. We describe how our three-weight version of ADMM/DC can give greatly improved performance for non-convex problems such as circle packing and solving large Sudoku puzzles, while retaining the exact performance of ADMM for convex problems. We also describe the advantages of our algorithm compared to other message-passing algorithms based upon belief propagation.
Electroweak Baryogenesis with a Supersymmetric Sector

We study a model with an exotic new sector strongly coupled to the Higgs boson, in which supersymmetry is introduced to protect the quartic coupling from a large running and avoid potential vacuum stability problem. The fermionic components present vector like mass terms, through which the Higgs diphoton decay branching ratio can be tuned. The bosonic components trigger a strongly rst order electroweak phase transition. We nd a large parameter region of eective Yukawa coupling y & 2 and mass parameters mf ms of a few hundred GeV, that can simultaneously accommodate the diphoton excess and electroweak baryogenesis, without vacuum stability and electroweak precision measurement problems.
Surface tension of the core-crust interface of neutron stars with global charge neutrality

It has been shown recently that taking into account strong, weak, electromagnetic, and gravitational interactions, and fulfilling the global charge neutrality of the system, a transition layer will happen between the core and crust of neutron stars, at the nuclear saturation density. We use relativistic mean field theory together with the Thomas-Fermi approximation to study the detailed structure of this transition layer and calculate its surface and Coulomb energy. We find that the surface tension is proportional to a power-law function of the baryon number density in the core bulk region. We also analyze the influence of the electron component and the gravitational field on the structure of the transition layer and the value of the surface tension, to compare and contrast with known phenomenological results in nuclear physics. Based on the above results we study the instability against Bohr-Wheeler surface deformations in the case of neutron stars obeying global charge neutrality. Assuming the core-crust transition at nuclear density ${\ensuremath{\rho}}_{\mathrm{core}}\ensuremath{\approx}2.7\ifmmode\times\else\texttimes\fi{}{10}^{14}$ g cm${}^{\ensuremath{-}3}$, we find that the instability sets the upper limit to the crust density, ${\ensuremath{\rho}}_{\mathrm{crust}}^{\mathrm{crit}}\ensuremath{\approx}1.2\ifmmode\times\else\texttimes\fi{}{10}^{14}$ g cm${}^{\ensuremath{-}3}$. This result implies a nonzero lower limit to the maximum electric field of the core-crust transition surface and makes inaccessible a limit of quasilocal charge neutrality in the limit ${\ensuremath{\rho}}_{\mathrm{crust}}={\ensuremath{\rho}}_{\mathrm{core}}$. The general framework presented here can be also applied to study the stability of sharp phase transitions in hybrid stars as well as in strange stars, both bare and with outer crust. The results of this work open the way to a more general analysis of the stability of these transition surfaces, accounting for other effects such as gravitational binding, centrifugal repulsion, magnetic field induced by rotating electric field, and therefore magnetic dipole-dipole interactions.
Modeling Temporal Activity Patterns in Dynamic Social Networks

The focus of this work is on developing probabilistic models for temporal activity of users in social networks (e.g., posting and tweeting) by incorporating the social network influence as perceived by the user. Although prior work in this area has developed sophisticated models for user activity, these models either ignore social network influence completely or incorporate it in an implicit manner. We overcome the nontransparency of the network in the model at the individual scale by proposing a coupled hidden Markov model (HMM), where each user's activity evolves according to a Markov chain with a hidden state that is influenced by the collective activity of the friends of the user. We develop generalized Baum-Welch and Viterbi algorithms for parameter learning and state estimation for the proposed framework. We then validate the proposed model using a significant corpus of user activity on Twitter. Our numerical studies show that with sufficient observations to ensure accurate model learning, the proposed framework explains the observed data better than either a renewal process-based model or a conventional (uncoupled) HMM. We also demonstrate the utility of the proposed approach in predicting the time to the next tweet. Finally, clustering in the model parameter space is shown to result in distinct natural clusters of users characterized by the interaction dynamic between a user and his network.
A Rank Minrelation - Majrelation Coefficient

Improving the detection of relevant variables using a new bivariate measure could importantly impact variable selection and large network inference methods. In this paper, we propose a new statistical coefficient that we call the rank minrelation coefficient. We define a minrelation of X to Y (or equivalently a majrelation of Y to X) as a measure that estimate p(Y > X) when X and Y are continuous random variables. The approach is similar to Lin's concordance coefficient that rather focuses on estimating p(X = Y). In other words, if a variable X exhibits a minrelation to Y then, as X increases, Y is likely to increases too. However, on the contrary to concordance or correlation, the minrelation is not symmetric. More explicitly, if X decreases, little can be said on Y values (except that the uncertainty on Y actually increases). In this paper, we formally define this new kind of bivariate dependencies and propose a new statistical coefficient in order to detect those dependencies. We show through several key examples that this new coefficient has many interesting properties in order to select relevant variables, in particular when compared to correlation.
Characterizing user behavior and information propagation on a social multimedia network

An increasing portion of modern socializing takes place via online social networks. Members of these communities often play distinct roles that can be deduced from observations of users' online activities. One such activity is the sharing of multimedia, the popularity of which can vary dramatically. Here we discuss our initial analysis of anonymized, scraped data from consenting Facebook users, together with associated demographic and psychological profiles. We present five clusters of users with common observed online behaviors, where these users also show correlated profile characteristics. Finally, we identify some common properties of the most popular multimedia content.
Translating Relational Queries into Spreadsheets

Spreadsheets are among the most commonly used applications for data management and analysis. They combine data processing with very diverse supplementary features: statistics, visualization, reporting, linear programming solvers, Web queries periodically downloading data from external sources, etc. However, the spreadsheet paradigm of computation still lacks sufficient analysis. In this article, we demonstrate that a spreadsheet can implement all data transformations definable in SQL, merely by utilizing spreadsheet formulas. We provide a query compiler, which translates any given SQL query into a worksheet of the same semantics, including NULL values. Thereby, database operations become available to the users who do not want to migrate to a database. They can define their queries using a high-level language and then get their execution plans in a plain vanilla spreadsheet. The functions available in spreadsheets impose limitations on the algorithms one can implement. In this paper, we offer O(n log2 n) sorting spreadsheet, using a non-constant number of rows, and, surprisingly, Depth-First-Search and Breadth-First-Search on graphs.
Physical-Layer Multicasting by Stochastic Transmit Beamforming and Alamouti Space-Time Coding

Consider transceiver designs in a multiuser multi-input single-output (MISO) downlink channel, where the users are to receive the same data stream simultaneously. This problem, known as physical-layer multicasting, has drawn much interest. Presently, a popularized approach is transmit beamforming, in which the beamforming optimization is handled by a rank-one approximation method called semidefinite relaxation (SDR). SDR-based beamforming has been shown to be promising for a small or moderate number of users. This paper describes two new transceiver strategies for physical-layer multicasting. The first strategy, called stochastic beamforming (SBF), randomizes the beamformer in a per-symbol time-varying manner, so that the rank-one approximation in SDR can be bypassed. We propose several efficiently realizable SBF schemes, and prove that their multicast achievable rate gaps with respect to the MISO multicast capacity must be no worse than 0.8314 bits/s/Hz, irrespective of any other factors such as the number of users. The use of channel coding and the assumption of sufficiently long code lengths play a crucial role in achieving the above result. The second strategy combines transmit beamforming and the Alamouti space-time code. The result is a rank-two generalization of SDR-based beamforming. We show by analysis that this SDR-based beamformed Alamouti scheme has a better worst-case effective signal-to-noise ratio (SNR) scaling, and hence a better multicast rate scaling, than SDR-based beamforming. We further the work by combining SBF and the beamformed Alamouti scheme, wherein an improved constant rate gap of 0.39 bits/s/Hz is proven. Simulation results show that under a channel-coded, many-user setting, the proposed multicast transceiver schemes yield significant SNR gains over SDR-based beamforming at the same bit error rate level.
MAP-REDUCE Runtime Enforcement of Information Flow Policies

We propose a flexible framework that can be easily customized to enforce a large variety of information flow properties. Our framework combines the ideas of secure multi-execution and map-reduce computations. The information flow property of choice can be obtained by simply changes to a map (or reduce) program that control parallel executions. 
We present the architecture of the enforcement mechanism and its customizations for non-interference (NI) (from Devriese and Piessens) and some properties proposed by Mantel, such as removal of inputs (RI) and deletion of inputs (DI), and demonstrate formally soundness and precision of enforcement for these properties.
Exploiting structural complexity for robust and rapid hyperspectral imaging

This paper presents several strategies for spectral de-noising of hyperspectral images and hypercube reconstruction from a limited number of tomographic measurements. In particular we show that the non-noisy spectral data, when stacked across the spectral dimension, exhibits low-rank. On the other hand, under the same representation, the spectral noise exhibits a banded structure. Motivated by these features we show that the de-noised spectral data and the unknown spectral noise and the respective bands can be simultaneously estimated through the use of a low-rank and simultaneous sparse minimization operation without prior knowledge of the noisy bands. This result is novel for for hyperspectral imaging applications. In addition, we show that imaging for the Computed Tomography Imaging Systems (CTIS) can be improved under limited angle tomography by using low-rank penalization. For both of these cases we exploit the recent results in the theory of low-rank matrix completion using nuclear norm minimization.
Galileon forces in the Solar System

We consider the challenging problem of obtaining an analytic understanding of realistic astrophysical dynamics in the presence of a Vainshtein screened fth force arising from infrared modications of General Relativity. In particular, we attempt to solve { within the most general at spacetime galileon model { the scalar force law between well separated bodies located well within the Vainshtein radius of the Sun. To this end, we derive the exact static Green’s function of the galileon wave equation linearized about the background eld generated by the Sun, for the minimal cubic and maximally quartic galileon theories, and then introduce a method to compute the general leading order force law perturbatively away from these limits. We also show that the same nonlinearities which produce the Vainshtein screening eect present obstacles to an analytic calculation of the galileon forces between closely bound systems within the solar system, such as that of the Earth and Moon. Within the test mass approximation, we deduce that a large enough quartic galileon interaction would suppress the eect on planetary perihelion precession below the
Imaginary part of the gravitational action at asymptotic boundaries and horizons

We study the imaginary part of the Lorentzian gravitational action for bounded regions, as described in . By comparing to a Euclidean calculation, we explain the agreement between the formula for this imaginary part and the formula for black hole entropy. We also clarify the topological structure of the imaginary part in Lovelock gravity. We then evaluate the action's imaginary part for some special regions. These include cylindrical slabs spanning the exterior of a stationary black hole spacetime, 'maximal diamonds' in various symmetric spacetimes, as well as local near-horizon regions. In the first setup, the black hole's entropy and conserved charges contribute to the action's imaginary and real parts, respectively. In the other two setups, the imaginary part coincides with the relevant entropy.
Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates

With a weighting scheme proportional to t, a traditional stochastic gradient descent (SGD) algorithm achieves a high probability convergence rate of O({\kappa}/T) for strongly convex functions, instead of O({\kappa} ln(T)/T). We also prove that an accelerated SGD algorithm also achieves a rate of O({\kappa}/T).
Direct Evidence for Synchronization in Japanese Business Cycles

We have analyzed the Indices of Industrial Production (Seasonal Adjustment Monthly Index) for a long period of 240 months (January 1988 to December 2007) in order to develop a deeper understanding of economic shocks. Angular frequencies estimated using the Hilbert transform are almost identical for the 16 industrial sectors in the indices. Moreover, partial phase locking was observed for the sectors. These are direct evidence of the synchronization present in Japanese business cycles. We also showed that the phase time series carries information on economic shocks. Common shock and individual shocks are separated using phase time series. The former dominates the economic shock in all of 1992, 1998 and 2001. The obtained results suggest that the business cycle may be described as dynamics of coupled limit cycle oscillators exposed to random individual shocks.