nils-herrmann/clean_scirepeval huggingface dataset:
Three-cluster breakup in deuteron-deuteron collisions: Single-scattering approximation

We present results for the three-cluster breakup in deuteron-deuteron collisions at 130 and 270 MeV deuteron beam energy. The breakup amplitude is calculated using the first term in the Neumann series expansion of the corresponding exact four-nucleon equations. In analogy with nucleon-deuteron breakup where an equivalent approximation is compared with exact calculations, we expect this single-scattering approximation to provide a rough estimation of three-body breakup observables in quasifree configurations. We predict the nucleon-deuteron and deuteron-deuteron three-cluster breakup cross sections to be of a comparable size and thereby question the reliability of the recent experimental data [A. Ramazani-Moghaddam-Arani, Ph.D. thesis, University of Groningen, 2009; A. Ramazani-Moghaddam-Arani et al., EPJ Web of Conferences 3, 04012 (2010)] that is smaller by about three orders of magnitude. We also show that an equivalent single-scattering approximation provides a reasonable description of deuteron-deuteron elastic scattering at forward scattering angles.
Recursive Neural Language Architecture for Tag Prediction

We consider the problem of learning distributed representations for tags from their associated content for the task of tag recommendation. Considering tagging information is usually very sparse, effective learning from content and tag association is very crucial and challenging task. Recently, various neural representation learning models such as WSABIE and its variants show promising performance, mainly due to compact feature representations learned in a semantic space. However, their capacity is limited by a linear compositional approach for representing tags as sum of equal parts and hurt their performance. In this work, we propose a neural feedback relevance model for learning tag representations with weighted feature representations. Our experiments on two widely used datasets show significant improvement for quality of recommendations over various baselines.
Descending Price Optimally Coordinates Search

Investigating potential purchases, such as a start-up company to acquire, is often a substantial investment under uncertainty. Standard market designs, such as simultaneous or ascending price auctions, compound this with additional uncertainty about the eventual price a bidder will have to pay in order to win. As a result they tend to confuse the process of search by leading to both wasteful information acquisition on goods that have already found a good purchaser and discouraging needed investigations of objects, potentially eliminating all gains from trade. Fully efficient procedures that avoid these problems, such as dynamic Vickrey-Clarke-Groves processes, are extremely complex and fragile. By contrast, we show that the Dutch auction preserves all of its properties from a standard setting without information costs because it guarantees, at the time of information acquisition, a price at which the good can be purchased.
Predictive Analytics Using Smartphone Sensors for Depressive Episodes

The behaviors of patients with depression are usually difficult to predict because the patients demonstrate the symptoms of a depressive episode without a warning at unexpected times. The goal of this research is to build algorithms that detect signals of such unusual moments so that doctors can be proactive in approaching already diagnosed patients before they fall in depression. Each patient is equipped with a smartphone with the capability to track its sensors. We first find the home location of a patient, which is then augmented with other sensor data to identify sleep patterns and select communication patterns. The algorithms require two to three weeks of training data to build standard patterns, which are considered normal behaviors; and then, the methods identify any anomalies in day-to-day data readings of sensors. Four smartphone sensors, including the accelerometer, the gyroscope, the location probe and the communication log probe are used for anomaly detection in sleeping and communication patterns.
Part-of-Speech Relevance Weights for Learning Word Embeddings

This paper proposes a model to learn word embeddings with weighted contexts based on part-of-speech (POS) relevance weights. POS is a fundamental element in natural language. However, state-of-the-art word embedding models fail to consider it. This paper proposes to use position-dependent POS relevance weighting matrices to model the inherent syntactic relationship among words within a context window. We utilize the POS relevance weights to model each word-context pairs during the word embedding training process. The model proposed in this paper paper jointly optimizes word vectors and the POS relevance matrices. Experiments conducted on popular word analogy and word similarity tasks all demonstrated the effectiveness of the proposed method.
Probabilistic Reasoning via Deep Learning: Neural Association Models

In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence. We propose to use neural networks to model association between any two events in a domain. Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are to be associated. The actual meaning of the conditional probabilities varies between applications and depends on how the models are trained. In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks (DNN) and relation-modulated neural nets (RMNN), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multi-relational knowledge bases and commonsense reasoning. Experimental results on several popular datasets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNNs and RMNNs perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer, where a pre-trained model can be quickly extended to an unseen relation after observing only a few training samples. To further prove the effectiveness of the proposed models, in this work, we have applied NAMs to solving challenging Winograd Schema (WS) problems. Experiments conducted on a set of WS problems prove that the proposed models have the potential for commonsense reasoning.
New dynamical instability in asymptotically anti–de Sitter spacetime

We present fully dynamical solutions to Einstein-scalar theory in asymptotically anti–de Sitter spacetime with a scalar potential containing particularly rich physics. Depending on one parameter in the potential, we find an especially interesting regime, which exhibits a dynamically unstable black brane, already at zero momentum, while nevertheless having positive specific heat. We show this using the nonlinear dynamics and give a clear interpretation in terms of the spectrum of linearized perturbations. Our results translate directly to their dual strongly coupled nonconformal field theories, whereby we in particular provide two mechanisms to obtain equilibration times much larger than the inverse temperature.
Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2

Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focused on tactical combat. Mastering MOBAs requires extensive practice, as is exemplified in the popular MOBA Defence of the Ancients 2 (DotA 2). In this paper, we present three data-driven measures of spatio-temporal behavior in DotA 2: 1) Zone changes; 2) Distribution of team members and: 3) Time series clustering via a fuzzy approach. We present a method for obtaining accurate positional data from DotA 2. We investigate how behavior varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results indicate that spatio-temporal behavior of MOBA teams is related to team skill, with professional teams having smaller within-team distances and conducting more zone changes than amateur teams. The temporal distribution of the within-team distances of professional and high-skilled teams also generally follows patterns distinct from lower skill ranks.
Thermodynamic products in extended phase-space

We have examined the thermodynamic properties for a variety of spherically symmetric charged-AdS black hole (BH) solutions, including the charged AdS BH surrounded by quintessence dark energy and charged AdS BH in f(R) gravity in extended phase-space. This framework involves treating the cosmological constant as thermodynamic variable (for example: thermodynamic pressure and thermodynamic volume). Then they should behave as an analog of Van-der-Waal (VdW) like systems. In the extended phase-space we have calculated the entropy product and thermodynamic volume product of all horizons. The mass (or enthalpy) independent nature of the said product signals they are universal quantities. The divergence of the specific heat indicates that the second-order phase transition occurs under certain condition.  A, we have studied the thermodynamic volume products for axisymmetric spacetime and it is shown to be not universal in nature. Finally,  = −ρ c+. The symbols are defined in the main work.
Thermodynamic Product Relations for Generalized Regular Black Hole

We derive thermodynamic product relations for four-parametric regular black hole (BH) solutions of the Einstein equations coupled with a nonlinear electrodynamics source. The four parameters can be described by the mass (), charge (), dipole moment (), and quadrupole moment (), respectively. We study its complete thermodynamics. We compute different thermodynamic products, that is, area product, BH temperature product, specific heat product, and Komar energy product, respectively. Furthermore, we show some complicated function of horizon areas that is indeed mass-independent and could turn out to be universal.
A universal tradeoff between power, precision and speed in physical communication

Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynamics do not provide general limits on power, precision and speed. Here we go beyond these classical theories to prove that the product of precision and speed is universally bounded by power dissipation in any physical communication channel whose dynamics is faster than that of the signal. Moreover, our derivation involves a novel connection between friction and information geometry. These results may yield insight into both the engineering design of communication devices and the structure and function of biological signaling systems.
Generating Text from Structured Data with Application to the Biography Domain

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. We experiment with a new dataset of biographies from Wikipedia that is an order of magni- tude larger than existing resources with over 700k samples. The dataset is also vastly more diverse with a 400k vocab- ulary, compared to a few hundred words for Weathergov or Robocup. Our model builds upon recent work on conditional neural language model for text genera- tion. To deal with the large vocabulary, we extend these models to mix a fixed vocabulary with copy actions that trans- fer sample-specific words from the in- put database to the generated output sen- tence. Our neural model significantly out- performs a classical Kneser-Ney language model adapted to this task by nearly 15 BLEU.
Disentangling Nonlinear Perceptual Embeddings With Multi-Query Triplet Networks

In typical perceptual tasks, higher-order concepts are inferred from visual features to assist with perceptual decision making. However, there is a multitude of visual concepts which can be inferred from a single stimulus. When learning nonlinear embeddings with siamese or triplet networks from similarities, we typically assume they are sourced from a single visual concept. In this paper, we are concerned with the hypothesis that it can be potentially harmful to ignore the heterogeneity of concepts affiliated with observed similarities when learning these embedding networks. We demonstrate empirically that this hypothesis holds and suggest an approach that deals with these shortcomings, by combining multiple notions of similarities in one compact system. We propose Multi-Query Networks (MQNs) that leverage recent advances in representation learning on factorized triplet embeddings in combination with Convolutional Networks in order to learn embeddings differentiated into semantically distinct subspaces, which are learned with a latent space attention mechanism. We show that the resulting model learns visually relevant semantic subspaces with features that do not only outperform single triplet networks, but even sets of concept specific networks.
Chatty maps: constructing sound maps of urban areas from social media data

Urban sound has a huge influence over how we perceive places. Yet, city planning is concerned mainly with noise, simply because annoying sounds come to the attention of city officials in the form of complaints, whereas general urban sounds do not come to the attention as they cannot be easily captured at city scale. To capture both unpleasant and pleasant sounds, we applied a new methodology that relies on tagging information of georeferenced pictures to the cities of London and Barcelona. To begin with, we compiled the first urban sound dictionary and compared it with the one produced by collating insights from the literature: ours was experimentally more valid (if correlated with official noise pollution levels) and offered a wider geographical coverage. From picture tags, we then studied the relationship between soundscapes and emotions. We learned that streets with music sounds were associated with strong emotions of joy or sadness, whereas those with human sounds were associated with joy or surprise. Finally, we studied the relationship between soundscapes and people's perceptions and, in so doing, we were able to map which areas are chaotic, monotonous, calm and exciting. Those insights promise to inform the creation of restorative experiences in our increasingly urbanized world.
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection

This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the framework is demonstrated on a critical plant science problem. In the United States, approximately $1 billion is lost per annum due to a nematode infection on soybean plants. Currently, plant-pathologists rely on labor-intensive and time-consuming identification of Soybean Cyst Nematode (SCN) eggs in soil samples via manual microscopy. The proposed framework attempts to significantly expedite the process by using a series of manually labeled microscopic images for training followed by automated high-throughput egg detection. The problem is particularly difficult due to the presence of a large population of non-egg particles (disturbances) in the image frames that are very similar to SCN eggs in shape, pose and illumination. Therefore, the selective autoencoder is trained to learn unique features related to the invariant shapes and sizes of the SCN eggs without handcrafting. After that, a composite non-maximum suppression and differencing is applied at the post-processing stage.
Inverse construction of the ΛLTB model from a distance-redshift relation

Spherically symmetric dust universe models with a positive cosmological constant Λ, known as Λ-Lemaȋtre-Tolman-Bondi (ΛLTB) models, are considered. We report a method to construct the ΛLTB model from a given distance-redshift relation observed at the symmetry center. The spherical inhomogeneity is assumed to be composed of growing modes. We derive a set of ordinary differential equations for three functions of the redshift, which specify the spherical inhomogeneity. Once a distance-redshift relation is given, with careful treatment of possible singular points, we can uniquely determine the model by solving the differential equations for each value of Λ. As a demonstration, we fix the distance-redshift relation as that of the flat ΛCDM model with (Ωdism0, ΩdisΛ0)=(0.3,0.7), where Ωdism0 and ΩdisΛ0 are the normalized matter density and the cosmological constant, respectively. Then, we construct the ΛLTB model for several values of ΩΛ0:=Λ/(3H02), where H0 is the present Hubble parameter observed at the symmetry center. We obtain void (over dense) structure around the symmetry center for ΩΛ0 < ΩdisΛ0(ΩΛ0 > ΩdisΛ0). We show the relation between the ratio ΩΛ0/ΩdisΛ0 and the amplitude of the inhomogeneity.
Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video

This paper proposes an end-to-end convolutional selective autoencoder approach for early detection of combustion instabilities using rapidly arriving flame image frames. The instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. These properties are described as self-sustaining, large amplitude pressure oscillations and show varying spatial scales periodic coherent vortex structure shedding. However, such instability is extremely difficult to detect before a combustion process becomes completely unstable due to its sudden (bifurcation-type) nature. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. In that process, the model learns to identify and extract rich descriptive and explanatory flame shape features. With such a training scheme, the selective autoencoder is shown to be able to detect subtle instability features as a combustion process makes transition from stable to unstable region. As a consequence, the deep learning tool-chain can perform as an early detection framework for combustion instabilities that will have a transformative impact on the safety and performance of modern engines.
Collective decision making with a mix of majority and minority seekers.

We study a model of a population making a binary decision based on information spreading within the population, which is fully connected or covering a square grid. We assume that a fraction of the population wants to make the choice of the minority, whereas the rest want to make the majority choice. This resembles opinion spreading with "contrarian" agents but has the game theoretic aspect that agents try to optimize their own situation in ways that are incompatible with the common good. When this fraction is less than 1/2, the population can efficiently self-organize to a state where agents get what they want-the majority (i.e., the majority seekers) have one opinion, the minority seekers have the other. If the fraction is larger than 1/2, there is a frustration in the population that dramatically changes the dynamics. In this region, the population converges, through some distinct phases, to a state of approximately equal-sized opinions. Just over the threshold the state of the population is furthest from the collectively optimal solution.
Deep Learning at Scale and at Ease

Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multimodal data analysis. Large deep learning models are developed for learning rich representations of complex data. There are two challenges to overcome before deep learning can be widely adopted in multimedia and other applications. One is usability, namely the implementation of different models and training algorithms must be done by nonexperts without much effort, especially when the model is large and complex. The other is scalability, namely the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets. To address these two challenges, in this article we design a distributed deep learning platform called SINGA, which has an intuitive programming model based on the common layer abstraction of deep learning models. Good scalability is achieved through flexible distributed training architecture and specific optimization techniques. SINGA runs on both GPUs and CPUs, and we show that it outperforms many other state-of-the-art deep learning systems. Our experience with developing and training deep learning models for real-life multimedia applications in SINGA shows that the platform is both usable and scalable.
A multinomial probabilistic model for movie genre predictions

This paper proposes a movie genre-prediction based on multinomial probability model. To the best of our knowledge, this problem has not been addressed yet in the field of recommender system. The prediction of a movie genre has many practical applications including complementing the items categories given by experts and providing a surprise effect in the recommendations given to a user. We employ mulitnomial event model to estimate a likelihood of a movie given genre and the Bayes rule to evaluate the posterior probability of a genre given a movie. Experiments with the MovieLens dataset validate our approach. We achieved 70% prediction rate using only 15% of the whole set for training.
A Novel Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

Maximization (EM) is a widely employed mixture model-based data clustering algorithm and produces exceptionally good results. However, many researchers reported that the EM algorithm requires huge computational efforts than other clustering algorithms. This paper presents an algorithm for the novel hybridization of EM and K-Means techniques for achieving better clustering performance (NovHbEMKM). This algorithm first performs K-Means and then using these results it performs EM and K-Means in the alternative iterations. Along with the NovHbEMKM, experiments are carried out with the algorithms for EM, EM using the results of K-Means and Cluster package of Purdue University. Experiments are carried out with datasets from UCI ML repository and synthetic datasets. Execution time, Clustering Fitness and Sum of Squared Errors (SSE) are computed as performance criteria. In all the experiments the proposed NovHbEMKM algorithm is taking less execution time by producing results with higher clustering fitness and lesser SSE than other algorithms including the Cluster package.