nils-herrmann/clean_scirepeval huggingface dataset:
Cohort effects in mortality modelling: a Bayesian state-space approach

Extensions of dynamic mortality models with cohort features have been proposed in the literature to account for these factors under the generalised linear modelling framework. In this paper we approach the problem of mortality modelling with cohort factors incorporated through a novel formulation under a state-space methodology. In the process we demonstrate that cohort factors can be formulated naturally under the state-space framework, despite the fact that cohort factors are indexed according to year-of-birth rather than year. Bayesian inference for cohort models in a state-space formulation is then developed based on an efficient Markov chain Monte Carlo sampler, allowing for the quantification of parameter uncertainty in cohort models and resulting mortality forecasts that are used for life expectancy and life table constructions. The effectiveness of our approach is examined through comprehensive empirical studies involving male and female populations from various countries. Our results show that cohort patterns are present for certain countries that we studied and the inclusion of cohort factors are crucial in capturing these phenomena, thus highlighting the benefits of introducing cohort models in the state-space framework. Forecasting of cohort models is also discussed in light of the projection of cohort factors.
Synchronization of Chaos

Dynamical networks are important models for the behaviour of complex systems, modelling physical, biological and societal systems, including the brain, food webs, epidemic disease in populations, power grids and many other. Such dynamical networks can exhibit behaviour in which deterministic chaos, exhibiting unpredictability and disorder, coexists with synchronization, a classical paradigm of order. We survey the main theory behind complete, generalized and phase synchronization phenomena in simple as well as complex networks and discuss applications to secure communications, parameter estimation and the anticipation of chaos.
Dimension quotients, Fox subgroups and limits of functors

This paper presents a description of the fourth dimension quotient, using the theory of limits of functors from the category of free presentations of a given group to the category of abelian groups. A functorial description of a quotient of the third Fox subgroup is given and, as a consequence, an identification (not involving an isolator) of the third Fox subgroup is obtained. It is shown that the limit over the category of free representations of the third Fox quotient represents the composite of two derived quadratic functors.
Permutation Generators Based on Unbalanced Feistel Network: Analysis of the Conditions of Pseudorandomness

A block cipher is a bijective function that transforms a plaintext to a ciphertext. A block cipher is a principle component in a cryptosystem because the security of a cryptosystem depends on the security of a block cipher. A Feistel network is the most widely used method to construct a block cipher. This structure has a property such that it can transform a function to a bijective function. But the previous Feistel network is unsuitable to construct block ciphers that have large input-output size. One way to construct block ciphers with large input-output size is to use an unbalanced Feistel network that is the generalization of a previous Feistel network. There have been little research on unbalanced Feistel networks and previous work was about some particular structures of unbalanced Feistel networks. So previous work didn't provide a theoretical base to construct block ciphers that are secure and efficient using unbalanced Feistel networks. 
In this thesis, we analyze the minimal number of rounds of pseudo-random permutation generators that use unbalanced Feistel networks. That is, after categorizing unbalanced Feistel networks as source-heavy structures and target-heavy structures, we analyze the minimal number of rounds of pseudo-random permutation generators that use each structure. Therefore, in order to construct a block cipher that is secure and efficient using unbalanced Feistel networks, we should follow the results of this thesis. Additionally, we propose a new unbalanced Feistel network that has some advantages such that it can extend a previous block cipher with small input-output size to a new block cipher with large input-output size. We also analyze the minimum number of rounds of a pseudo-random permutation generator that uses this structure.
Trans-Planckian quantum corrections and inflationary vacuum fluctuations of non-minimally coupled scalar fields

In this paper, we discuss how trans-Planckian physics affects inflationary vacuum fluctuations and primordial density perturbations. The trans-Planckian problem during inflation has been widely discussed in the literature, but it is still under debate. We reconsider this problem by using the two-point correlation function of the non-minimally coupled scalar fields and constructing the effective potential with the adiabatic (WKB) regularization or approximation. First, we clearly show that the cut-off divergence of the quantum fluctuations does not drastically change during inflation under reasonable assumptions and the corrections can be embedded in standard effective potential. Thus, the UV effects on the primordial density perturbation are well translated into the effective potential. Then, we find out the modified effective potential from the inflationary fluctuations and show how the trans-Planckian or UV corrections change the potential during inflation. We clearly show that the new physics strongly affects the inflation potential during inflation and we obtain an inflationary constraint  see text], where  see text] is the interaction coupling at the UV scale
Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition

This work deviates from easy-to-define class boundaries for object interactions. For the task of object interaction recognition, often captured using an egocentric view, we show that semantic ambiguities in verbs and recognising sub-interactions along with concurrent interactions result in legitimate class overlaps . We thus aim to model the mapping between observations and interaction classes, as well as class overlaps, towards a probabilistic multi-label classifier that emulates human annotators. Given a video segment containing an object interaction, we model the probability for a verb, out of a list of possible verbs, to be used to annotate that interaction. The proba- bility is learnt from crowdsourced annotations, and is tested on two public datasets, comprising 1405 video sequences for which we provide annotations on 90 verbs. We outper- form conventional single-label classification by 11% and 6% on the two datasets respectively, and show that learning from annotation probabilities outperforms majority voting and enables discovery of co-occurring labels.
The Emergence of Crowdsourcing among Pokémon Go Players

Since its launching, Pok{\'e}mon Go has been pointed as the largest gaming phenomenon of the smartphone age. As the game requires the user to walk in the real world to see and capture Pok{\'e}mons, a new wave of crowdsourcing apps have emerged to allow users to collaborate with each other, sharing where and when Pok{\'e}mons were found. In this paper we characterize one of such initiatives, called PokeCrew. Our analyses uncover a set of aspects of user behavior and system usage in such emerging crowdsourcing task, helping unveil some problems and benefits. We hope our effort can inspire the design of new crowdsourcing systems.
A Hybrid Deep Learning Approach for Texture Analysis

Texture classification is a problem that has variousapplications such as remote sensing and forest speciesrecogni- tion. Solutions tend to be custom fit to the datasetused but fails to generalize. The Convolutional NeuralNetwork (CNN) in combination with Support Vector Machine(SVM) form a robust selection between powerful invariantfeature extractor and accurate classifier. The fusion ofexperts provides stability in classification rates amongdifferent datasets.
Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot

Robot vision is a fundamental device for human-robot interaction and robot complex tasks. In this paper, we use Kinect and propose a feature graph fusion (FGF) for robot recognition. Our feature fusion utilizes RGB and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and utilize word embedding method to enhance the recognition results. We also collect DUT RGB-D face dataset and a benchmark datset to evaluate the effectiveness and efficiency of our method. The experimental results illustrate FGF is robust and effective to face and object datasets in robot applications.
DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills

Face recognition (FR) methods report significant performance by adopting the convolutional neural network (CNN) based learning methods. Although CNNs are mostly trained by optimizing the softmax loss, the recent trend shows an improvement of accuracy with different strategies, such as task-specific CNN learning with different loss functions, fine-tuning on target dataset, metric learning and concatenating features from multiple CNNs. Incorporating these tasks obviously requires additional efforts. Moreover, it demotivates the discovery of efficient CNN models for FR which are trained only with identity labels. We focus on this fact and propose an easily trainable and single CNN based FR method. Our CNN model exploits the residual learning framework. Additionally, it uses normalized features to compute the loss. Our extensive experiments show excellent generalization on different datasets. We obtain very competitive and state-of-the-art results on the LFW, IJB-A, YouTube faces and CACD datasets.
Reasoning by cases in structured argumentation.

We extend the ASPIC+ framework for structured argumentation so as to allow applications of the reasoning by cases inference scheme for defeasible arguments. Given an argument with conclusion 'A or B', an argument based on A with conclusion C, and an argument based on B with conclusion C, we allow the construction of an argument with conclusion C. Our framework leads to different results than other approaches in non-monotonic logic for dealing with disjunctive information, such as disjunctive default theory or approaches based on the OR-rule (which allows to derive a defeasible rule 'If (A or B) then C', given two defeasible rules 'If A then C' and 'If B then C'). We raise new questions regarding the subtleties of reasoning defeasibly with disjunctive information, and show that its formalization is more intricate than one would presume.
Combinatorial Ricci curvature on cell-complex and Gauss-Bonnnet theorem

In this paper we present the Ricci curvature on cell-complexes and show the Gauss-Bonnnet type theorem on graphs and 2-complex that decomposes closed surface. The defferential forms on a cell complex is defined as linear maps on chain complex, and Laplacian operates this defferential forms. Then we construct the Bochner-Weitzenbock formula and define the Ricci curvature. This curvature is determined by the combinatorial calculation. We show also the properties of combinatorial vector fields on a cell complex
Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop

Although we may complain about meetings, they are an essential part of an information worker's work life. Consequently, busy people spend a significant amount of time scheduling meetings. We present Calendar.help, a system that provides fast, efficient scheduling through structured workflows. Users interact with the system via email, delegating their scheduling needs to the system as if it were a human personal assistant. Common scheduling scenarios are broken down using well-defined workflows and completed as a series of microtasks that are automated when possible and executed by a human otherwise. Unusual scenarios fall back to a trained human assistant executing an unstructured macrotask. We describe the iterative approach we used to develop Calendar.help, and share the lessons learned from scheduling thousands of meetings during a year of real-world deployments. Our findings provide insight into how complex information tasks can be broken down into repeatable components that can be executed efficiently to improve productivity.
Exact electrodynamics versus standard optics for a slab of cold dense gas

We study light propagation through a slab of cold gas using both the standard electrodynamics of polarizable media, and massive atom-by-atom simulations of the electrodynamics. The main finding is that the predictions from the two methods may differ qualitatively when the density of the atomic sample $ and the wavenumber of resonant light $k$ satisfy $rho k^-3gtrsim 1$. The reason is that the standard electrodynamics is a mean-field theory, whereas for sufficiently strong light-mediated dipole-dipole interactions the atomic sample becomes correlated. The deviations from mean-field theory appear to scale with the parameter $rho k^-3$, and we demonstrate noticeable effects already at $rho k^-3 simeq 10^-2$. In dilute gases and in gases with an added inhomogeneous broadening the simulations show shifts of the resonance lines in qualitative agreement with the predicted Lorentz-Lorenz shift and "cooperative Lamb shift", but the quantitative agreement is unsatisfactory. Our interpretation is that the microscopic basis for the local-field corrections in electrodynamics is not fully understood.
Supersymmetric, fermionic solutions in three-dimensional supergravity

Building upon known supersymmetric backgrounds, we derive novel half-BPS fermionic solutions in three-dimensional supergravity. By virtue of an essential dependence on fermionic degrees of freedom, they possess no purely bosonic analogue. In the Anti de Sitter case this notably includes nonsingular solutions for which the corresponding Chern-Simons gauge field $\mathcal{A}=\omega\pm e/L$ vanishes, providing access to configurations which are ordinarily singular in pure gravity.
Overcoming Catastrophic Forgetting by Incremental Moment Matching

Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task. Here, we propose a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM incrementally matches the moment of the posterior distribution of the neural network which is trained on the first and the second task, respectively. To make the search space of posterior parameter smooth, the IMM procedure is complemented by various transfer learning techniques including weight transfer, L2-norm of the old and the new parameter, and a variant of dropout with the old parameter. We analyze our approach on a variety of datasets including the MNIST, CIFAR-10, Caltech-UCSD-Birds, and Lifelog datasets. The experimental results show that IMM achieves state-of-the-art performance by balancing the information between an old and a new network.
Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps between low-level features and high-level semantic concepts. In CBIR, the images are represented in the feature space and the performance of CBIR depends on the type of selected feature representation. Late fusion also known as visual words integration is applied to enhance the performance of image retrieval. The recent advances in image retrieval diverted the focus of research towards the use of binary descriptors as they are reported computationally efficient. In this paper, we aim to investigate the late fusion of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT). The late fusion of binary and local descriptor is selected because among binary descriptors, FREAK has shown good results in classification-based problems while SIFT is robust to translation, scaling, rotation and small distortions. The late fusion of FREAK and SIFT integrates the performance of both feature descriptors for an effective image retrieval. Experimental results and comparisons show that the proposed late fusion enhances the performances of image retrieval.
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection

In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursiveinput fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset.
Maximal representations of uniform complex hyperbolic lattices in exceptional Hermitian Lie groups

We complete the classification of maximal representations of uniform complex 
hyperbolic lattices in Hermitian Lie groups by dealing with the exceptional 
groups ${\rm E}_6$ and ${\rm E}_7$. We prove that if $\rho$ is a maximal 
representation of a uniform complex hyperbolic lattice $\Gamma\subset{\rm 
SU}(1,n)$, $n>1$, in an exceptional Hermitian group $G$, then $n=2$ and $G={\rm 
E}_6$, and we describe completely the representation $\rho$. The case of 
classical Hermitian target groups was treated by Vincent Koziarz and the second 
named author (). However we do not focus immediately on the 
exceptional cases and instead we provide a more unified perspective, as 
independent as possible of the classification of the simple Hermitian Lie 
groups. This relies on the study of the cominuscule representation of the 
complexification of the target group. As a by product of our methods, when the 
target Hermitian group $G$ has tube type, we obtain an inequality on the Toledo 
invariant of the representation $\rho:\Gamma\rightarrow G$ which is stronger 
than the Milnor-Wood inequality (thereby excluding maximal representations in 
such groups).
Interferometric constraints on quantum geometrical shear noise correlations

Final measurements and analysis are reported from the first-generation Holometer, the first instrument capable of measuring correlated variations in space-time position at strain noise power spectral densities smaller than a Planck time. The apparatus consists of two co-located, but independent and isolated, 40 m power-recycled Michelson interferometers, whose outputs are cross-correlated to 25 MHz. The data are sensitive to correlations of differential position across the apparatus over a broad band of frequencies up to and exceeding the inverse light crossing time, 7.6 MHz. By measuring with Planck precision the correlation of position variations at spacelike separations, the Holometer searches for faint, irreducible correlated position noise backgrounds predicted by some models of quantum space-time geometry. The first-generation optical layout is sensitive to quantum geometrical noise correlations with shear symmetry—those that can be interpreted as a fundamental noncommutativity of space-time position in orthogonal directions. General experimental constraints are placed on parameters of a set of models of spatial shear noise correlations, with a sensitivity that exceeds the Planck-scale holographic information bound on position states by a large factor. This result significantly extends the upper limits placed on models of directional noncommutativity by currently operating gravitational wave observatories.
Interactive natural language acquisition in a multi-modal recurrent neural architecture

For the complex human brain that enables us to communicate in natural language, we gathered good understandings of principles underlying language acquisition and processing, knowledge about sociocultural conditions, and insights into activity patterns in the brain. However, we were not yet able to understand the behavioural and mechanistic characteristics for natural language and how mechanisms in the brain allow to acquire and process language. In bridging the insights from behavioural psychology and neuroscience, the goal of this paper is to contribute a computational understanding of appropriate characteristics that favour language acquisition. Accordingly, we provide concepts and refinements in cognitive modelling regarding principles and mechanisms in the brain and propose a neurocognitively plausible model for embodied language acquisition from real-world interaction of a humanoid robot with its environment. In particular, the architecture consists of a continuous time recurrent neural network, where parts have different leakage characteristics and thus operate on multiple timescales for every modality and the association of the higher level nodes of all modalities into cell assemblies. The model is capable of learning language production grounded in both, temporal dynamic somatosensation and vision, and features hierarchical concept abstraction, concept decomposition, multi-modal integration, and self-organisation of latent representations.
Three-dimensional axisymmetric sources for Majumdar–Papapetrou type spacetimes

From Newtonian potential-density pairs, we construct three-dimensional axisymmetric relativistic sources for a Majumdar–Papapetrou type conformastatic spacetime. As simple examples, we build two families of relativistic thick disks from the first two Miyamoto–Nagai potential-density pairs used in Newtonian gravity to model flat galaxies, and a three-component relativistic model of galaxy (bulge, disk and dark matter halo). We study the equatorial circular motion of test particles around such structures. Also the stability of the orbits is analyzed for radial perturbation using an extension of the Rayleigh criterion. In all examples, the relativistic effects are analyzed and compared with the Newtonian approximation. The models are considered satisfying all the energy conditions.
Maxwell-Boltzmann type Hawking radiation

Twenty years ago, Rovelli proposed that the degeneracy of black hole (i.e. the exponential of the Bekenstein-Hawking entropy) is given by the number of ways the black hole horizon area can be expressed as a sum of unit areas. However, when counting the sum, one should treat the area quanta on the black hole horizon as distinguishable. This distinguishability of area quanta is noted in Rovelli's paper. Building on this idea, we derive that the Hawking radiation spectrum is not given by Planck radiation spectrum (i.e., Bose-Einstein distribution) but given by Maxwell-Boltzmann distribution.