nils-herrmann/clean_scirepeval huggingface dataset:
Panoptic Edge Detection

Pursuing more complete and coherent scene understanding towards realistic vision applications drives edge detection from category-agnostic to category-aware semantic level. However, finer delineation of instance-level boundaries still remains unexcavated. In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges.We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches. Based on the same feature representation, the semantic edge branch produces semantic-level boundaries for all categories and the object detection branch generates instance proposals. Conditioned on the prior information from these two branches, the instance edge branch aims at instantiating edge predictions for instance categories. Besides, we also devise a Panoptic Dual F-measure (F2) metric for the new PED task to uniformly measure edge prediction quality for both stuff and instances. By joint end-to-end training, the proposed PEN framework outperforms all competitive baselines on Cityscapes and ADE20K datasets.
Assessing the Ability of Self-Attention Networks to Learn Word Order

Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization and strong performance on a variety of NLP tasks, e.g. machine translation. Due to the lack of recurrence structure such as recurrent neural networks (RNN), SAN is ascribed to be weak at learning positional information of words for sequence modeling. However, neither this speculation has been empirically confirmed, nor explanations for their strong performances on machine translation tasks when “lacking positional information” have been explored. To this end, we propose a novel word reordering detection task to quantify how well the word order information learned by SAN and RNN. Specifically, we randomly move one word to another position, and examine whether a trained model can detect both the original and inserted positions. Experimental results reveal that: 1) SAN trained on word reordering detection indeed has difficulty learning the positional information even with the position embedding; and 2) SAN trained on machine translation learns better positional information than its RNN counterpart, in which position embedding plays a critical role. Although recurrence structure make the model more universally-effective on learning word order, learning objectives matter more in the downstream tasks such as machine translation.
Twisted Representations of Algebra of q-Difference Operators, Twisted q-W Algebras and Conformal Blocks

We study certain representations of quantum toroidal $\mathfrak{gl}_1$ algebra for $q=t$. We construct explicit bosonization of the Fock modules $\mathcal{F}_u^{(n',n)}$ with a nontrivial slope $n'/n$. As a vector space, it is naturally identified with the basic level 1 representation of affine $\mathfrak{gl}_n$. We also study twisted $W$-algebras of $\mathfrak{sl}_n$ acting on these Fock modules. As an application, we prove the relation on $q$-deformed conformal blocks which was conjectured in the study of $q$-deformation of isomonodromy/CFT correspondence.
Reasoning about disclosure in data integration in the presence of source constraints

Data integration systems allow users to access data sitting in multiple sources by means of queries over a global schema, related to the sources via mappings. Datasources often contain sensitive information, and thus an analysis is needed to verify that a schema satisfies a privacy policy, given as a set of queries whose answers should not be accessible to users. Such an analysis should take into account not only knowledge that an attacker may have about the mappings, but also what they may know about the semantics of the sources.In this paper, we show that source constraints can have a dramatic impact on disclosure analysis. We study the problem of determining whether a given data integration system discloses a source query to an attacker in the presence of constraints, providing both lower and upper bounds on source-aware disclosure analysis.
Relation Embedding with Dihedral Group in Knowledge Graph

Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks. As a family of effective approaches for link predictions, embedding methods try to learn low-rank representations for both entities and relations such that the bilinear form defined therein is a well-behaved scoring function. Despite of their successful performances, existing bilinear forms overlook the modeling of relation compositions, resulting in lacks of interpretability for reasoning on KG. To fulfill this gap, we propose a new model called DihEdral, named after dihedral symmetry group. This new model learns knowledge graph embeddings that can capture relation compositions by nature. Furthermore, our approach models the relation embeddings parametrized by discrete values, thereby decrease the solution space drastically. Our experiments show that DihEdral is able to capture all desired properties such as (skew-) symmetry, inversion and (non-) Abelian composition, and outperforms existing bilinear form based approach and is comparable to or better than deep learning models such as ConvE.
Continual learning with hypernetworks

Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. To overcome this problem, we present a novel approach based on task-conditioned hypernetworks, i.e., networks that generate the weights of a target model based on task identity. Continual learning (CL) is less difficult for this class of models thanks to a simple key feature: instead of recalling the input-output relations of all previously seen data, task-conditioned hypernetworks only require rehearsing task-specific weight realizations, which can be maintained in memory using a simple regularizer. Besides achieving state-of-the-art performance on standard CL benchmarks, additional experiments on long task sequences reveal that task-conditioned hypernetworks display a very large capacity to retain previous memories. Notably, such long memory lifetimes are achieved in a compressive regime, when the number of trainable hypernetwork weights is comparable or smaller than target network size. We provide insight into the structure of low-dimensional task embedding spaces (the input space of the hypernetwork) and show that task-conditioned hypernetworks demonstrate transfer learning. Finally, forward information transfer is further supported by empirical results on a challenging CL benchmark based on the CIFAR-10/100 image datasets.
Evaluating network partitions through visualization

Network clustering requires making many decisions manually, such as the number of groups and a statistical model to be used. Even after filtering using an information criterion or regularizing with a nonparametric framework, we are commonly left with multiple candidates with reasonable partitions. In the end, the user has to decide which inferred groups should be regarded as informative. Here we propose a visualization method that efficiently represents network partitioning based on statistical inference algorithms. Our non-statistical assessment procedure based on visualization helps users extract informative groups when they cannot uniquely determine significant groups on the basis of statistical assessments. The proposed visualization is also effective for use as a benchmark test of different clustering algorithms.
Parameterised Complexity for Abduction

Abductive reasoning is a non-monotonic formalism stemming from the work of Peirce. It describes the process of deriving the most plausible explanations of known facts. Considering the positive version asking for sets of variables as explanations, we study, besides asking for existence of the set of explanations, two explanation size limited variants of this reasoning problem (less than or equal to, and equal to). In this paper, we present a thorough classification regarding the parameterised complexity of these problems under a wealth of different parameterisations. Furthermore, we analyse all possible Boolean fragments of these problems in the constraint satisfaction approach with co-clones. Thereby, we complete the parameterised picture started by Fellows et al. (AAAI 2012), partially building on results of Nordh and Zanuttini ( 2008). In this process, we outline a fine-grained analysis of the inherent intractability of these problems and pinpoint their tractable parts.
Observations of zero electrical resistance of Au-Ag thin films near room temperature.

Recent observations of superconducting like transition at 286 K in Ag and Ag nanostructures by Thapa et al.  have rekindled the hope for room temperature superconductivity under ambient conditions. We also investigated the electrical properties of Ag-Au nanostructure in the form of thin film grown on SiO2/Si substrate by DC sputtering and observed signature of zero resistance in the temperatures range of 243 K to 275 K. While the observed electrical resistance of samples shows intriguing and perplexing behavior under temperature cycling and external magnetic field; the large spatial inhomogeneity present in thin film hampers the reproducibility indicating the stability issues associated with superconducting like phase.
Context-enriched identification of particles with a convolutional network for neutrino events

Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they travel through matter, the full context of the interaction in which they are produced can aid the classification task substantially. We have developed the first convolutional neural network for particle identification which uses context information. This is also the first implementation of a four-tower siamese-type architecture both for separation of independent inputs and inclusion of context information. The network classifies clusters of energy deposits from the NOvA neutrino detectors as electrons, muons, photons, pions, and protons with an overall efficiency and purity of 83.3% and 83.5%, respectively. We show that providing the network with context information improves performance by comparing our results with a network trained without context information.
The cross-correlation between 21cm and CMB B modes from the cosmic birefringence in existence of light scalar field

We study the cross-correlation between the 21cm and CMB B mode fluctuations which are induced by the cosmic birefringence when the non-constant scalar field couples to the electromagnetic field strength. Such multi-wavelength signals can potentially probe the reionization history of the Universe and also explore the nature of fundamental physics such as the parity violation and the scalar field dynamics in the early Universe. We illustrate the feasibility to detect such 21cm-B mode cross-correlations through commonly discussed scalar field models, the quintessence-like scalar field which is responsible for the current dark energy and the axion-like scalar field which is responsible for the current dark matter density.
Impact of kinetic and potential self-interactions on scalar dark matter

We consider models of scalar dark matter with a generic interaction potential and noncanonical kinetic terms of the K-essence type that are subleading with respect to the canonical term. We analyze the low-energy regime and derive, in the nonrelativistic limit, the effective equations of motions. In the fluid approximation they reduce to the conservation of matter and to the Euler equation for the velocity field. We focus on the case where the scalar field mass 10-21 << m less than or similar to 10(-4) eV is much larger than for fuzzy dark matter, so that the quantum pressure is negligible on cosmological and galactic scales, while the self-interaction potential and noncanonical kinetic terms generate a significant repulsive pressure. At the level of cosmological perturbations, this provides a dark-matter density-dependent speed of sound. At the nonlinear level, the hydrostatic equilibrium obtained by balancing the gravitational and scalar interactions imply that virialized structures have a solitonic core of finite size depending on the speed of sound of the dark matter fluid. For the most relevant potential in. lambda(4)phi(4)/4 or K-essence with a (partial derivative phi)(4) interaction, the size of such stable cores cannot exceed 60 kpc. Structures with a density contrast larger than 106 can be accommodated with a speed of sound c(s) less than or similar to 10(-6). We also consider the case of a cosine self-interaction, as an example of bounded nonpolynomial self-interaction. This gives similar results in low-mass and low-density halos whereas solitonic cores are shown to be absent in massive.
The Strength of Structural Diversity in Online Social Networks

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one's online social reputation. To capture the structural diversity of an individual, we first consider the number of weakly and strongly connected components in one's contact neighborhood and further take the coexposure network of social neighbors into consideration. We show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation, and the inclusion of a coexposure network provides an additional ingredient to achieve that goal. After synthetically controlling several possible confounding factors through matching experiments, structural diversity still plays a nonnegligible role in the prediction of personal online social reputation. Our work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains, such as social influence and collective intelligence studies.
Behavior of information flow near criticality.

Recent experiments have indicated that many biological systems self-organize near their critical point, which hints at a common design principle. While it has been suggested that information transmission is optimized near the critical point, it remains unclear how information transmission depends on the dynamics of the input signal, the distance over which the information needs to be transmitted, and the distance to the critical point. Here we employ stochastic simulations of a driven two-dimensional Ising system and study the instantaneous mutual information and the information transmission rate between a driven input spin and an output spin. The instantaneous mutual information varies nonmonotonically with the temperature but increases monotonically with the correlation time of the input signal. In contrast, there exists not only an optimal temperature but also an optimal finite input correlation time that maximizes the information transmission rate. This global optimum arises from a fundamental trade-off between the need to maximize the frequency of independent input messages, the necessity to respond fast to changes in the input, and the need to respond reliably to these changes. The optimal temperature lies above the critical point but moves toward it as the distance between the input and output spin is increased.
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion

End-to-end models for raw audio generation are a challenge, specially if they have to work with non-parallel data, which is a desirable setup in many situations. Voice conversion, in which a model has to impersonate a speaker in a recording, is one of those situations. In this paper, we propose Blow, a single-scale normalizing flow using hypernetwork conditioning to perform many-to-many voice conversion between raw audio. Blow is trained end-to-end, with non-parallel data, on a frame-by-frame basis using a single speaker identifier. We show that Blow compares favorably to existing flow-based architectures and other competitive baselines, obtaining equal or better performance in both objective and subjective evaluations. We further assess the impact of its main components with an ablation study, and quantify a number of properties such as the necessary amount of training data or the preference for source or target speakers.
Global Mirrors and Discrepant Transformations for Toric Deligne-Mumford Stacks

We introduce a global Landau-Ginzburg model which is mirror to several toric Deligne-Mumford stacks and describe the change of the Gromov-Witten theories under discrepant transformations. We prove a formal decomposition of the quantum cohomology D-modules (and of the all-genus Gromov-Witten potentials) under a discrepant toric wall-crossing. In the case of weighted blowups of weak-Fano compact toric stacks along toric centres, we show that an analytic lift of the formal decomposition corresponds, via the $\widehat{\Gamma}$-integral structure, to an Orlov-type semiorthogonal decomposition of topological $K$-groups. We state a conjectural functoriality of Gromov-Witten theories under discrepant transformations in terms of a Riemann-Hilbert problem.
How Does Active Participation Affect Consensus: Adaptive Network Model of Opinion Dynamics and Influence Maximizing Rewiring

In this paper we study the impact of active participation, or deliberately seeking out other agents with an aim of convincing them, on the dynamics of consensus formation. For this purpose, we propose an adaptive network model in which two processes shape opinion dynamics at interwoven time-scales as follows: (i) agents adapt their opinions subject to influence from social network neighbours who hold opinions within a tolerance interval δ and (ii) agents rewire network connections with an aim of maximizing their own influence on overall system opinion. We study this system both in an endogenous setting, in which all agents are subject to influence and also attempt to maximize influence, and in a setting of exogenous control, in which external agents not subject to influence adaptively attempt to maximize their influence. In both settings we find three regimes of stationary opinion configurations: (i) for low δ a regime of two evenly balanced radicalized opinion clusters at the extremes of the opinion space, (ii) for intermediate δ a ’winner-takes-most’ regime of two unevenly sized radicalized opinion clusters, and (iii) for large δ a regime in which very low spread compromise consensus states can be reached. Comparing to adaptive processes of random and deliberately spread-reducing rewiring, we demonstrate that in regime (iii) competitive influence maximization can achieve near-minimal opinion spread within near-optimal times. Further, we also show that competitive influence maximizing rewiring can reduce the impact of small influential minorities on consensus states.
Fashion Editing with Multi-scale Attention Normalization

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreover, they directly utilize conventional convolution and normalization layers to restore the incomplete image, which tends to wash away the sketch and color information. In this paper, we propose a novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes. FE-GAN consists of two modules: 1) a free-form parsing network that learns to control the human parsing generation by manipulating sketch and color; 2) a parsing-aware inpainting network that renders detailed textures with semantic guidance from the human parsing map. A new attention normalization layer is further applied at multiple scales in the decoder of the inpainting network to enhance the quality of the synthesized image. Extensive experiments on high-resolution fashion image datasets demonstrate that the proposed method significantly outperforms the state-of-the-art methods on image manipulation.
In-flight photometry extraction of PLATO targets

The ESA PLATO space mission is devoted to unveiling and characterizing new extrasolar planets and their host stars. This mission will encompass a very large (>2100 deg2) field of view, granting it the potential to survey up to one million stars depending on the final observation strategy. The telemetry budget of the spacecraft cannot handle transmitting individual images for such a huge stellar sample at the right cadence, so the development of an appropriate strategy to perform on-board data reduction is mandatory.
Aims. We employ mask-based (aperture) photometry to produce stellar light curves in flight. Our aim is thus to find the mask model that optimizes the scientific performance of the reduced data.
Methods. We considered three distinct aperture models: binary mask, weighted Gaussian mask, and weighted gradient mask giving lowest noise-to-signal ratio, computed through a novel direct method. Each model was tested on synthetic images generated for 50 000 potential PLATO targets. We extracted the stellar population from the Gaia DR2 catalogue. An innovative criterion was adopted for choosing between different mask models. We designated as optimal the model providing the best compromise between sensitivity to detect true and false planet transits. We determined the optimal model based on simulated noise-to-signal ratio and frequency of threshold crossing events.
Results. Our results show that, although the binary mask statistically presents a few percent higher noise-to-signal ratio compared to weighted masks, both strategies have very similar efficiency in detecting legitimate planet transits. When it comes to avoiding spurious signals from contaminant stars however the binary mask statistically collects considerably less contaminant flux than weighted masks, thereby allowing the former to deliver up to ∼30% less false transit signatures at 7.1σ detection threshold.
Conclusions. Our proposed approach for choosing apertures has been proven to be decisive for the determination of a mask model capable to provide near maximum planet yield and substantially reduced occurrence of false positives for the PLATO mission. Overall, this work constitutes an important step in the design of both on-board and on-ground science data processing pipelines.