nils-herrmann/clean_scirepeval huggingface dataset:
Low-Rank Tensor Approximation for Chebyshev Interpolation in Parametric Option Pricing

Treating high dimensionality is one of the main challenges in the development of computational methods for solving problems arising in finance, where tasks such as pricing, calibration, and risk assessment need to be performed accurately and in real-time. Among the growing literature addressing this problem, Gass et al.  propose a complexity reduction technique for parametric option pricing based on Chebyshev interpolation. As the number of parameters increases, however, this method is affected by the curse of dimensionality. In this article, we extend this approach to treat high-dimensional problems: Additionally exploiting low-rank structures allows us to consider parameter spaces of high dimensions. The core of our method is to express the tensorized interpolation in tensor train (TT) format and to develop an efficient way, based on tensor completion, to approximate the interpolation coefficients. We apply the new method to two model problems: American option pricing in the Heston model and European basket option pricing in the multi-dimensional Black-Scholes model. In these examples we treat parameter spaces of dimensions up to 25. The numerical results confirm the low-rank structure of these problems and the effectiveness of our method compared to advanced techniques.
Evaluating Older Users' Experiences with Commercial Dialogue Systems: Implications for Future Design and Development

Understanding the needs of a variety of distinct user groups is vital in designing effective, desirable dialogue systems that will be adopted by the largest possible segment of the population. Despite the increasing popularity of dialogue systems in both mobile and home formats, user studies remain relatively infrequent and often sample a segment of the user population that is not representative of the needs of the potential user population as a whole. This is especially the case for users who may be more reluctant adopters, such as older adults. 
In this paper we discuss the results of a recent user study performed over a large population of age 50 and over adults in the Midwestern United States that have experience using a variety of commercial dialogue systems. We show the common preferences, use cases, and feature gaps identified by older adult users in interacting with these systems. Based on these results, we propose a new, robust user modeling framework that addresses common issues facing older adult users, which can then be generalized to the wider user population.
Joint Training of Neural Network Ensembles

We examine the practice of joint training for neural network ensembles, in which a multi-branch architecture is trained via single loss. This approach has recently gained traction, with claims of greater accuracy per parameter along with increased parallelism. We introduce a family of novel loss functions generalizing multiple previously proposed approaches, with which we study theoretical and empirical properties of joint training. These losses interpolate smoothly between independent and joint training of predictors, demonstrating that joint training has several disadvantages not observed in prior work. However, with appropriate regularization via our proposed loss, the method shows new promise in resource limited scenarios and fault-tolerant systems, e.g., IoT and edge devices. Finally, we discuss how these results may have implications for general multi-branch architectures such as ResNeXt and Inception.
Extended 2D Consensus Hippocampus Segmentation.

Hippocampus segmentation plays a key role in diagnosing various brain disorders such as Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression and others. Nowadays, segmentation is still mainly performed manually by specialists. Segmentation done by experts is considered to be a gold-standard when evaluating automated methods, buts it is a time consuming and arduos task, requiring specialized personnel. In recent years, efforts have been made to achieve reliable automated segmentation. For years the best performing authomatic methods were multi atlas based with around 80-85% Dice coefficient and very time consuming, but machine learning methods are recently rising with promising time and accuracy performance. A method for volumetric hippocampus segmentation is presented, based on the consensus of tri-planar U-Net inspired fully convolutional networks (FCNNs), with some modifications, including residual connections, VGG weight transfers, batch normalization and a patch extraction technique employing data from neighbor patches. A study on the impact of our modifications to the classical U-Net architecture was performed. Our method achieves cutting edge performance in our dataset, with around 96% volumetric Dice accuracy in our test data. In a public validation dataset, HARP, we achieve 87.48% DICE. GPU execution time is in the order of seconds per volume, and source code is publicly available. Also, masks are shown to be similar to other recent state-of-the-art hippocampus segmentation methods in a third dataset, without manual annotations.
Evaluating Range Value at Risk Forecasts.

The debate of what quantitative risk measure to choose in practice has mainly focused on the dichotomy between Value at Risk (VaR) -- a quantile -- and Expected Shortfall (ES) -- a tail expectation. Range Value at Risk (RVaR) is a natural interpolation between these two prominent risk measures, which constitutes a tradeoff between the sensitivity of the latter and the robustness of the former, turning it into a practically relevant risk measure on its own. As such, there is a need to statistically validate RVaR forecasts and to compare and rank the performance of different RVaR models, tasks subsumed under the term 'backtesting' in finance. The predictive performance is best evaluated and compared in terms of strictly consistent loss or scoring functions. That is, functions which are minimised in expectation by the correct RVaR forecast. Much like ES, it has been shown recently that RVaR does not admit strictly consistent scoring functions, i.e., it is not elicitable. Mitigating this negative result, this paper shows that a triplet of RVaR with two VaR components at different levels is elicitable. We characterise the class of strictly consistent scoring functions for this triplet. Additional properties of these scoring functions are examined, including the diagnostic tool of Murphy diagrams. The results are illustrated with a simulation study, and we put our approach in perspective with respect to the classical approach of trimmed least squares in robust regression.
Fast-SCNN: Fast Semantic Segmentation Network

The encoder-decoder framework is state-of-the-art for offline semantic image segmentation. Since the rise in autonomous systems, real-time computation is increasingly desirable. In this paper, we introduce fast segmentation convolutional neural network (Fast-SCNN), an above real-time semantic segmentation model on high resolution image data (1024x2048px) suited to efficient computation on embedded devices with low memory. Building on existing two-branch methods for fast segmentation, we introduce our `learning to downsample' module which computes low-level features for multiple resolution branches simultaneously. Our network combines spatial detail at high resolution with deep features extracted at lower resolution, yielding an accuracy of 68.0% mean intersection over union at 123.5 frames per second on Cityscapes. We also show that large scale pre-training is unnecessary. We thoroughly validate our metric in experiments with ImageNet pre-training and the coarse labeled data of Cityscapes. Finally, we show even faster computation with competitive results on subsampled inputs, without any network modifications.
Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance

The production and consumption of information about Bitcoin and other digital-, or 'crypto'-, currencies have grown together with their market capitalisation. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages, and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analysing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than classic baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors.
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community. Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures. Our code, models, selfplay datasets, and auxiliary data are publicly available.
An alternative way to locally measure the Hubble constant using Gravitational Waves and Pulsar Timing Arrays

Recent research show that the cosmological components of the Universe should influence on the propagation of Gravitational Waves (GWs). Moreover, it has been proposed a new way to measure the cosmological constant using Pulsar Timing Arrays (PTAs). However, these results have considered very particular cases (e.g. a de Sitter Universe or a mixing with non-relativistic matter). In this work we propose an extension of these results, using the Hubble constant as the natural parameter that includes all the cosmological information, studying its consequences on the propagation of GWs and exploring the possibilities of a local measurement. From linearized gravity we consider a mixture of perfect fluids permeating the spacetime and study the propagation of GWs within the context of the LCDM model. Using this framework we found a relationship, e.g. an analytical expression and an approximation that differs by 1.5% from numerical simulations, between the local value of the Hubble constant and some observables of Pulsar Timing Array experiments. The results show that the value of the Hubble constant is relevant within the measurement of pulsars timing residual and the simulations show that, under the accuracy expected by the IPTA collaboration in the next years, this effect could be eventually observed.
Coherent coupling of single molecules to on-chip ring resonators

We report on cryogenic coupling of organic molecules to ring microresonators obtained by looping subwavelength waveguides (nanoguides). We discuss fabrication and characterization of the chip-based nanophotonic elements which yield a resonator finesse in the order of 20 when covered by molecular crystals. Our observed extinction dips from single molecules reach 22%, consistent with an expected enhancement factor of up to 11 for the molecular emission into the nanoguide. Future efforts will aim at efficient coupling of a handful of molecules via their interaction with a ring microresonator mode, setting the ground for the realization of quantum optical cooperative effects.
Neutrino physics with the PTOLEMY project: active neutrino properties and the light sterile case

The PTOLEMY project aims to develop a scalable design for a Cosmic Neutrino Background (CNB) detector, the first of its kind and the only one conceived that can look directly at the image of the Universe encoded in neutrino background produced in the first second after the Big Bang. The scope of the work for the next three years is to complete the conceptual design of this detector and to validate with direct measurements that the non-neutrino backgrounds are below the expected cosmological signal. In this paper we discuss in details the theoretical aspects of the experiment and its physics goals. In particular, we mainly address three issues. First we discuss the sensitivity of PTOLEMY to the standard neutrino mass scale. We then study the perspectives of the experiment to detect the CNB via neutrino capture on tritium as a function of the neutrino mass scale and the energy resolution of the apparatus. Finally, we consider an extra sterile neutrino with mass in the eV range, coupled to the active states via oscillations, which has been advocated in view of neutrino oscillation anomalies. This extra state would contribute to the tritium decay spectrum, and its properties, mass and mixing angle, could be studied by analyzing the features in the beta decay electron spectrum.
First results from the TNG50 simulation: galactic outflows driven by supernovae and black hole feedback

We present the new TNG50 cosmological, magnetohydrodynamical simulation – the third and final volume of the IllustrisTNG project. This simulation occupies a unique combination of large volume and high resolution, with a 50 Mpc box sampled by 21603 gas cells (baryon mass of 8 × 104 M⊙). The median spatial resolution of star-forming interstellar medium gas is ∼100−140 pc. This resolution approaches or exceeds that of modern ‘zoom’ simulations of individual massive galaxies, while the volume contains ∼20 000 resolved galaxies with $M_\star \gtrsim 10^7$ M⊙. Herein we show first results from TNG50, focusing on galactic outflows driven by supernovae as well as supermassive black hole feedback. We find that the outflow mass loading is a non-monotonic function of galaxy stellar mass, turning over and rising rapidly above 1010.5 M⊙ due to the action of the central black hole (BH). The outflow velocity increases with stellar mass, and at fixed mass it is faster at higher redshift. The TNG model can produce high-velocity, multiphase outflows that include cool, dense components. These outflows reach speeds in excess of 3000 km s−1 out to 20 kpc with an ejective, BH-driven origin. Critically, we show how the relative simplicity of model inputs (and scalings) at the injection scale produces complex behaviour at galactic and halo scales. For example, despite isotropic wind launching, outflows exhibit natural collimation and an emergent bipolarity. Furthermore, galaxies above the star-forming main sequence drive faster outflows, although this correlation inverts at high mass with the onset of quenching, whereby low-luminosity, slowly accreting, massive BHs drive the strongest outflows.
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning

We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect further evidence to help discriminate potential threats. The main technical challenges are the partial observability of the agent intent, the adversary modeling, and the corresponding uncertainty modeling. Note that an adversary agent may act to mislead the autonomous agent by using a deceptive strategy that is learned from past experiences. We propose an approach that combines belief space planning, generative adversary modeling, and maximum entropy reinforcement learning to obtain a stochastic belief space policy. By accounting for various adversarial behaviors in the simulation framework and minimizing the predictability of the autonomous agent’s action, the resulting policy is more robust to unmodeled adversarial strategies. This improved robustness is empirically shown against an adversary that adapts to and exploits the autonomous agent’s policy when compared with a standard Chance-Constraint Partially Observable Markov Decision Process robust approach.
Deep Convolutional Sum-Product Networks for Probabilistic Image Representations

Sum-Product Networks (SPNs) are hierarchical probabilistic graphical models capable of fast and exact inference. Applications of SPNs to real-world data such as large image datasets has been fairly limited in previous literature. We introduce Convolutional Sum-Product Networks (ConvSPNs) which exploit the inherent structure of images in a way similar to deep convolutional neural networks, optionally with weight sharing. ConvSPNs encode spatial relationships through local products and local sum operations. ConvSPNs obtain state-of-the-art results compared to other SPN-based approaches on several visual datasets, including color images, for both generative as well as discriminative tasks. ConvSPNs are the first pure-SPN models applied to color images that do not depend on additional techniques for feature extraction. In addition, we introduce two novel methods for regularizing SPNs trained with hard EM. Both regularization methods have been motivated by observing an exponentially decreasing variance of log probabilities with respect to the depth of randomly structured SPNs. We show that our regularization provides substantial further improvements in generative visual tasks.
Reactive, Proactive, and Inductive Agents: An Evolutionary Path for Biological and Artificial Spiking Networks

Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of new stimuli, and act on these predictions. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior. Based on earlier in-vitro and in-silico experiments, we define the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior. Our results support the existence of specific evolutionary steps and four conditions necessary for embodied neural networks to evolve predictive and inductive abilities from an initial reactive strategy.
Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction

We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image. This challenging task requires inferring the shape of both visible and occluded surfaces. Our approach utilizes viewer-centered, multi-layer representation of scene geometry adapted from recent methods for single object shape completion. To improve the accuracy of view-centered representations for complex scenes, we introduce a novel "Epipolar Feature Transformer" that transfers convolutional network features from an input view to other virtual camera viewpoints, and thus better covers the 3D scene geometry. Unlike existing approaches that first detect and localize objects in 3D, and then infer object shape using category-specific models, our approach is fully convolutional, end-to-end differentiable, and avoids the resolution and memory limitations of voxel representations. We demonstrate the advantages of multi-layer depth representations and epipolar feature transformers on the reconstruction of a large database of indoor scenes.
First Identification of 10 kpc [C ii] 158 μm Halos around Star-forming Galaxies at z = 5–7

We report the discovery of 10 kpc [C ii] 158 μm halos surrounding star-forming galaxies in the early universe. We choose deep Atacama Large Millimeter/submillimeter Array data for 18 galaxies, each with a star formation rate of ≃10–70 M⊙ with no signature of an active galactic nucleus whose [C ii] lines are individually detected at z = 5.153–7.142, and we conduct stacking of the [C ii] lines and dust continuum in the uv-visibility plane. The radial profiles of the surface brightnesses show a 10 kpc scale [C ii] halo at the 9.2σ level, significantly more extended than the Hubble Space Telescope stellar continuum data by a factor of ∼5 on the exponential-profile basis, as well as the dust continuum. We compare the radial profiles of [C ii] and Lyα halos universally found in star-forming galaxies at this epoch, and we find that the scale lengths agree within the 1σ level. While two independent hydrodynamic zoom-in simulations match the dust and stellar continuum properties, the simulations cannot reproduce the extended [C ii] line emission. The existence of the extended [C ii] halo is evidence of outflow remnants in the early galaxies and suggests that the outflows may be dominated by cold-mode outflows expelling the neutral gas.
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing

Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited for this task, being a model-free measure that captures nonlinear and lagged dependencies between time series to infer a minimal directed network model. Greedy algorithms have been proposed to efficiently deal with high-dimensional datasets while avoiding redundant inferences and capturing synergistic effects. However, multiple statistical comparisons may inflate the false positive rate and are computationally demanding, which limited the size of previous validation studies. The algorithm we present—as implemented in the IDTxl open-source software—addresses these challenges by employing hierarchical statistical tests to control the family-wise error rate and to allow for efficient parallelization. The method was validated on synthetic datasets involving random networks of increasing size (up to 100 nodes), for both linear and nonlinear dynamics. The performance increased with the length of the time series, reaching consistently high precision, recall, and specificity (>98% on average) for 10,000 time samples. Varying the statistical significance threshold showed a more favorable precision-recall trade-off for longer time series. Both the network size and the sample size are one order of magnitude larger than previously demonstrated, showing feasibility for typical EEG and magnetoencephalography experiments.