nils-herrmann/clean_scirepeval huggingface dataset:
Anomalous Diffusions in Option Prices: Connecting Trade Duration and the Volatility Term Structure

Anomalous diffusions arise as scaling limits of continuous-time random walks (CTRWs) whose innovation times are distributed according to a power law. The impact of a non-exponential waiting time does not vanish with time and leads to different distribution spread rates compared to standard models. In financial modelling this has been used to accommodate for random trade duration in the tick-by-tick price process. We show here that anomalous diffusions are able to reproduce the market behaviour of the implied volatility more consistently than usual Levy or stochastic volatility models. We focus on two distinct classes of underlying asset models, one with independent price innovations and waiting times, and one allowing dependence between these two components. These two models capture the well-known paradigm according to which shorter trade duration is associated with higher return impact of individual trades. We fully describe these processes in a semimartingale setting leading no-arbitrage pricing formulae, and study their statistical properties. We observe that skewness and kurtosis of the asset returns do not tend to zero as time goes by. We also characterize the large-maturity asymptotics of Call option prices, and find that the convergence rate is slower than in standard Levy regimes, which in turn yields a declining implied volatility term structure and a slower decay of the skew.
Application of independent component analysis to the iKAGRA data

We apply the independent component analysis (ICA) to the real data from a gravitational wave detector for the first time. Specifically we use the iKAGRA data taken in April 2016, and calculate the correlations between the gravitational wave strain channel and 35 physical environmental channels. Using a couple of seismic channels which are found to be strongly correlated with the strain, we perform ICA. Injecting a sinusoidal continuous signal in the strain channel, we find that ICA recovers correct parameters with enhanced signal-to-noise ratio, which demonstrates usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal result for the case environmental noises act on the strain channel linearly.
Measurable Counterfactual Local Explanations for Any Classifier

We propose a novel method for explaining the predictions of any classifier. In our approach, local explanations are expected to explain both the outcome of a prediction and how that prediction would change if 'things had been different'. Furthermore, we argue that satisfactory explanations cannot be dissociated from a notion and measure of fidelity, as advocated in the early days of neural networks' knowledge extraction. We introduce a definition of fidelity to the underlying classifier for local explanation models which is based on distances to a target decision boundary. A system called CLEAR: Counterfactual Local Explanations via Regression, is introduced and evaluated. CLEAR generates w-counterfactual explanations that state minimum changes necessary to flip a prediction's classification. CLEAR then builds local regression models, using the w-counterfactuals to measure and improve the fidelity of its regressions. By contrast, the popular LIME method, which also uses regression to generate local explanations, neither measures its own fidelity nor generates counterfactuals. CLEAR's regressions are found to have significantly higher fidelity than LIME's, averaging over 45% higher in this paper's four case studies.
Kinetic freeze-out in central heavy-ion collisions between 7.7 and 2760 GeV per nucleon pair

We fit the single-particle $p_t$ spectra of identified pions, kaons, and (anti)protons from central collisions of gold or lead nuclei at energies between 7.7 and 2760~GeV per nucleon pair. Blast wave model with included resonance production and with an assumption of partial chemical equilibrium is used and the fits are performed with the help of a Gaussian emulator process. A kinetic freeze-out temperature is found about 100~MeV for the lowest collision energies and 80~MeV at the LHC. The average transverse expansion velocity grows with increasing $\sqrt{s_{NN}}$ from 0.45 to 0.65. Due to partial chemical equilibrium, the influence of resonance decays on the shape of the $p_t$ spectra for $\sqrt{s_{NN}}$ above 27~GeV is small.
Exploring the M-dwarf Luminosity–Temperature–Radius relationships using Gaia DR2

There is growing evidence that M-dwarf stars suffer radius inflation when compared to theoretical models, suggesting that models are missing some key physics required to completely describe stars at effective temperatures less than about 4000 K. The advent of Gaia DR2 distances finally makes available large data sets to determine the nature and extent of this effect. We employ an all-sky sample, comprising of >15 000 stars, to determine empirical relationships between luminosity, temperature, and radius. This is accomplished using only geometric distances and multiwave-band photometry, by utilizing a modified spectral energy distribution fitting method. The radii we measure show an inflation of $3\!-\!7{{\ \rm per\ cent}}$ compared to models, but no more than a $1\!-\!2{{\ \rm per\ cent}}$ intrinsic spread in the inflated sequence. We show that we are currently able to determine M-dwarf radii to an accuracy of $2.4{{\ \rm per\ cent}}$ using our method. However, we determine that this is limited by the precision of metallicity measurements, which contribute $1.7{{\ \rm per\ cent}}$ to the measured radius scatter. We also present evidence that stellar magnetism is currently unable to explain radius inflation in M-dwarfs.
Branching rules for cell modules on a tower of the partition algebras

Partition algebras with non-zero parameters are cellularly stratified and thus have the features of both cellular algebras and stratified algebras. Also, partition algebras form a tower of algebras. In this paper, we provide a diagrammatic approach to study the branching rules for cell modules on a tower of the partition algebras. This also allows us to calculate the structure constants of the Grothendieck ring of the tower.
Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras

We present an approach to accurately estimate high fidelity markerless 3D pose and volumetric reconstruction of human performance using only a small set of camera views ($\sim 2$). Our method utilises a dual loss in a generative adversarial network that can yield improved performance in both reconstruction and pose estimate error. We use a deep prior implicitly learnt by the network trained over a dataset of view-ablated multi-view video footage of a wide range of subjects and actions. Uniquely we use a multi-channel symmetric 3D convolutional encoder-decoder with a dual loss to enforce the learning of a latent embedding that enforces skeletal joint positions and a deep volumetric reconstruction of the performer. An extensive evaluation is performed with state of the art performance reported on three datasets; Human 3.6M, TotalCapture and TotalCaptureOutdoor. The method opens the possibility of high-end volumetric and pose performance capture in on-set and prosumer scenarios where time or cost prohibit a high witness camera count.
Two phase free boundary problem for Poisson kernels

We provide a potential theoretic characterization of vanishing chord-arc domains under minimal assumptions. In particular we show that, in the appropriate class of domains, the oscillation of the logarithm of the interior and exterior Poisson kernels yields a great deal of geometric information about the domain. We use techniques from the classical calculus of variations, potential theory, quantitative geometric measure theory to accomplish this. A striking feature of this work is that we make (almost) no a priori topological assumptions on our domain in contrast with Bortz-Hofmann PAMS 16 and Kenig-Toro Crelle 06.
Exponential polynomials with Fatou and non-escaping sets of finite Lebesgue measure

We give conditions ensuring that the Fatou set and the complement of the fast escaping set of an exponential polynomial f both have finite Lebesgue measure. Essentially, these conditions are designed such that 
$|f(z)|\ge \exp (|z|^\alpha )$
 for some 
$\alpha>0$
 and all z outside a set of finite Lebesgue measure.
Experimental determination of proton hardness factors at several irradiation facilities

The scheduled High Luminosity upgrade of the CERN Large Hadron Collider presents new challenges in terms of radiation hardness. As a consequence, campaigns to qualify the radiation hardness of detector sensors and components are undertaken worldwide. The effects of irradiation with beams of different particle species and energy, aiming to assess displacement damage in semiconductor devices, are communicated in terms of the equivalent 1 MeV neutron fluence, using the hardness factor for the conversion. In this work, the hardness factors for protons at three different kinetic energies have been measured by analysing the I–V and C–V characteristics of reverse biased diodes, pre- and post-irradiation. The sensors were irradiated at the MC40 Cyclotron of the University of Birmingham, the cyclotron at the Karlsruhe Institute of Technology, and the IRRAD proton facility at CERN, with the respective measured proton hardness factors being: 2.1± 0.5 for 24 MeV, 2.2 ± 0.4 for 23 MeV, and 0.62± 0.04 for 23 GeV. The hardness factors currently used in these three facilities are in agreement with the presented measurements.
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

Anomaly detection plays in many fields of research, along with the strongly related task of outlier detection, a very important role. Especially within the context of the automated analysis of video material recorded by surveillance cameras, abnormal situations can be of very different nature. For this purpose this work investigates Generative-Adversarial-Network-based methods (GAN) for anomaly detection related to surveillance applications. The focus is on the usage of static camera setups, since this kind of camera is one of the most often used and belongs to the lower price segment. In order to address this task, multiple subtasks are evaluated, including the influence of existing optical flow methods for the incorporation of short-term temporal information, different forms of network setups and losses for GANs, and the use of morphological operations for further performance improvement. With these extension we achieved up to 2.4% better results. Furthermore, the final method reduced the anomaly detection error for GAN based methods by about 42.8%.
Sim-to-Real Learning for Casualty Detection from Ground Projected Point Cloud Data

This paper addresses the problem of human body detection—particularly a human body lying on the ground (a.k.a. casualty)—using point cloud data. This ability to detect a casualty is one of the most important features of mobile rescue robots, in order for them to be able to operate autonomously. We propose a deep-learning-based casualty detection method using a deep convolutional neural network (CNN). This network is trained to be able to detect a casualty using a point-cloud data input. In the method we propose, the point cloud input is pre-processed to generate a depth image-like ground-projected heightmap. This heightmap is generated based on the projected distance of each point onto the detected ground plane within the point cloud data. The generated heightmap—in image form—is then used as an input for the CNN to detect a human body lying on the ground. To train the neural network, we propose a novel sim-to-real approach, in which the network model is trained using synthetic data obtained in simulation and then tested on real sensor data. To make the model transferable to real data implementations, during the training we adopt specific data augmentation strategies with the synthetic training data. The experimental results show that data augmentation introduced during the training process is essential for improving the performance of the trained model on real data. More specifically, the results demonstrate that the data augmentations on raw point-cloud data have contributed to a considerable improvement of the trained model performance.
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation

Table-to-text generation aims to translate the structured data into the unstructured text. Most existing methods adopt the encoder-decoder framework to learn the transformation, which requires large-scale training samples. However, the lack of large parallel data is a major practical problem for many domains. In this work, we consider the scenario of low resource table-to-text generation, where only limited parallel data is available. We propose a novel model to separate the generation into two stages: key fact prediction and surface realization. It first predicts the key facts from the tables, and then generates the text with the key facts. The training of key fact prediction needs much fewer annotated data, while surface realization can be trained with pseudo parallel corpus. We evaluate our model on a biography generation dataset. Our model can achieve 27.34 BLEU score with only 1,000 parallel data, while the baseline model only obtain the performance of 9.71 BLEU score.
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning

Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper addresses the memory capacity and bandwidth challenges of embedding layers and the associated tensor operations. We present our vertically integrated hardware/software co-design, which includes a custom DIMM module enhanced with near-memory processing cores tailored for DL tensor operations. These custom DIMMs are populated inside a GPU-centric system interconnect as a remote memory pool, allowing GPUs to utilize for scalable memory bandwidth and capacity expansion. A prototype implementation of our proposal on real DL systems shows an average 6.2-17.6× performance improvement on state-of-the-art DNN-based recommender systems.
Tip of the red giant branch distance to the nearby dwarf galaxy [TT2009] 25 in the NGC 891 group

Dwarf galaxies are key objects for small-scale cosmological tests like the abundance problems or the planes-of-satellites problem. It is therefore a crucial task to get accurate information for as many nearby dwarf galaxies as possible. Using extremely deep, ground-based $V$ and $i$-band Subaru Suprime Cam photometry with a completeness of $i=27$ mag, we measure the tip of the red giant branch distance for the dwarf galaxy [TT2009] 25. This dwarf resides in the field around the Milky Way-analog NGC 891. By using a Bayesian approach, we measure a distance of $10.28^{+1.17}_{-1.73}$ Mpc, which is consistent with the distance of NGC 891, thus we confirm it as a member of NGC 891. The dwarf galaxy follows the scaling relations defined by the Local Group dwarfs. We do not find an extended stellar halo around [TT2009] 25. In the small field of view of 100 kpc covered by the survey, only one bright dwarf galaxy and the giant stream are apparent. This is comparable to the Milky Way, where one bright dwarfs reside in the same volume, as well as the Sagittarius stream - excluding satellites which are farther away but would be projected in the line-of-sight. It is thus imperative to survey for additional dwarf galaxies in a larger area around NGC 891 to test the abundance of dwarf galaxies and compare it to the number of satellites around the Milky Way.
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints

Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore, efficient algorithms for solving such SOECs need to limit the fraction of data points that they use, which we refer to as algorithmic data complexity. Recent stochastic first order methods exhibit low data complexity when handling SOECs but guarantee near-feasibility and near-optimality only at convergence. These methods may thus return highly infeasible solutions when heuristically terminated, as is often the case, due to theoretical convergence criteria being highly conservative. This issue limits the use of first order methods in several applications where the SOEC constraints encode implementation requirements. We design a stochastic feasible level set method (SFLS) for SOECs that has low data complexity and emphasizes feasibility before convergence. Specifically, our level-set method solves a root-finding problem by calling a novel first order oracle that computes a stochastic upper bound on the level-set function by extending mirror descent and online validation techniques. We establish that SFLS maintains a high-probability feasible solution at each root-finding iteration and exhibits favorable iteration complexity compared to state-of-the-art deterministic feasible level set and stochastic subgradient methods. Numerical experiments on three diverse applications validate the low data complexity of SFLS relative to the former approach and highlight how SFLS finds feasible solutions with small optimality gaps significantly faster than the latter method.
ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules

Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset of the object segmentation problem. bviously, portrait segmentation has its unique requirements. First, because the portrait segmentation is performed in the middle of a whole process of many realworld applications, it requires extremely lightweight models. Second, there has not been any public datasets in this domain that contain a sufficient number of images with unbiased statistics. To solve the problems, we introduce a new extremely lightweight portrait segmentation model consisting of a two-branched architecture based on the concentrated-comprehensive convolutions block. Our method reduces the number of parameters from 2.1M to 37.7K (around 98.2% reduction), while maintaining the accuracy within a 1% margin from the state-of-the-art portrait segmentation method. In our qualitative and quantitative analysis on the EG1800 dataset, we show that our method outperforms various existing lightweight segmentation models. Second, we propose a simple method to create additional portrait segmentation data which can improve accuracy on the EG1800 dataset. Also, we analyze the bias in public datasets by additionally annotating race, gender, and age on our own. The augmented dataset, the additional annotations and code are available in this https URL .
Measurement of psi(2S) production cross-sections in proton-proton collisions at 7 and 13 TeV

The cross-sections of $\psi(2S)$ meson production in proton-proton collisions at $\sqrt{s}=13~\mathrm{TeV}$ are measured with a data sample collected by the LHCb detector corresponding to an integrated luminosity of $275~p\mathrm{b}^{-1}$. The production cross-sections for prompt $\psi(2S)$ mesons and those for $\psi(2S)$ mesons from $b$-hadron decays ($\psi{(2S)}\mathrm{-from-}b$) are determined as functions of the transverse momentum, $p_{\mathrm{T}}$, and the rapidity, $y$, of the $\psi(2S)$ meson in the kinematic range $2<p_{\mathrm{T}}<20~\mathrm{GeV}/c$ and $2.0<y<4.5$. The production cross-sections integrated over this kinematic region are \begin{equation*} \begin{split} \sigma(\mbox{prompt }\psi(2S),13~\mathrm{TeV}) &= {1.430 \pm 0.005(\mathrm{stat}) \pm 0.099 (\mathrm{syst})\mu\mathrm{b}},\\ \sigma(\psi(2S)\mathrm{-from-}b,13~\mathrm{TeV})&={0.426 \pm 0.002(\mathrm{stat}) \pm0.030 (\mathrm{syst})\mu\mathrm{b}}. \end{split} \end{equation*} A new measurement of $\psi(2S)$ production cross-sections in $pp$ collisions at $\sqrt{s}=7~\mathrm{TeV}$ is also performed using data collected in 2011, corresponding to an integrated luminosity of $614~{p\mathrm{b}^{-1}}$.The integrated production cross-sections in the kinematic range $3.5<p_{\mathrm{T}}<14~\mathrm{GeV}/c$ and $2.0<y<4.5$ are \begin{equation*} \begin{split} \sigma(\mbox{prompt }\psi(2S),7~\mathrm{TeV}) &={0.471 \pm0.001 (\mathrm{stat}) \pm 0.025 (\mathrm{syst})\mu\mathrm{b}},\\ \sigma(\psi(2S)\mathrm{-from-}b,7~\mathrm{TeV}) &={0.126\pm0.001 (\mathrm{stat}) \pm0.008 (\mathrm{syst})\mu\mathrm{b}}. \end{split} \end{equation*} All results show reasonable agreement with theoretical calculations.
Combinatorial index formulas for Lie algebras of seaweed type

Analogous to the types A, B, and C cases, we address the computation of the index of seaweed subalgebras in the type-D case. Formulas for the algebra’s index can be computed by counting the connected components of its associated meander. We focus on a set of distinguished vertices of the meander, called the tail of the meander, and using the tail, we provide comprehensive combinatorial formulas for the index of a seaweed in all the classical types. Using these formulas, we provide all general closed-form index formulas where the index is given by a polynomial greatest common divisor formula in the sizes of the parts that define the seaweed.
"Conservatives Overfit, Liberals Underfit": The Social-Psychological Control of Affect and Uncertainty

The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological theory. We introduce a revised BayesAct model that more deeply integrates social-psychological theorising, and we demonstrate a component of the model as being sufficient to account for cognitive biases about fairness, dissonance and conformity. We show how the model can unify different exploration strategies in reinforcement learning.