nils-herrmann/clean_scirepeval huggingface dataset:
Model of lamellipodium initiation during cell spreading

Cell spreading requires a major reorganisation of the actin cytoskeleton, from a cortical structure to a lamellipodium where filaments are mostly parallel to the substrate. We propose a model inspired by the physics of nematic liquid crystals and fluid membranes, in which the coupling between actin mechanics, filaments orientation, and the local curvature of the cell membrane naturally yields the collective reorientation of actin filaments at the highly curved edge of a spreading cell. Filament orientation increases the traction force exerted by the frictional flow of polymerising actin on the substrate, creating a positive feedback loop between edge curvature, filament alignment, and traction force that promotes cell spreading. We establish the condition under which this feedback leads to a full wetting transition, which we interpret as the initiation of a lamellipodium, and we uncover the existence of bi-stability between partial and full spreading which could trigger spontaneous cell polarization and lead to migration.
Funding Adjustments in Equity Linear Products

Valuation adjustments are nowadays a common practice to include credit and liquidity effects in option pricing. Funding costs arising from collateral procedures, hedging strategies and taxes are added to option prices to take into account the production cost of financial contracts so that a profitability analysis can be reliably assessed. In particular, when dealing with linear products, we need a precise evaluation of such contributions since bid-ask spreads may be very tight. In this paper we start from a general pricing framework inclusive of valuation adjustments to derive simple evaluation formulae for the relevant case of total return equity swaps when stock lending and borrowing is adopted as hedging strategy.
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains

Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts. We create and publish a new dataset covering two domains and carefully analyse the suggested annotations. We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases. Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks.
An Information-Theoretical Approach to the Information Capacity and Cost-Effectiveness Evaluation of Color Palettes

Colors are used as effective tools of representing and transferring information. Number of colors in a palette is the direct arbiter of the information conveying capacity. Yet it should be well elaborated, since increasing the entropy by adding colors comes with its cost on decoding. Despite the possible effects upon diverse applications, a methodology for cost-effectiveness evaluation of palettes seems deficient. In this work, this need is being addressed from an information-theoretical perspective, via the articulated metrics and formulae. Besides, the proposed metrics are computed for some developed and known palettes, and observed results are evaluated.
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild

Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes it challenging for non-technical collaborators and endpoint users (e.g. physicians) to easily provide feedback on model development and to gain trust in ML. The accessibility challenge also makes collaboration more difficult and limits the ML researcher's exposure to realistic data and scenarios that occur in the wild. To improve accessibility and facilitate collaboration, we developed an open-source Python package, Gradio, which allows researchers to rapidly generate a visual interface for their ML models. Gradio makes accessing any ML model as easy as sharing a URL. Our development of Gradio is informed by interviews with a number of machine learning researchers who participate in interdisciplinary collaborations. Their feedback identified that Gradio should support a variety of interfaces and frameworks, allow for easy sharing of the interface, allow for input manipulation and interactive inference by the domain expert, as well as allow embedding the interface in iPython notebooks. We developed these features and carried out a case study to understand Gradio's usefulness and usability in the setting of a machine learning collaboration between a researcher and a cardiologist.
GIBBONFINDR: An R package for the detection and classification of acoustic signals

The recent improvements in recording technology, data storage and battery life have led to an increased interest in the use of passive acoustic monitoring for a variety of research questions. One of the main obstacles in implementing wide scale acoustic monitoring programs in terrestrial environments is the lack of user-friendly, open source programs for processing large sound archives. Here we describe the new, open-source R package GIBBONFINDR which has functions for detection, classification and visualization of acoustic signals using a variety of readily available machine learning algorithms in the R programming environment. We provide a case study showing how GIBBONFINDR functions can be used in a workflow to detect and classify Bornean gibbon (Hylobates muelleri) calls in long-term acoustic data sets recorded in Danum Valley Conservation Area, Sabah, Malaysia. Machine learning is currently one of the most rapidly growing fields-- with applications across many disciplines-- and our goal is to make commonly used signal processing techniques and machine learning algorithms readily available for ecologists who are interested in incorporating bioacoustics techniques into their research.
Nearly $k$-distance sets

We say that a set of points $S\subset \mathbb{R}^d$ is an $\varepsilon$-nearly $k$-distance set if there exist $1\le t_1\le \ldots\le t_k,$ such that the distance between any two distinct points in $S$ falls into $[t_1,t_1+\varepsilon]\cup\ldots\cup[t_k,t_k+\varepsilon]$. In this paper, we study the quantity $M_k(d) = \lim_{\varepsilon\to 0}\max\{|S|\ :\ S\text{ is an }\varepsilon\text{-nearly } k \text{-distance set in } \mathbb{R}^d\}$ and its relation to the classical quantity $m_k(d)$: the size of the largest $k$-distance set in $\mathbb{R}^d$. We obtain that $M_k(d) = m_k(d)$ for $k=2,3$, as well as for any fixed $k$, provided that $d$ is sufficiently large. The last result answers a question, proposed by Erdős, Makai and Pach. 
We also address a closely related Turan-type problem, studied by Erdős, Makai, Pach, and Spencer in the 80's: given $n$ points in $\mathbb{R}^d$, how many pairs of them form a distance that belongs to $[t_1,t_1+1]\cup\ldots\cup[t_k,t_k+1],$ where $t_1,\ldots, t_k$ are fixed and any two points in the set are at distance at least $1$ apart? We establish the connection between this quantity and a quantity closely related to $M_k(d-1)$, as well as obtain an exact answer for the same ranges $k,d$ as above.
On the physical (im)possibility of lightsabers

In this paper, we use a science-fiction theme (i.e. the iconic lightsaber from the Star Wars universe) as a pedagogical tool to introduce aspects of nonlinear electrodynamics due to the quantum vacuum to an audience with an undergraduate physics background. In particular, we focus on one major problem with lightsabers that is commonly invoked as an argument to dismiss them as unrealistic: light blades are not solid and thus cannot be used in a duel as normal swords would. Using techniques coming from ultra intense laser science, we show that for high enough laser intensities, two lightsaber blades can ‘feel’ solid to each other. We argue that this aspect of lightsabers is not impossible due to limitations of the laws of physics, but is very implausible due to the high intensities and energy needed for their operation.
Flexibly Fair Representation Learning by Disentanglement

We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled representation learning literature, we propose an algorithm for learning compact representations of datasets that are useful for reconstruction and prediction, but are also \emph{flexibly fair}, meaning they can be easily modified at test time to achieve subgroup demographic parity with respect to multiple sensitive attributes and their conjunctions. We show empirically that the resulting encoder---which does not require the sensitive attributes for inference---enables the adaptation of a single representation to a variety of fair classification tasks with new target labels and subgroup definitions.
Multi-Frequency Vector Diffusion Maps

We introduce multi-frequency vector diffusion maps (MFVDM), a new framework for organizing and analyzing high dimensional datasets. The new method is a mathematical and algorithmic generalization of vector diffusion maps (VDM) and other non-linear dimensionality reduction methods. MFVDM combines different nonlinear embeddings of the data points defined with multiple unitary irreducible representations of the alignment group that connect two nodes in the graph. We illustrate the efficacy of MFVDM on synthetic data generated according to a random graph model and cryo-electron microscopy image dataset. The new method achieves better nearest neighbor search and alignment estimation than the state-of-the-arts VDM and diffusion maps (DM) on extremely noisy data.
The [46, 9, 20]2 code is unique

The minimum distance of all binary linear codes with dimension at most eight is known. The smallest open case for dimension nine is length $n=46$ with known bounds $19\le d\le 20$. Here we present a $[46,9,20]_2$ code and show its uniqueness. Interestingly enough, this unique optimal code is asymmetric, i.e., it has a trivial automorphism group. Additionally, we show the non-existence of $[47,10,20]_2$ and $[85,9,40]_2$ codes.
Generating Question-Answer Hierarchies

The process of knowledge acquisition can be viewed as a question-answer game between a student and a teacher in which the student typically starts by asking broad, open-ended questions before drilling down into specifics (Hintikka, 1981; Hakkarainen and Sintonen, 2002). This pedagogical perspective motivates a new way of representing documents. In this paper, we present SQUASH (Specificity-controlled Question-Answer Hierarchies), a novel and challenging text generation task that converts an input document into a hierarchy of question-answer pairs. Users can click on high-level questions (e.g., “Why did Frodo leave the Fellowship?”) to reveal related but more specific questions (e.g., “Who did Frodo leave with?”). Using a question taxonomy loosely based on Lehnert (1978), we classify questions in existing reading comprehension datasets as either GENERAL or SPECIFIC . We then use these labels as input to a pipelined system centered around a conditional neural language model. We extensively evaluate the quality of the generated QA hierarchies through crowdsourced experiments and report strong empirical results.
Scaling Autoregressive Video Models

Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models often attempt to address these issues by combining sometimes complex, usually video-specific neural network architectures, latent variable models, adversarial training and a range of other methods. Despite their often high complexity, these approaches still fall short of generating high quality video continuations outside of narrow domains and often struggle with fidelity. In contrast, we show that conceptually simple autoregressive video generation models based on a three-dimensional self-attention mechanism achieve competitive results across multiple metrics on popular benchmark datasets, for which they produce continuations of high fidelity and realism. We also present results from training our models on Kinetics, a large scale action recognition dataset comprised of YouTube videos exhibiting phenomena such as camera movement, complex object interactions and diverse human movement. While modeling these phenomena consistently remains elusive, we hope that our results, which include occasional realistic continuations encourage further research on comparatively complex, large scale datasets such as Kinetics.
An Extensible Interactive Interface for Agent Design

In artificial intelligence, we often specify tasks through a reward function. While this works well in some settings, many tasks are hard to specify this way. In deep reinforcement learning, for example, directly specifying a reward as a function of a high-dimensional observation is challenging. Instead, we present an interface for specifying tasks interactively using demonstrations. Our approach defines a set of increasingly complex policies. The interface allows the user to switch between these policies at fixed intervals to generate demonstrations of novel, more complex, tasks. We train new policies based on these demonstrations and repeat the process. We present a case study of our approach in the Lunar Lander domain, and show that this simple approach can quickly learn a successful landing policy and outperforms an existing comparison-based deep RL method.
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections

Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant languages, especially when neither annotated target data nor parallel corpora are available. In this paper, we focus on methods for cross-lingual transfer to distant languages and propose to learn a generative model with a structured prior that utilizes labeled source data and unlabeled target data jointly. The parameters of source model and target model are softly shared through a regularized log likelihood objective. An invertible projection is employed to learn a new interlingual latent embedding space that compensates for imperfect cross-lingual word embedding input. We evaluate our method on two syntactic tasks: part-of-speech (POS) tagging and dependency parsing. On the Universal Dependency Treebanks, we use English as the only source corpus and transfer to a wide range of target languages. On the 10 languages in this dataset that are distant from English, our method yields an average of 5.2% absolute improvement on POS tagging and 8.3% absolute improvement on dependency parsing over a direct transfer method using state-of-the-art discriminative models.
Does Object Recognition Work for Everyone?

The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used image datasets in object recognition. We find that the systems perform relatively poorly on household items that commonly occur in countries with a low household income. Qualitative analyses suggest the drop in performance is primarily due to appearance differences within an object class (e.g., dish soap) and due to items appearing in a different context (e.g., toothbrushes appearing outside of bathrooms). The results of our study suggest that further work is needed to make object-recognition systems work equally well for people across different countries and income levels.
How a Single Paper Affects the Impact Factor: Implications for Scholarly Publishing

Because the Impact Factor (IF) is an average quantity and most journals are small, IFs are volatile. We study how a single paper affects the IF using data from 11639 journals in the 2017 Journal Citation Reports. We define as volatility the IF gain (or loss) caused by a single paper, and this is inversely proportional to journal size. We find high volatilities for hundreds of journals annually due to their top-cited paper: whether it is a highly-cited paper in a small journal, or a moderately (or even low) cited paper in a small and low-cited journal. For example, 1218 journals had their most cited paper boost their IF by more than 20%, while for 231 journals the boost exceeded 50%. We find that small journals are rewarded much more than large journals for publishing a highly-cited paper, and are also penalized more for publishing a low-cited paper, especially if they have a high IF. This produces a strong incentive for prestigious, high-IF journals to stay small, to remain competitive in IF rankings. We discuss the implications for breakthrough papers to appear in prestigious journals. We also question the practice of ranking journals by IF given this uneven reward mechanism.
Not gone with the wind: Planet occurrence is independent of stellar galactocentric velocity

We demonstrate that planet occurrence does not depend on stellar galactocentric velocity in the Solar neighbourhood. Using Gaia DR2 astrometry and radial velocity data, we calculate 3D galactocentric velocities for 197 090 Kepler field stars, 1647 of which are confirmed planet hosts. When we compare the galactocentric velocities of planet hosts to those of the entire field star sample, we observe a statistically significant (KS p-value  = 10−70) distinction, with planet hosts being apparently slower than field stars by ∼40 km s−1. We explore some potential explanations for this difference and conclude that it is not a consequence of the planet–metallicity relation or distinctions in the samples’ thin/thick disc membership, but rather an artefact of Kepler’s selection function. Non Kepler-host stars that have nearly identical distances, temperatures, surface gravities, and Kepler magnitudes to the confirmed planet hosts also have nearly identical velocity distributions. Using one of these identical non-host samples, we consider that the probability of a star with velocity vtot hosting a planet can be described by an exponential function proportional to $e^{(-v_{\mathrm{tot}}/v_0)}$. Using a Markov Chain Monte Carlo sampler, we determine that v0 >976 km s−1 to 99 per cent confidence, which implies that planets in the Solar neighbourhood are just as likely to form around high-velocity stars as they are around low-velocity stars. Our work highlights the subtle ways in which selection biases can create strong correlations without physical underpinnings.
THE PLANCHEREL FORMULA FOR COMPLEX SEMISIMPLE QUANTUM GROUPS

We calculate the Plancherel formula for complex semisimple quantum groups, that is, Drinfeld doubles of q-deformations of compact semisimple Lie groups. As a consequence we obtain a concrete description of their associated reduced group C *-algebras. The main ingredients in our proof are the Bernstein-Gelfand-Gelfand complex and the Hopf trace formula.
A first look on 3D effects in open axion haloscopes

We explore finite size 3D effects in open axion haloscopes such as a dish antenna, a dielectric disk and a minimal dielectric haloscope consisting of a mirror and one dielectric disk. Particularly dielectric haloscopes are a promising new method for detecting dark matter axions in the mass range above 40 μeV. By using two specialized independent approaches—based on finite element methods and Fourier optics —we compute the electromagnetic fields in these settings expected in the presence of an axion dark matter field. This allows us to study diffraction and near field effects for realistically sized experimental setups in contrast to earlier idealized 1D studies with infinitely extended mirrors and disks. We also study axion velocity effects and disk tiling. Diffraction effects are found to become less relevant towards larger axion masses and for the larger disk radii for example aimed at in full size dielectric haloscopes such as MADMAX . The insights of our study not only provide a foundation for a realistic modelling of open axion dark matter search experiments in general, they are in particular also the first results taking into account 3D effects for dielectric haloscopes.
Topic Sensitive Attention on Generic Corpora Corrects Sense Bias in Pretrained Embeddings

Given a small corpus D_T pertaining to a limited set of focused topics, our goal is to train embeddings that accurately capture the sense of words in the topic in spite of the limited size of D_T. These embeddings may be used in various tasks involving D_T. A popular strategy in limited data settings is to adapt pretrained embeddings E trained on a large corpus. To correct for sense drift, fine-tuning, regularization, projection, and pivoting have been proposed recently. Among these, regularization informed by a word’s corpus frequency performed well, but we improve upon it using a new regularizer based on the stability of its cooccurrence with other words. However, a thorough comparison across ten topics, spanning three tasks, with standardized settings of hyper-parameters, reveals that even the best embedding adaptation strategies provide small gains beyond well-tuned baselines, which many earlier comparisons ignored. In a bold departure from adapting pretrained embeddings, we propose using D_T to probe, attend to, and borrow fragments from any large, topic-rich source corpus (such as Wikipedia), which need not be the corpus used to pretrain embeddings. This step is made scalable and practical by suitable indexing. We reach the surprising conclusion that even limited corpus augmentation is more useful than adapting embeddings, which suggests that non-dominant sense information may be irrevocably obliterated from pretrained embeddings and cannot be salvaged by adaptation.
FCC-ee: Your Questions Answered

This document answers in simple terms many FAQs about FCC-ee, including comparisons with other colliders. It complements the FCC-ee CDR and the FCC Physics CDR by addressing many questions from non-experts and clarifying issues raised during the European Strategy symposium in Granada, with a view to informing discussions in the period between now and the final endorsement by the CERN Council in 2020 of the European Strategy Group recommendations. This document will be regularly updated as more questions appear or new information becomes available.
Physics departments should discuss sexual harassment -- But consider this first

In April 2019, Aycock et al. published "Sexual harassment reported by undergraduate female physicists"  Their main finding is that 3/4 of undergraduate women in physics in the U.S. report experiencing sexual harassment. Gender minorities experience high rates of harassment also. Many physics departments will want to discuss these findings with the ultimate goal of making our field harassment-free. However, there are major challenges inherent in having these discussions, since many participants will have experienced harassment themselves. We suggest questions for discussion organizers to reflect on, and ideas for how departments can approach these discussions.