Empowering Active Learning to Jointly Optimize System and User Demands

Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading. In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances). We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user, while the users should receive only exercises that match their skills. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.Comment: To appear as a long paper in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020). Download our code and simulated user models at github: https://github.com/UKPLab/acl2020-empowering-active-learnin

NMDA Currents Modulate the Synaptic Input–Output Functions of Neurons in the Dorsal Nucleus of the Lateral Lemniscus in Mongolian Gerbils

Neurons in the dorsal nucleus of the lateral lemniscus (DNLL) receive excitatory and inhibitory inputs from the superior olivary complex (SOC) and convey GABAergic inhibition to the contralateral DNLL and the inferior colliculi. Unlike the fast glycinergic inhibition in the SOC, this GABAergic inhibition outlasts auditory stimulation by tens of milliseconds. Two mechanisms have been postulated to explain this persistent inhibition. One, an “integration-based” mechanism, suggests that postsynaptic excitatory integration in DNLL neurons generates prolonged activity, and the other favors the synaptic time course of the DNLL output itself. The feasibility of the integration-based mechanism was tested in vitro in DNLL neurons of Mongolian gerbils by quantifying the cellular excitability and synaptic input–output functions (IO-Fs). All neurons were sustained firing and generated a near monotonic IO-F on current injections. From synaptic stimulations, we estimate that activation of approximately five fibers, each on average liberating ∼18 vesicles, is sufficient to trigger a single postsynaptic action potential. A strong single pulse of afferent fiber stimulation triggered multiple postsynaptic action potentials. The steepness of the synaptic IO-F was dependent on the synaptic NMDA component. The synaptic NMDA receptor current defines the slope of the synaptic IO-F by enhancing the temporal and spatial EPSP summation. Blocking this NMDA-dependent amplification during postsynaptic integration of train stimulations resulted into a ∼20% reduction of the decay time course of the GABAergic inhibition. Thus, our data show that the NMDA-dependent amplification of the postsynaptic activity contributes to the GABAergic persistent inhibition generated by DNLL neurons

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1's proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of the methods. Therefore, we propose a new F1-based metric yielding a changed system ranking. Next, we compare the features and architectures used, which leads to a novel feature-rich stacked LSTM model that performs on par with the best systems, but is superior in predicting minority classes. To understand the methods' ability to generalize, we derive a new dataset and perform both in-domain and cross-domain experiments. Our qualitative and quantitative study helps interpreting the original FNC-1 scores and understand which features help improving performance and why. Our new dataset and all source code used during the reproduction study are publicly available for future research

Na3DyCl6

Single crystals of the title compound, tris­odium hexa­chloridodysprosate, Na3DyCl6, were obtained as a by-product of synthesis using dysprosium(III) chloride and sodium chloride among others. The monoclinic structure with its typical β angle close to 90° [90.823 (4)°] is isotypic with the mineral cryolite (Na3AlF6) and the high-temperature structure of the Na3 MCl6 series, with M = Eu–Lu, Y and Sc. The isolated, almost perfect [DyCl6]3− octa­hedra are inter­connected via two crystallographically different Na+ cations: while one Na+ resides on centres of symmetry (as well as Dy3+) and also builds almost perfect, isolated [NaCl6]5− octa­hedra, the other Na+ is surrounded by seven chloride anions forming a distorted [NaCl7]6− trigonal prism with just one cap as close secondary contact

K2LaCl5

The ternary title compound, dipotassium lanthanum penta­chloride, K2LaCl5, is isotypic with Y2HfS5 and various ternary rare-earth metal(III) halides with the general formula A 2 MX 5 (A = NH4, InI, Na–Cs; M = La–Dy; X = Cl–I). The La3+ cations and three of the four symmetry-independent chloride anions are located on a crystallographic mirror plane. The La3+ cations are surrounded by seven chloride anions, each in the shape of a monocapped trigonal prism, whereas the coordination spheres of the K+ cations exhibit one more cap. Three of the four independent chloride anions reside in a fivefold cationic coordination, leading to distorted square pyramids. The fourth chloride anion has only four cationic neighbours, forming no specific polyhedron

Episodic excursions of low-mass protostars on the Hertzsprung-Russell diagram

Following our recent work devoted to the effect of accretion on the pre-main-sequence evolution of low-mass stars, we perform a detailed analysis of episodic excursions of low-mass protostars in the Hertzsprung-Russell (H-R) diagram triggered by strong mass accretion bursts typical of FU Orionis-type objects (FUors). These excursions reveal themselves as sharp increases in the stellar total luminosity and/or effective temperature of the protostar and can last from hundreds to a few thousands of years, depending on the burst strength and characteristics of the protostar. During the excursions, low-mass protostars occupy the same part of the H-R diagram as young intermediate-mass protostars in the quiescent phase of accretion. Moreover, the time spent by low-mass protostars in these regions is on average a factor of several longer than that spent by the intermediate-mass stars in quiescence. During the excursions, low-mass protostars pass close to the position of most known FUors in the H-R diagram, but owing to intrinsic ambiguity the model stellar evolutionary tracks are unreliable in determining the FUor properties. We find that the photospheric luminosity in the outburst state may dominate the accretion luminosity already after a few years after the onset of the outburst, meaning that the mass accretion rates of known FUors inferred from the bolometric luminosity may be systematically overestimated, especially in the fading phase.Comment: 15 pages, 12 figure

Challenges in the Automatic Analysis of Students' Diagnostic Reasoning

Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges