Miracle-dz/UniFAR-FTUs huggingface dataset:
Focusing Knowledge-based Graph Argument Mining via Topic Modeling

Decision-making usually takes five steps: identifying the problem, collecting data, extracting evidence, identifying pro and con arguments, and making decisions. Focusing on extracting evidence, this paper presents a hybrid model that combines latent Dirichlet allocation and word embeddings to obtain external knowledge from structured and unstructured data. We study the task of sentence-level argument mining, as arguments mostly require some degree of world knowledge to be identified and understood. Given a topic and a sentence, the goal is to classify whether a sentence represents an argument in regard to the topic. We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata, building a graph based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence. Also, we build a second graph based on topic-specific articles found via Google to tackle the general incompleteness of structured knowledge bases. Combining these graphs, we obtain a graph-based model which, as our evaluation shows, successfully capitalizes on both structured and unstructured data.
Object and Relation Centric Representations for Push Effect Prediction

Pushing is an essential non-prehensile manipulation skill used for tasks ranging from pre-grasp manipulation to scene rearrangement, reasoning about object relations in the scene, and thus pushing actions have been widely studied in robotics. The effective use of pushing actions often requires an understanding of the dynamics of the manipulated objects and adaptation to the discrepancies between prediction and reality. For this reason, effect prediction and parameter estimation with pushing actions have been heavily investigated in the literature. However, current approaches are limited because they either model systems with a fixed number of objects or use image-based representations whose outputs are not very interpretable and quickly accumulate errors. In this paper, we propose a graph neural network based framework for effect prediction and parameter estimation of pushing actions by modeling object relations based on contacts or articulations. Our framework is validated both in real and simulated environments containing different shaped multi-part objects connected via different types of joints and objects with different masses, and it outperforms image-based representations on physics prediction. Our approach enables the robot to predict and adapt the effect of a pushing action as it observes the scene. It can also be used for tool manipulation with never-seen tools. Further, we demonstrate 6D effect prediction in the lever-up action in the context of robot-based hard-disk disassembly.
What Do We See in Them? Identifying Dimensions of Partner Models for Speech Interfaces Using a Psycholexical Approach

Perceptions of system competence and communicative ability, termed partner models, play a significant role in speech interface interaction. Yet we do not know what the core dimensions of this concept are. Taking a psycholexical approach, our paper is the first to identify the key dimensions that define partner models in speech agent interaction. Through a repertory grid study (N=21), a review of key subjective questionnaires, an expert review of resulting word pairs and an online study of 356 users of speech interfaces, we identify three key dimensions that make up a users’ partner model: 1) perceptions towards partner competence and dependability; 2) assessment of human-likeness; and 3) a system’s perceived cognitive flexibility. We discuss the implications for partner modelling as a concept, emphasising the importance of salience and the dynamic nature of these perceptions.
Spatiotemporal Vortex Pulses: Angular Momenta and Spin-Orbit Interaction.

Recently, spatiotemporal optical vortex pulses carrying a purely transverse intrinsic orbital angular momentum were generated experimentally [Optica 6, 1547 (2019)OPTIC82334-253610.1364/OPTICA.6. 001547; Nat. Photonics 14, 350 (2020)NPAHBY1749-488510.1038/s41566-020-0587-z]. However, an accurate theoretical analysis of such states and their angular-momentum properties remains elusive. Here, we provide such analysis, including scalar and vector spatiotemporal Bessel-type solutions as well as description of their propagational, polarization, and angular-momentum properties. Most importantly, we calculate both local densities and integral values of the spin and orbital angular momenta, and predict observable spin-orbit interaction phenomena related to the coupling between the transverse spin and orbital angular momentum. Our analysis is readily extended to spatiotemporal vortex pulses of other natures (e.g., acoustic).
Generalized electromagnetic theorems for nonlocal plasmonics

The ultraconfined light of plasmonic modes put their effective wavelength close to the mean free path of electrons inside the metal electron gas. The Drude model, which can not take the repulsive interactions of electrons into account, then clearly begins to show its limits. In an intermediate length scale where a full quantum treatment is computationally prohibitive, the semiclassical hydrodynamic model, instrinsically non-local, has proven successful. Here we generalize the expression for the absorption volume density and the reciprocity theorem in the framework of this hydrodynamic model. We validate numerically these generalized theorems and show that using classical expressions instead leads to large discrepancies.
Observation of collectivity enhanced magnetoassociation of 6Li in the quantum degenerate regime

The association process of Feshbach molecules is well described by a Landau–Zener (LZ) transition above the Fermi temperature, such that two-body physics dominates the dynamics. However, using 6Li atoms and the associated Feshbach resonance at B r = 834.1 G, we observe an enhancement of the atom–molecule coupling as the fermionic atoms reach degeneracy, demonstrating the importance of many-body coherence not captured by the conventional LZ model. In the experiment, we apply a linear association ramp ranging from adiabatic to non-equilibrium molecule association for various temperatures. We develop a theoretical model that explains the temperature dependence of the atom–molecule coupling. Furthermore, we characterize this dependence experimentally and extract the atom–molecule coupling coefficient as a function of temperature, finding qualitative agreement between our model and experimental results. In addition, we simulate the dynamics of molecular association during a nonlinear field ramp. We find that, in the non-equilibrium regime, molecular association efficiency can be enhanced by sweeping the magnetic field cubically with time. Accurate measurement of the atom–molecule coupling coefficient is important for both theoretical and experimental studies of molecular association and many-body collective dynamics.
Regularization Strategy for Point Cloud via Rigidly Mixed Sample

Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularization is essential for point clouds since lack of generality is more likely to occur in point cloud due to small datasets. This paper proposes a Rigid Subset Mix (RSMix)1, a novel data augmentation method for point clouds that generates a virtual mixed sample by replacing part of the sample with shape-preserved subsets from another sample. RSMix preserves structural information of the point cloud sample by extracting subsets from each sample without deformation using a neighboring function. The neighboring function was carefully designed considering unique properties of point cloud, unordered structure and non-grid. Experiments verified that RSMix successfully regularized the deep neural networks with remarkable improvement for shape classification. We also analyzed various combinations of data augmentations including RSMix with single and multi-view evaluations, based on abundant ablation studies.
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation

Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). However, the presence of overlap between the train and test datasets can lead to inflated results, inadvertently evaluating the model’s ability to memorize and interpreting it as the ability to generalize. In addition, such data sets may not provide an effective indicator of the performance of these methods in real world scenarios. We identify leakage of training data into test data on several publicly available datasets used to evaluate NLP tasks, including named entity recognition and relation extraction, and study them to assess the impact of that leakage on the model’s ability to memorize versus generalize.
Radial structure and formation of the Milky Way disc

Context. The formation of the Galactic disc is an enthusiastically debated issue. Numerous studies and models seek to identify the dominant physical process(es) that shaped its observed properties: e.g. satellite accretion, star burst, quenching, gas infall, stellar radial migration. Aims. Taking advantage of the improved coverage of the inner Milky Way provided by the SDSS DR16 APOGEE catalogue and of the ages published in the APOGEE-AstroNN Value Added Catalogue (VAC), we examine the radial evolution of the chemical and age properties of the Galactic stellar disc, with the aim to better constrain its formation. Methods. Using a sample of 199,307 giant stars with precise APOGEE abundances and APOGEE-astroNN ages, selected in a ±2 kpc layer around the galactic plane, we assess the dependency with guiding radius of: (i) the median metallicity, (ii) the ridge lines of the [Fe/H]-[Mg/Fe] and age-[Mg/Fe] distributions and (iii) the Age Distribution Function (ADF). Results. The giant star sample allows us to probe the radial behaviour of the Galactic disc from Rg = 0 to 14-16 kpc. The thick disc [Fe/H]-[Mg/Fe] ridge lines follow closely grouped parallel paths, supporting the idea that the thick disc did form from a well-mixed medium. However, the ridge lines present a small drift in [Mg/Fe], which decreases with increasing guiding radius. At sub-solar metallicity, the intermediate and outer thin disc [Fe/H]-[Mg/Fe] ridge lines follow parallel sequences shifted to lower metallicity as the guiding radius increases. We interpret this pattern, as the signature of a dilution of the inter-stellar medium from Rg ∼ 6 kpc to the outskirt of the disc, which occured before the onset of the thin disc formation. The APOGEE-AstroNN VAC provides stellar ages for statistically significant samples of thin disc stars from the Galactic centre up to Rg ∼ 14 kpc. An important result provided by this dataset, is that the thin disc presents evidence of an inside-out formation up to Rg ∼ 10 − 12 kpc. Moreover, about ∼ 7 Gyr ago, the [Mg/Fe] ratio in the outer thin disc (Rg > 10 kpc) was higher by about ∼ 0.03 − 0.05 dex than in the more internal regions of the thin disc. This could be the fossil record of a pollution of the outer disc gas reservoir, by the thick disc during its starburst phase.
Electrolytic Dissolution of Binary Alloys Containing a Noble Metal

In the electrolytic dissolution of binary alloys the concepts of ionization and redeposition of a metal, coupling of the anodic reactions in accordance with the concepts of irreversible thermodynamics, and volume diffusion in the presence of given supersaturations of single and double vacancies are analyzed theoretically. The ionization‐redeposition mechanism is in principle possible but only if coupling of the anodic reactions occurs. Volume diffusion may be operative via divacancies. Results from x‐ray investigations and from measurements with a rotating disk‐ring electrode for copper‐gold alloy indicate that interdiffusion of the constituent metals in the alloy occurs to a significant extent and dissolution of Au does not take place. Electrochemical measurements with a Cu‐Zn alloy involving 30 a/o (atomic per cent) Zn give no indication of occurrence of the ionization‐redeposition mechanism.
Pitfalls of Static Language Modelling

Our world is open-ended, non-stationary and constantly evolving; thus what we talk about and how we talk about it changes over time. This inherent dynamic nature of language comes in stark contrast to the current static language modelling paradigm, which constructs training and evaluation sets from overlapping time periods. Despite recent progress, we demonstrate that state-of-the-art Transformer models perform worse in the realistic setup of predicting future utterances from beyond their training period—a consistent pattern across three datasets from two domains. We ﬁnd that, while increasing model size alone—a key driver behind recent progress—does not provide a solution for the temporal generalization problem, having models that continually update their knowledge with new information can indeed slow down the degradation over time. Hence, given the compilation of ever-larger language modelling training datasets, combined with the growing list of language-model-based NLP applications that require up-to-date knowledge about the world, we argue that now is the right time to rethink our static language modelling evaluation protocol, and develop adaptive language models that can remain up-to-date with respect to our ever-changing and non-stationary world. 1
Nanomechanical Characterization of the Interfacial Properties of Bilayers of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine

We investigate the mechanical properties of bilayers of 1,2-dipalmitoyl-sn-glycero-3phosphocholine in the gel phase by using peak force tapping with quantitative nanomechanical mapping. We study both dry and aqueous bilayers and liposomes supported on the oxidized silicon surface. We report the observation of a marked substrate effect on the measured Young modulus of supported bilayer stacks which decreases as the height of the stack increases. In contrast a clear substrate effect is not observed for the top bilayer of supported aqueous liposomes, which is however affected by the surface curvature of the sample. Adhesion forces present quantitative differences between dry and aqueous samples, with the former being dominated by capillary effects and the latter by non-contact interactions between tip and substrate. The mechanical properties of stacked bilayers reveal a threshold between two different regimes, for the lower portion and the upper portion of the stack, that reveals a change in the plasticity of the system. The threshold is affected by applied setpoint and aging of the sample. We propose that it arises from the presence of thin layers of hydration water between the headgroups of contacting phospholipid bilayers, which appears to have a major effect on the response of the bilayer to an external mechanical stress.
Pliable Index Coding via Conflict-Free Colorings of Hypergraphs

In the pliable index coding (PICOD) problem, a server is to serve multiple clients, each of which possesses a unique subset of the complete message set as side information and requests a new message which it does not have. The goal of the server is to do this using as few transmissions as possible. This work presents a hypergraph coloring approach to the PICOD problem. A conflict-free coloring of a hypergraph is known from literature as an assignment of colors to its vertices so that each edge of the graph contains one uniquely colored vertex. For a given PICOD problem represented by a hypergraph consisting of messages as vertices and request-sets as edges, we present achievable PICOD schemes using conflict-free colorings of the PICOD hypergraph. Various graph theoretic parameters arising out of such colorings (and some new coloring variants) then give a number of upper bounds on the optimal PICOD length, which we study in this work. Our achievable schemes based on hypergraph coloring include scalar as well as vector linear PICOD schemes. For the scalar case, using the correspondence with conflict-free coloring, we show the existence of an achievable scheme which has length $O(\log^{2}\Gamma)$, where $\Gamma$ refers to a parameter of the hypergraph that captures the maximum ‘incidence’ number of other edges on any edge. This result improves upon known achievability results in PICOD literature, in some parameter regimes.
Self-consistent light-front quark model analysis of $B\to D\ell\nu_\ell$ transition form factors

We investigate the transition form factors $f_+(q^2)$ and $f_-(q^2)$ [or $f_0(q^2)$] for the exclusive semileptonic $B\to D\ell\nu_\ell$ $(\ell=e, \mu,\tau)$ decays in the standard light-front quark model (LFQM) based on the LF quantization. The common belief is that while $f_+(q^2)$ can be obtained without involving any treacherous contributions such as the zero mode and the instantaneous contribution, $f_-(q^2)$ receives those treacherous contributions since it involves at least two components of the current, e.g. $(J^+, J^-)$ or $(J^+, {\bf J}_\perp)$. Contrary to the common belief, we show in the Drell-Yan ($q^+=0$) frame that $f_-(q^2)$ obtained from $(J^+, J^-)$ gives identical result to $f_-(q^2)$ obtained from $(J^+, {\bf J}_\perp)$ without involving such treacherous contributions in the standard LFQM. In our numerical calculations, we obtain the form factors and branching ratios for $B\to D\ell\nu_\ell$ $(\ell=e, \mu,\tau)$ and compare with the experimental data as well as other theoretical model predictions. Our results for ${\rm Br}(B\to D\ell\nu_\ell)$ show reasonable agreement with the experimental data except for the semitauonic $B^0\to D^-\tau\nu_\tau$ decay. The ratio ${\cal R}(D)=\frac{{\rm Br}(B\to D\tau\nu_\tau)}{{\rm Br}(B\to D\ell'\nu_{\ell'})}$ $(\ell'=e,\mu)$ is also estimated and compared with the experimental data as well as other theoretical predictions.
Group Consensus of Linear Multiagent Systems Under Nonnegative Directed Graphs

Group consensus implies reaching multiple groups where agents belonging to the same cluster reach state consensus. This article focuses on linear multiagent systems under nonnegative directed graphs. A new necessary and sufficient condition for ensuring group consensus is derived, which requires the spanning forest of the underlying directed graph and that of its quotient graph induced with respect to a clustering partition to contain equal minimum number of directed trees. This condition is further shown to be equivalent to containing cluster spanning trees, a commonly used topology for the underlying graph in the literature. Under a designed controller gain, lower bound of the overall coupling strength for achieving group consensus is specified. Moreover, the pattern of the multiple consensus states formed by all clusters is characterized when the overall coupling strength is large enough.
Fast Concept Mapping: The Emergence of Human Abilities in Artificial Neural Networks when Learning Embodied and Self-Supervised

Most artificial neural networks used for object detection and recognition are trained in a fully supervised setup. This is not only very resource consuming as it requires large data sets of labeled examples but also very different from how humans learn. We introduce a setup in which an artificial agent first learns in a simulated world through self-supervised exploration. Following this, the representations learned through interaction with the world can be used to associate semantic concepts such as different types of doors. To do this, we use a method we call fast concept mapping which uses correlated firing patterns of neurons to define and detect semantic concepts. This association works instantaneous with very few labeled examples, similar to what we observe in humans in a phenomenon called fast mapping. Strikingly, this method already identifies objects with as little as one labeled example which highlights the quality of the encoding learned self-supervised through embodiment using curiosity-driven exploration. It therefor presents a feasible strategy for learning concepts without much supervision and shows that through pure interaction with the world meaningful representations of an environment can be learned.
Music source separation conditioned on 3D point clouds

Recently, significant progress has been made in audio source separation by the application of deep learning techniques. Current methods that combine both audio and visual information use 2D representations such as images to guide the separation process. However, in order to (re)-create acoustically correct scenes for 3D virtual/augmented reality applications from recordings of real music ensembles, detailed information about each sound source in the 3D environment is required. This demand, together with the proliferation of 3D visual acquisition systems like LiDAR or rgb-depth cameras, stimulates the creation of models that can guide the audio separation using 3D visual information. This paper proposes a multi-modal deep learning model to perform music source separation conditioned on 3D point clouds of music performance recordings. This model extracts visual features using 3D sparse convolutions, while audio features are extracted using dense convolutions. A fusion module combines the extracted features to finally perform the audio source separation. It is shown, that the presented model can distinguish the musical instruments from a single 3D point cloud frame, and perform source separation qualitatively similar to a reference case, where manually assigned instrument labels are provided.
Gyro-kinematic Ages for around 30,000 Kepler Stars

Estimating stellar ages is important for advancing our understanding of stellar and exoplanet evolution and investigating the history of the Milky Way. However, ages for low-mass stars are hard to infer as they evolve slowly on the main sequence. In addition, empirical dating methods are difficult to calibrate for low-mass stars as they are faint. In this work, we calculate ages for Kepler F, G, and crucially K and M dwarfs, using their rotation and kinematic properties. We apply the simple assumption that the velocity dispersion of stars increases over time and adopt an age–velocity-dispersion relation (AVR) to estimate average stellar ages for groupings of coeval stars. We calculate the vertical velocity dispersion of stars in bins of absolute magnitude, temperature, rotation period, and Rossby number and then convert velocity dispersion to kinematic age via an AVR. Using this method, we estimate gyro-kinematic ages for 29,949 Kepler stars with measured rotation periods. We are able to estimate ages for clusters and asteroseismic stars with an rms of 1.22 Gyr and 0.26 Gyr respectively. With our Astraea machine-learning algorithm, which predicts rotation periods, we suggest a new selection criterion (a weight of 0.15) to increase the size of the McQuillan et al. catalog of Kepler rotation periods by up to 25%. Using predicted rotation periods, we estimated gyro-kinematic ages for stars without measured rotation periods and found promising results by comparing 12 detailed age–element abundance trends with literature values.