Miracle-dz/UniFAR-FTUs huggingface dataset:
Classes of Full-Duplex Channels With Capacity Achieved Without Adaptation

Full-duplex communication allows a terminal to transmit and receive signals simultaneously, and hence, it is helpful in general to adapt transmissions to received signals. However, this often requires unaffordable complexity. This work focuses on simple non-adaptive transmission, and provides two classes of channels for which Shannon’s information capacity regions are achieved without adaptation. The first is the injective semi-deterministic two-way channel that includes additive channels with various types of noises modeling wireless, coaxial cable, and other settings. The other is the Poisson two-way channel, for which we show that non-adaptive transmission is asymptotically optimal in the high dark current regime.
Modeling Personality vs. Modeling Personalidad

Sensor data collected from smartphones provides the possibility to passively infer a user's personality traits. Such models can be used to enable technology personalization, while contributing to our substantive understanding of how human behavior manifests in daily life. A significant challenge in personality modeling involves improving the accuracy of personality inferences, however, research has yet to assess and consider the cultural impact of users' country of residence on model replicability. We collected mobile sensing data and self-reported Big Five traits from 166 participants (54 women and 112 men) recruited in five different countries (UK, Spain, Colombia, Peru, and Chile) for 3 weeks. We developed machine learning based personality models using culturally diverse datasets - representing different countries - and we show that such models can achieve state-of-the-art accuracy when tested in new countries, ranging from 63% (Agreeableness) to 71% (Extraversion) of classification accuracy. Our results indicate that using country-specific datasets can improve the classification accuracy between 3% and 7% for Extraversion, Agreeableness, and Conscientiousness. We show that these findings hold regardless of gender and age balance in the dataset. Interestingly, using gender- or age- balanced datasets as well as gender-separated datasets improve trait prediction by up to 17%. We unpack differences in personality models across the five countries, highlight the most predictive data categories (location, noise, unlocks, accelerometer), and provide takeaways to technologists and social scientists interested in passive personality assessment.
DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data

In recent years, data-driven methods have been developed to learn dynamical systems and partial differential equations (PDE). The goal of such work is discovering unknown physics and the corresponding equations. However, prior to achieving this goal, major challenges remain to be resolved, including learning PDE under noisy data and limited discrete data. To overcome these challenges, in this work, a deep-learning based data-driven method, called DL-PDE, is developed to discover the governing PDEs of underlying physical processes. The DL-PDE method combines deep learning via neural networks and data-driven discovery of PDE via sparse regressions. In the DL-PDE, a neural network is first trained, and then a large amount of meta-data is generated, and the required derivatives are calculated by automatic differentiation. Finally, the form of PDE is discovered by sparse regression. The proposed method is tested with physical processes, governed by groundwater flow equation, convection-diffusion equation, Burgers equation and Korteweg-de Vries (KdV) equation, for proof-of-concept and applications in real-world engineering settings. The proposed method achieves satisfactory results when data are noisy and limited.
Positive scalar curvature on stratified spaces, I: the simply connected case

Let $M_\Sigma$ be an $n$-dimensional Thom-Mather stratified space of depth $1$. We denote by $\beta M$ the singular locus and by $L$ the associated link. In this paper we study the problem of when such a space can be endowed with a wedge metric of positive scalar curvature. We relate this problem to recent work on index theory on stratified spaces, giving first an obstruction to the existence of such a metric in terms of a wedge $\alpha$-class $\alpha_w (M_\Sigma)\in KO_n$. In order to establish a sufficient condition we need to assume additional structure: we assume that the link of $M_\Sigma$ is a homogeneous space of positive scalar curvature, $L=G/K$, where the semisimple compact Lie group $G$ acts transitively on $L$ by isometries. Examples of such manifolds include compact semisimple Lie groups and Riemannian symmetric spaces of compact type. Under these assumptions, when $M_\Sigma$ and $\beta M$ are spin, we reinterpret our obstruction in terms of two $\alpha$-classes associated to the resolution of $M_\Sigma$, $M$, and to the singular locus $\beta M$. Finally, when $M_\Sigma$, $\beta M$, $L$, and $G$ are simply connected and $\dim M$ is big enough, and when some other conditions on $L$ (satisfied in a large number of cases) hold, we establish the main result of this article, showing that the vanishing of these two $\alpha$-classes is also sufficient for the existence of a well-adapted wedge metric of positive scalar curvature.
Linking Graph Entities with Multiplicity and Provenance

Entity linking is a fundamental database problem with applicationsin data integration, data cleansing, information retrieval, knowledge fusion, and knowledge-base population. It is the task of accurately identifying multiple, differing, and possibly contradictingrepresentations of the same real-world entity in data. In this work,we propose an entity linking system capable of linking entitiesacross different databases and mentioned-entities extracted fromtext data. Our entity linking solution, called Certus, uses a graph model to represent the profiles of entities. The graph model is versatile, thus, it is capable of handling multiple values for an attributeor a relationship, as well as the provenance descriptions of thevalues. Provenance descriptions of a value provide the settings ofthe value, such as validity periods, sources, security requirements,etc. This paper presents the architecture for the entity linking system, the logical, physical, and indexing models used in the system,and the general linking process. Furthermore, we demonstrate theperformance of update operations of the physical storage modelswhen the system is implemented in two state-of-the-art databasemanagement systems, HBase and Postgres.
The Entropic Dynamics Approach to Quantum Mechanics

Entropic Dynamics (ED) is a framework in which Quantum Mechanics is derived as an application of entropic methods of inference. In ED the dynamics of the probability distribution is driven by entropy subject to constraints that are codified into a quantity later identified as the phase of the wave function. The central challenge is to specify how those constraints are themselves updated. In this paper we review and extend the ED framework in several directions. A new version of ED is introduced in which particles follow smooth differentiable Brownian trajectories (as opposed to non-differentiable Brownian paths). To construct ED we make use of the fact that the space of probabilities and phases has a natural symplectic structure (i.e., it is a phase space with Hamiltonian flows and Poisson brackets). Then, using an argument based on information geometry, a metric structure is introduced. It is shown that the ED that preserves the symplectic and metric structures—which is a Hamilton-Killing flow in phase space—is the linear Schrödinger equation. These developments allow us to discuss why wave functions are complex and the connections between the superposition principle, the single-valuedness of wave functions, and the quantization of electric charges. Finally, it is observed that Hilbert spaces are not necessary ingredients in this construction. They are a clever but merely optional trick that turns out to be convenient for practical calculations.
IMS-Speech: A Speech to Text Tool

We present the IMS-Speech, a web based tool for German and Englishspeech transcription aiming to facilitate research in various disciplines which require accesses to lexical information in spoken language materials. This tool isbased on modern open source software stack, advanced speech recognition meth-ods and public data resources and is freely available for academic researchers. Theutilized models are built to be generic in order to provide transcriptions of competitive accuracy on a diverse set of tasks and conditions.
Convolutional Humanoid Animation via Deformation

In this paper we present a new deep learning-driven approach to image-based synthesis of animations involving humanoid characters. Unlike previous deep approaches to image-based animation our method makes no assumptions on the type of motion to be animated nor does it require dense temporal input to produce motion. Instead we generate new animations by interpolating between user chosen keyframes, arranged sparsely in time. Utilizing a novel configuration manifold learning approach we interpolate suitable motions between these keyframes. In contrast to previous methods, ours requires less data (animations can be generated from a single youtube video) and is broadly applicable to a wide range of motions including facial motion, whole body motion and even scenes with multiple characters. These improvements serve to significantly reduce the difficulty in producing image-based animations of humanoid characters, allowing even broader audiences to express their creativity.
Users' traffic on two-sided Internet platforms. Qualitative dynamics

Internet platforms' traffic defines important characteristics of platforms, such as pricing of services, advertisements, speed of operations. One can estimate the traffic with the traditional time series models like ARIMA, Holt-Winters, functional and kernel regressions. When using these methods, we usually smooth-out noise and various external effects in the data and obtain short-term predictions of processes. However, these models do not necessarily help us to understand the underlying mechanism and the tendencies of the processes. In this article, we discuss the dynamical system approach to the modeling, which is designed to discover the underlying mechanism and the qualitative properties of the system's phase portrait. We show how to reconstruct the governing differential equations from data. The external effects are modeled as system's parameters (initial conditions). Utilizing this new approach, we construct the models for the volume of users, interacting through Internet platforms, such as "Amazon.com", "Homes.mil" or "Wikipedia.org". Then, we perform qualitative analysis of the system's phase portrait and discuss the main characteristics of the platforms.
Cosmic Ray Ensembles from Ultra-High Energy Photons Propagating in the Galactic and Intergalactic Space

Propagation of ultra-high energy photons in the galactic and intergalactic space gives rise to cascades comprising thousands of photons. Using Monte Carlo simulations, we investigate the development of such cascades in the solar magnetosphere, and find that the photons in the cascades are distributed over hundreds of kilometers as they arrive at the top of the Earth’s atmosphere. We also perform similar study for cascades starting as far as 10 Mpc away from us using relevant magnetic field models. A few photons correlated in time are expected to arrive at the Earth from the latter type of cascade. We present our simulation results and discuss the prospects for detection of these cascades with the Cosmic-Ray Extremely Distributed Observatory.
Comparison of Coupled Nonlinear Oscillator Models for the Transient Response of Power Generating Stations Connected to Low Inertia Systems

Coupled nonlinear oscillators, e.g., Kuramoto models, are commonly used to analyze electrical power systems. The cage model from statistical mechanics has also been used to describe the dynamics of synchronously connected generation stations. Whereas the Kuramoto model is good for describing high inertia grid systems, the cage one allows both high and low inertia grids to be modelled. This is illustrated by comparing both the synchronization time and relaxation towards synchronization of each model by treating their equations of motion in a common framework rooted in the dynamics of many coupled phase oscillators. A solution of these equations via matrix continued fractions is implemented rendering the characteristic relaxation times of a grid-generator system over a wide range of inertia and damping. Following an abrupt change in the dynamical system, the power output and both generator and grid frequencies all exhibit damped oscillations now depending on the (finite) grid inertia. In practical applications, it appears that for a small inertia system the cage model is preferable.
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-the-art performance on CAD model datasets such as ModelNet40 with high accuracy (~92\%). Despite such impressive results, in this paper, we argue that object classification is still a challenging task when objects are framed with real-world settings. To prove this, we introduce ScanObjectNN, a new real-world point cloud object dataset based on scanned indoor scene data. From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions. We identify three key open problems for point cloud object classification, and propose new point cloud classification neural networks that achieve state-of-the-art performance on classifying objects with cluttered background. Our dataset and code are publicly available in our project page https://hkust-vgd.github. io/scanobjectnn/.
General Hand Guidance Framework using Microsoft HoloLens

Hand guidance emerged from the safety requirements for collaborative robots, namely possessing joint-torque sensors. Since then it has proven to be a powerful tool for easy trajectory programming, allowing lay-users to reprogram robots intuitively. Going beyond, a robot can learn tasks by user demonstrations through kinesthetic teaching, enabling robots to generalise tasks and further reducing the need for reprogramming. However, hand guidance is still mostly relegated to collaborative robots. Here we propose a method that does not require any sensors on the robot or in the robot cell, by using a Microsoft HoloLens augmented reality head mounted display. We reference the robot using a registration algorithm to match the robot model to the spatial mesh. The in-built hand tracking and localisation capabilities are then used to calculate the position of the hands relative to the robot. By decomposing the hand movements into orthogonal rotations and propagating it down through the kinematic chain, we achieve a generalised hand guidance without the need to build a dynamic model of the robot itself. We tested our approach on a commonly used industrial manipulator, the KUKA KR-5.
Neural Machine Translation With Noisy Lexical Constraints

In neural machine translation, lexically constrained decoding generates translation outputs strictly including the constraints predefined by users, and it is beneficial to improve translation quality at the cost of more decoding overheads if the constraints are perfect. Unfortunately, those constraints may contain mistakes in real-world situations and incorrect constraints will undermine lexically constrained decoding. In this article, we propose a novel framework that is capable of improving the translation quality even if the constraints are noisy. The key to our framework is to treat the lexical constraints as external memories. More concretely, it encodes the constraints by a memory encoder and then leverages the memories by a memory integrator. Experiments demonstrate that our framework can not only deliver substantial BLEU gains in handling noisy constraints, but also achieve speedup in decoding. These results motivate us to apply our models to a new scenario where the constraints are generated without the help of users. Experiments show that our models can indeed improve the translation quality with the automatically generated constraints.
Lack of close-in, massive planets of main-sequence A-type stars from Kepler

Some theories of planet formation and evolution predict that intermediate-mass stars host more hot Jupiters than Sun-like stars, others reach the conclusion that such objects are very rare. By determining the frequencies of those planets we can test those theories. Based on the analysis of Kepler light curves it has been suggested that about 8 per cent of the intermediate-mass stars could have a close-in substellar companion. This would indicate a very high frequency of such objects. Up to now, there was no satisfactory proof or test of this hypothesis. We studied a previously reported sample of 166 planet candidates around main-sequence A-type stars in the Kepler field. We selected six of them for which we obtained extensive long-term radial velocity measurements with the Alfred Jensch 2-m telescope in Tautenburg and the Perek 2-m telescope in Ondřejov. We derive upper limits of the masses of the planet candidates. We show that we are able to detect this kind of planet with our telescopes and their instrumentation using the example of MASCARA-1 b. With the transit finding pipeline Extrans we confirm that there is no single transit event from a Jupiter-like planet in the light curves of those 166 stars. We furthermore determine that the upper limit for the occurrence rate of close-in, massive planets for A-type stars in the Kepler sample is around 0.75 per cent. We argue that there is currently little evidence for a very high frequency of close-in, massive planets of intermediate-mass stars.
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective

Can an arbitrarily intelligent reinforcement learning agent be kept under control by a human user? Or do agents with sufficient intelligence inevitably find ways to shortcut their reward signal? This question impacts how far reinforcement learning can be scaled, and whether alternative paradigms must be developed in order to build safe artificial general intelligence. In this paper, we use an intuitive yet precise graphical model called causal influence diagrams to formalize reward tampering problems. We also describe a number of modifications to the reinforcement learning objective that prevent incentives for reward tampering. We verify the solutions using recently developed graphical criteria for inferring agent incentives from causal influence diagrams. Along the way, we also compare corrigibility and self-preservation properties of the various solutions, and discuss how they can be combined into a single agent without reward tampering incentives.
Interpretation of the newly observed 
Λb(6146)
 and 
Λb(6152)
 states in a chiral quark model

The strong decays of the low-lying $\lambda$-mode $\Lambda_b(1D,2S)$ and $\Sigma_b(2S)$ states are studied in a chiral quark model. We find that: (i) the newly observed $\Lambda_b(6146/6152)$ resonances in the $\Lambda_b\pi^+\pi^-$ spectrum by the LHCb Collaboration might be explained with the $\lambda$-mode $\Lambda_b(1D)$ states in the quark model. It should be emphasized that whether the structure in the $\Lambda_b\pi^+\pi^-$ spectrum correspond to two states or one state should be further clarified with more observations in future experiments. (ii) The $\Lambda_b(2S)$ $|J^P = \frac{1}{2}^+,0\rangle$ state mainly decays into $\Sigma_b^*\pi$ channel, which may be an ideal channel for searching for this $\Lambda_b(2S)$ state in future experiments. (iii) The $\Sigma_b(2S)$ $|J^P = \frac{1}{2}^+,1 \rangle$ and $|J^P = \frac{3}{2}^+,1 \rangle$ dominantly decay into $\Lambda_b\pi$ with $\Gamma \simeq 3.82$ MeV and $\Gamma \simeq 4.72$ MeV, respectively.
Evaluation of a Recommender System for Assisting Novice Game Designers

Game development is a complex task involving multiple disciplines and technologies. Developers and researchers alike have suggested that AI-driven game design assistants may improve developer workflow. We present a recommender system for assisting humans in game design as well as a rigorous human subjects study to validate it. The AI-driven game design assistance system suggests game mechanics to designers based on characteristics of the game being developed. We believe this method can bring creative insights and increase user’s productivity. We conducted quantitative studies that showed the recommender system increases users’ levels of accuracy and computational affect, and decreases their levels of workload.
SDM-NET

We introduce SDM-NET, a deep generative neural network which produces structured deformable meshes. Specifically, the network is trained to generate a spatial arrangement of closed, deformable mesh parts, which respects the global part structure of a shape collection, e.g., chairs, airplanes, etc. Our key observation is that while the overall structure of a 3D shape can be complex, the shape can usually be decomposed into a set of parts, each homeomorphic to a box, and the finer-scale geometry of the part can be recovered by deforming the box. The architecture of SDM-NET is that of a two-level variational autoencoder (VAE). At the part level, a PartVAE learns a deformable model of part geometries. At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring the coherence between global shape structure and surface details. Through extensive experiments and comparisons with the state-of-the-art deep generative models of shapes, we demonstrate the superiority of SDM-NET in generating meshes with visual quality, flexible topology, and meaningful structures, benefiting shape interpolation and other subsequent modeling tasks.
The Dual Complex of a Semi-log Canonical Surface

Semi-log canonical varieties are a higher-dimensional analogue of stable curves. They are the varieties appearing as the boundary $\Delta $ of a log canonical pair $(X,\Delta )$ and also appear as limits of canonically polarized varieties in moduli theory. For certain three-fold pairs $(X,\Delta ),$ we show how to compute the PL homeomorphism type of the dual complex of a dlt minimal model directly from the normalization data of $\Delta $.
Beyond The Wall Street Journal: Anchoring and Comparing Discourse Signals across Genres

Recent research on discourse relations has found that they are cued not only by discourse markers (DMs) but also by other textual signals and that signaling information is indicative of genres. While several corpora exist with discourse relation signaling information such as the Penn Discourse Treebank (PDTB, Prasad et al. 2008) and the Rhetorical Structure Theory Signalling Corpus (RST-SC, Das and Taboada 2018), they both annotate the Wall Street Journal (WSJ) section of the Penn Treebank (PTB, Marcus et al. 1993), which is limited to the news domain. Thus, this paper adapts the signal identification and anchoring scheme (Liu and Zeldes, 2019) to three more genres, examines the distribution of signaling devices across relations and genres, and provides a taxonomy of indicative signals found in this dataset.