Miracle-dz/UniFAR-FTUs huggingface dataset:
IAE-Net: Integral Autoencoders for Discretization-Invariant Learning

Discretization invariant learning aims at learning in the inﬁnite-dimensional function spaces with the capacity to process heterogeneous discrete representations of functions as inputs and/or outputs of a learning model. This paper proposes a novel deep learning framework based on integral autoencoders (IAE-Net) for discretization invariant learning. The basic building block of IAE-Net consists of an encoder and a decoder as integral transforms with data-driven kernels, and a fully connected neural network between the encoder and decoder. This basic building block is applied in parallel in a wide multi-channel structure, which is repeatedly composed to form a deep and densely connected neural network with skip connections as IAE-Net. IAE-Net is trained with randomized data augmentation that generates training data with heterogeneous structures to facilitate the performance of discretization invariant learning. The proposed IAE-Net is tested with various applications in predictive data science, solving forward and inverse problems in scientiﬁc computing, and signal/image processing. Compared with alternatives in the literature, IAE-Net achieves state-of-the-art performance in existing applications and creates a wide range of new applications where existing methods fail.
In-Place Rotation for Enhancing Snake-like Robot Mobility

Gaits engineered for snake-like robots to rotate in-place instrumentally fill a gap in the set of locomotive gaits that have traditionally prioritized translation. This paper designs a Turn-in-Place gait and demonstrates the ability of a shape-centric modeling framework to capture the gait's locomotive properties. Shape modeling for turning involves a time-varying continuous body curve described by a standing wave. Presumed viscous robot-ground frictional interactions lead to body dynamics conditioned on the time-varying shape model. The dynamic equations describing the Turn-in-Place gait are validated by an articulated snake-like robot using a physics-based simulator and a physical robot. The results affirm the shape-centric modeling framework's capacity to model a variety of snake-like robot gaits with fundamentally different body-ground contact patterns. As an applied demonstration, example locomotion scenarios partner the shape-centric Turn-in-Place gait with a Rectilinear gait for maneuvering through constrained environments based on a multi-modal locomotive planning strategy. Unified shape-centric modeling facilitates trajectory planning and tracking for a snake-like robot to successfully negotiate non-trivial obstacle configurations.
Deep Residual Reinforcement Learning based Autonomous Blimp Control

Blimps are well suited to perform long-duration aerial tasks as they are energy efficient, relatively silent and safe. To address the blimp navigation and control task, in previous work we developed a hardware and software-in-the-loop framework and a PID-based controller for large blimps in the presence of wind disturbance. However, blimps have a deformable structure and their dynamics are inherently non-linear and time-delayed, making PID controllers difficult to tune. Thus, often resulting in large tracking errors. Moreover, the buoyancy of a blimp is constantly changing due to variations in ambient temperature and pressure. To address these issues, in this paper we present a learning-based framework based on deep residual reinforcement learning (DRRL), for the blimp control task. Within this framework, we first employ a PID controller to provide baseline performance. Subsequently, the DRRL agent learns to modify the PID decisions by interaction with the environment. We demonstrate in simulation that DRRL agent consistently improves the PID performance. Through rigorous simulation experiments, we show that the agent is robust to changes in wind speed and buoyancy. In real-world experiments, we demonstrate that the agent, trained only in simulation, is sufficiently robust to control an actual blimp in windy conditions. We openly provide the source code of our approach at https://github.com/robot-perception-group/AutonomousBlimpDRL. Video demonstration is provided at https://youtu.be/EMC4KnlH0yI.
Binary neutron star mergers as a probe of quark-hadron crossover equations of state

It is anticipated that the gravitational radiation detected in future gravitational wave (GW) detectors from binary neutron star (NS) mergers can probe the high-density equation of state (EOS). We perform the first simulations of binary NS mergers which adopt various parametrizations of the quark-hadron crossover (QHC) EOS. These are constructed from combinations of a hadronic EOS ($n_{b}<2~n_0$) and a quark-matter EOS ($n_{b}>~5~n_0$), where $n_{b}$ and $n_0$ are the baryon number density and the nuclear saturation density, respectively. At the crossover densities ($2~ n_0<n_{b}<5~ n_0$) the QHC EOSs continuously soften, while remaining stiffer than hadronic and first-order phase transition EOSs, achieving the stiffness of strongly correlated quark matter. This enhanced stiffness leads to significantly longer lifetimes of the postmerger NS than that for a pure hadronic EOS. We find a dual nature of these EOSs such that their maximum chirp GW frequencies $f_{max}$ fall into the category of a soft EOS while the dominant peak frequencies ($f_{peak}$) of the postmerger stage fall in between that of a soft and stiff hadronic EOS. An observation of this kind of dual nature in the characteristic GW frequencies will provide crucial evidence for the existence of strongly interacting quark matter at the crossover densities for QCD.
Stellar Obliquities in Exoplanetary Systems

The rotation of a star and the revolutions of its planets are not necessarily aligned. This article reviews the measurement techniques, key findings, and theoretical interpretations related to the obliquities (spin–orbit angles) of planet-hosting stars. The best measurements are for stars with short-period giant planets, which have been found on prograde, polar, and retrograde orbits. It seems likely that dynamical processes such as planet–planet scattering and secular perturbations are responsible for tilting the orbits of close-in giant planets, just as those processes are implicated in exciting orbital eccentricities. The observed dependence of the obliquity on orbital separation, planet mass, and stellar structure suggests that in some cases, tidal dissipation damps a star’s obliquity within its main-sequence lifetime. The situation is not as clear for stars with smaller or wider-orbiting planets. Although the earliest measurements of such systems tended to find low obliquities, some glaring exceptions are now known in which the star’s rotation is misaligned with respect to the coplanar orbits of multiple planets. In addition, statistical analyses based on projected rotation velocities and photometric variability have found a broad range of obliquities for F-type stars hosting compact multiple-planet systems. The results suggest it is unsafe to assume that stars and their protoplanetary disks are aligned. Primordial misalignments might be produced by neighboring stars or more complex events that occur during the epoch of planet formation.
Architecture Analysis for Regenerative Energy Management in an Electromechanical Actuation System Design

Electromechanical actuation is a form of actuation in which the actuator piston is driven by an electrical motor rather than a hydraulic system. The use of electromechanical actuators (EMAs) will eliminate hydraulic lines which are heavy and costly to maintain. Another benefit of EMAs is that they can regenerate energy under certain loading conditions. Typically, the regenerated energy is simply fed to resistor where it is dissipated as heat. This study explores the option of storing regenerated energy. Several storage architectures are considered, and then the optimal storage architecture is selected. This optimal architecture is compared to the dissipation architecture being used today. The dissipation and storage architectures will be evaluated based upon their volume, weight, and thermal characteristics.
Multi-Party Quantum Purity Distillation with Bounded Classical Communication

We consider the task of distilling local purity from a noisy quantum state ρ<sup>ABC</sup>, wherein we provide a protocol for three parties, Alice, Bob and Charlie, to distill local purity (at a rate P) from many independent copies of a given quantum state ρ<sup>ABC</sup>. The three parties have access to their respective subsystems of ρ<sup>ABC</sup>, and are only allowed to use local unitary operations. In addition, Alice and Bob can communicate with Charlie using a one-way multiple-access dephasing channel of link rates R<inf>1</inf> and R<inf>2</inf>, respectively. The objective of the protocol is to minimize the usage of the dephasing channel (in terms of rates R<inf>1</inf> and R<inf>2</inf>) while maximizing the asymptotic purity that can be jointly distilled from ρ<sup>ABC</sup>. To achieve this, we employ ideas from distributed measurement compression protocols, and in turn, characterize a set of sufficient conditions on (P, R<inf>1</inf>,R<inf>2</inf>) in terms of quantum information theoretic quantities such that P amount of purity can be distilled using rates R<inf>1</inf> and R<inf>2</inf>.
GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains

Cereal grains are a vital part of human diets and are important commodities for people's livelihood and international trade. Grain Appearance Inspection (GAI) serves as one of the crucial steps for the determination of grain quality and grain stratification for proper circulation, storage and food processing, etc. GAI is routinely performed manually by qualified inspectors with the aid of some hand tools. Automated GAI has the benefit of greatly assisting inspectors with their jobs but has been limited due to the lack of datasets and clear definitions of the tasks. In this paper we formulate GAI as three ubiquitous computer vision tasks: fine-grained recognition, domain adaptation and out-of-distribution recognition. We present a large-scale and publicly available cereal grains dataset called GrainSpace. Specifically, we construct three types of device prototypes for data acquisition, and a total of 5.25 million images determined by professional inspectors. The grain samples including wheat, maize and rice are collected from five countries and more than 30 regions. We also develop a comprehensive benchmark based on semi-supervised learning and self-supervised learning techniques. To the best of our knowledge, GrainSpace is the first publicly released dataset for cereal grain inspection, https://github.com/hellodfan/GrainSpace.
Semantic Norm Recognition and its application to Portuguese Law

Being able to clearly interpret legal texts and fully understanding our rights, obligations and other legal norms has become progressively more important in the digital society. However, simply giving citizens access to the laws is not enough, as there is a need to provide meaningful information that cater to their specific queries and needs. For this, it is necessary to extract the relevant semantic information present in legal texts. Thus, we introduce the SNR (Semantic Norm Recognition) system, an automatic semantic information extraction system trained on a domain-specific (legal) text corpus taken from Portuguese Consumer Law. The SNR system uses the Portuguese Bert (BERTimbau) and was trained on a legislative Portuguese corpus. We demonstrate how our system achieved good results (81.44\% F1-score) on this domain-specific corpus, despite existing noise, and how it can be used to improve downstream tasks such as information retrieval.
Modeling hadronization using machine learning

We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques. We successfully implement, validate, and train a conditional sliced-Wasserstein autoencoder to replicate the Pythia generated kinematic distributions of first-hadron emissions, when the Lund string model of hadronization implemented in Pythia is restricted to the emissions of pions only. The trained models are then used to generate the full hadronization chains, with an IR cutoff energy imposed externally. The hadron multiplicities and cumulative kinematic distributions are shown to match the Pythia generated ones. We also discuss possible future generalizations of our results.
Optimal Methods for Risk Averse Distributed Optimization

This paper studies the communication complexity of convex risk averse optimization over a network. The problem generalizes the well-studied risk-neutral ﬁnite-sum distributed optimization problem and its importance stems from the need to handle risk in an uncertain environment. For algorithms in the literature, there exists a gap in communication complexities for solving risk-averse and risk-neutral problems. We propose two distributed algorithms, namely the distributed risk averse optimization (DRAO) method and the distributed risk averse optimization with sliding (DRAO-S) method, to close the gap. Speciﬁcally, the DRAO method achieves the optimal communication complexity by assuming a certain saddle point subproblem can be easily solved in the server node. The DRAO-S method removes the strong assumption by introducing a novel saddle point sliding subroutine which only requires the projection over the ambiguity set P . We observe that the number of P -projections performed by DRAO-S is optimal. Moreover, we develop matching lower complexity bounds to show that communication complexities of both DRAO and DRAO-S are not improvable. Numerical experiments are conducted to demonstrate the encouraging empirical performance of the DRAO-S method.
Parameterized Two‐Qubit Gates for Enhanced Variational Quantum Eigensolver

The variational quantum eigensolver is a prominent hybrid quantum‐classical algorithm expected to impact near‐term quantum devices. They are usually based on a circuit ansatz consisting of parameterized single‐qubit gates and fixed two‐qubit gates. The effect of parameterized two‐qubit gates in the variational quantum eigensolver is studied. A variational quantum eigensolver algorithm is simulated using fixed and parameterized two‐qubit gates in the circuit ansatz and it is shown that the parameterized versions outperform the fixed versions, both when it comes to best energy and reducing outliers, for a range of Hamiltonians with applications in quantum chemistry and materials science.
Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guide

Understanding the neural basis of language comprehension in the brain has been a long-standing goal of various scientific research programs. Recent advances in language modelling and in neuroimaging methodology promise potential improvements in both the investigation of language's neurobiology and in the building of better and more human-like language models. This survey traces a line from early research linking Event Related Potentials and complexity measures derived from simple language models to contemporary studies employing Artificial Neural Network models trained on large corpora in combination with neural response recordings from multiple modalities using naturalistic stimuli.
CUBES: A Parallel Synthesizer for SQL Using Examples

In recent years, more people have seen their work depend on data manipulation tasks. However, many of these users do not have the background in programming required to write complex programs, particularly SQL queries. One way of helping these users is automatically synthesizing the SQL query given a small set of examples. Several program synthesizers for SQL have been recently proposed, but they do not leverage multicore architectures. This paper proposes CUBES, a parallel program synthesizer for the domain of SQL queries using input-output examples. Since input-output examples are an under-specification of the desired SQL query, sometimes, the synthesized query does not match the user's intent. CUBES incorporates a new disambiguation procedure based on fuzzing techniques that interacts with the user and increases the confidence that the returned query matches the user intent. We perform an extensive evaluation on around 4000 SQL queries from different domains. Experimental results show that our sequential version can solve more instances than other state-of-the-art SQL synthesizers. Moreover, the parallel approach can scale up to 16 processes with super-linear speedups for many hard instances. Our disambiguation approach is critical to achieving an accuracy of around 60%, significantly larger than other SQL synthesizers.
Analysis of multipartite entanglement distribution using a central quantum-network node

We study the performance (rate and fidelity) of distributing multipartite entangled states in a quantum network through the use of a central node. Specifically, we consider the scenario where the multipartite entangled state is first prepared locally at a central node, and then transmitted to the end nodes of the network through quantum teleportation. As our first result, we present leading-order analytical expressions and lower bounds for both the rate and fidelity at which a specific class of multipartite entangled states, namely Greenberger-Horne-Zeilinger (GHZ) states, are distributed. Our analytical expressions for the fidelity accurately account for time-dependent depolarizing noise encountered by individual quantum bits while stored in quantum memory, as verified using Monte Carlo simulations. As our second result, we compare the performance to the case where the central node is an entanglement switch and the GHZ state is created by the end nodes in a distributed fashion. Apart from these two results, we outline how the teleportation-based scheme could be physically implemented using trapped ions or nitrogen-vacancy centers in diamond.
An Audio-Visual Attention Based Multimodal Network for Fake Talking Face Videos Detection

DeepFake based digital facial forgery is threatening the public media security, especially when lip manipulation has been used in talking face generation, the difficulty of fake video detection is further improved. By only changing lip shape to match the given speech, the facial features of identity is hard to be discriminated in such fake talking face videos. Together with the lack of attention on audio stream as the prior knowledge, the detection failure of fake talking face generation also becomes inevitable. Inspired by the decision-making mechanism of human multisensory perception system, which enables the auditory information to enhance post-sensory visual evidence for informed decisions output, in this study, a fake talking face detection framework FTFDNet is proposed by incorporating audio and visual representation to achieve more accurate fake talking face videos detection. Furthermore, an audio-visual attention mechanism (AVAM) is proposed to discover more informative features, which can be seamlessly integrated into any audio-visual CNN architectures by modularization. With the additional AVAM, the proposed FTFDNet is able to achieve a better detection performance on the established dataset (FTFDD). The evaluation of the proposed work has shown an excellent performance on the detection of fake talking face videos, which is able to arrive at a detection rate above 97%.
Knowledge-Enriched Attention Network With Group-Wise Semantic for Visual Storytelling

As a technically challenging topic, visual storytelling aims at generating an imaginary and coherent story with narrative multi-sentences from a group of relevant images. Existing methods often generate direct and rigid descriptions of apparent image-based contents, because they are not capable of exploring implicit information beyond images. Hence, these schemes could not capture consistent dependencies from holistic representation, impairing the generation of reasonable and fluent stories. To address these problems, a novel knowledge-enriched attention network with group-wise semantic model is proposed. Three main novel components are designed and supported by substantial experiments to reveal practical advantages. First, a knowledge-enriched attention network is designed to extract implicit concepts from external knowledge system, and these concepts are followed by a cascade cross-modal attention mechanism to characterize imaginative and concrete representations. Second, a group-wise semantic module with second-order pooling is developed to explore the globally consistent guidance. Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion. Substantial experiments on the visual storytelling datasets with both objective and subjective evaluation metrics demonstrate the superior performance of the proposed scheme as compared with other state-of-the-art methods. The source code of this work can be found in https://mic.tongji. edu.cn.
Adaptive Trajectory Prediction via Transferable GNN

Pedestrian trajectory prediction is an essential component in a wide range of AI applications such as autonomous driving and robotics. Existing methods usually assume the training and testing motions follow the same pattern while ignoring the potential distribution differences (e.g., shopping mall and street). This issue results in inevitable performance decrease. To address this issue, we propose a novel Transferable Graph Neural Network (TGNN) frame-work, which jointly conducts trajectory prediction as well as domain alignment in a unified framework. Specifically, a domain-invariant GNN is proposed to explore the structural motion knowledge where the domain-specific knowledge is reduced. Moreover, an attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representations for knowledge transfer. By this way, disparities across different trajectory domains will be better alleviated. More challenging while practical trajectory prediction experiments are designed, and the experimental results verify the superior performance of our proposed model. To the best of our knowledge, our work is the pioneer which fills the gap in benchmarks and techniques for practical pedestrian trajectory prediction across different domains.
Domain Generalisation for Object Detection

Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain specific features, so that a model can generalise well on previously unseen target domains. This paper studies domain generalisation in the object detection setting. We propose new terms for handling both the bounding box detector and domain belonging, and incorporate them with consistency regularisation. This allows us to learn a domain agnostic feature representation for object detection, applicable to the problem of domain generalisation. The proposed approach is evaluated using four standard object detection datasets with available domain metadata, namely GWHD, Cityscapes, BDD100K, Sim10K and exhibits consistently superior generalisation performance over baselines.