Miracle-dz/UniFAR-FTUs huggingface dataset:
Amortised Inference in Structured Generative Models with Explaining Away

A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data. Such structure can be expressed in the pattern of interactions between explanatory latent variables captured through a probabilistic graphical model. Although the learning of structured graphical models has a long history, much recent work in unsupervised modelling has instead emphasised flexible deep-network-based generation, either transforming independent latent generators to model complex data or assuming that distinct observed variables are derived from different latent nodes. Here, we extend the output of amortised variational inference to incorporate structured factors over multiple variables, able to capture the observation-induced posterior dependence between latents that results from “explaining away” and thus allow complex observations to depend on multiple nodes of a structured graph. We show that appropriately parameterised factors can be combined efficiently with variational message passing in elaborate graphical structures. We instantiate the framework based on Gaussian Process Factor Analysis models, and empirically evaluate its improvement over existing methods on synthetic data with known generative processes. We then fit the structured model to high-dimensional neural spiking time-series from the hippocampus of freely moving rodents, demonstrating that the model identifies latent signals that correlate with behavioural covariates.
Consistent Selection of the Number of Groups in Panel Models via Sample-Splitting

Group number selection is a key question for group panel data modelling. In this work, we develop a cross validation method to tackle this problem. Specifically, we split the panel data into a training dataset and a testing dataset on the time span. We first use the training dataset to estimate the parameters and group memberships. Then we apply the fitted model to the testing dataset and then the group number is estimated by minimizing certain loss function values on the testing dataset. We design the loss functions for panel data models either with or without fixed effects. The proposed method has two advantages. First, the method is totally data-driven thus no further tuning parameters are involved. Second, the method can be flexibly applied to a wide range of panel data models. Theoretically, we establish the estimation consistency by taking advantage of the optimization property of the estimation algorithm. Experiments on a variety of synthetic and empirical datasets are carried out to further illustrate the advantages of the proposed method.
Using Quantum computers to speed up dynamic testing of software

Software under test can be analyzed dynamically, while it is being executed, to find defects. However, as the number and possible values of input parameters increase, the cost of dynamic testing rises. This paper examines whether quantum computers (QCs) can help speed up the dynamic testing of programs written for classical computers (CCs). To accomplish this, an approach is devised involving the following three steps: (1) converting a classical program to a quantum program; (2) computing the number of inputs causing errors, denoted by K, using a quantum counting algorithm; and (3) obtaining the actual values of these inputs using Grover’s search algorithm. This approach can accelerate exhaustive and non-exhaustive dynamic testing techniques. On the CC, the computational complexity of these techniques is O(N), where N represents the count of combinations of input parameter values passed to the software under test. In contrast, on the QC, the complexity is O(ε−1 √N/K), where ε is a relative error of measuring K. The paper illustrates how the approach can be applied and discusses its limitations. Moreover, it provides a toy example executed on a simulator and an actual QC. This paper may be of interest to academics and practitioners as the approach presented in the paper may serve as a starting point for exploring the use of QC for dynamic testing of CC code.
HandMime: Sign Language Fingerspelling Acquisition via Imitation Learning

—Learning ﬁne-grained movements is among the most challenging topics in robotics. This holds true especially for robotic hands. Robotic sign language acquisition or, more speciﬁcally, ﬁngerspelling sign language acquisition in robots can be considered a speciﬁc instance of such challenge. In this paper, we propose an approach for learning dexterous motor imitation from videos examples, without the use of any additional information. We build an URDF model of a robotic hand with a single actuator for each joint. By leveraging pre-trained deep vision models, we extract the 3D pose of the hand from RGB videos. Then, using state-of-the-art re-inforcement learning algorithms for motion imitation (namely, proximal policy optimisation), we train a policy to reproduce the movement extracted from the demonstrations. We identify the best set of hyperparameters to perform imitation based on a reference motion. Additionally, we demonstrate the ability of our approach to generalise over 6 different ﬁngerspelled letters.
Fully Resolved Numerical Simulations of Fused Deposition Modeling. Part I-Fluid Flow

Purpose — This paper presents a first step toward developing a comprehensive methodology for fully resolved numerical simulations of fusion deposition modeling. Design/methodology/approach — A front-tracking/finite volume method previously developed for simulations of multiphase flows is extended to model the injection of hot polymer and its cooling down. Findings — The accuracy and convergence properties of the new method are tested by grid refinement and the method is shown to produce convergent solutions for the shape of the filament, the temperature distribution, contact area and reheat region when new filaments are deposited on top of previously laid down filaments. Research limitations/implications — The present paper focuses on modeling the fluid flow and the cooling. The modeling of solidification, volume changes and residual stresses will be described in Part II. Practical implications — The ability to carry our fully resolved numerical simulations of the fusion deposition process is expected to help explore new deposition strategies and to provide the “ground truth” for the development of reduced order models. Originality/value — The present paper is the first fully resolved simulation of the deposition in fusion filament modeling.
CosmoGridV1: a simulated 𝗐CDM theory prediction for map-level cosmological inference

We present CosmoGridV1: a large set of lightcone simulations for map-level cosmological inference with probes of large scale structure. It is designed for cosmological parameter measurement based on Stage-III photometric surveys with non-Gaussian statistics and machine learning. CosmoGridV1 spans the wCDM model by varying Ωm, σ 8, w 0, H 0, n s, Ω b , and assumes three degenerate neutrinos with fixed ∑ mν = 0.06 eV. This space is covered by 2500 grid points on a Sobol sequence. At each grid point, we run 7 simulations with PkdGrav3 and store 69 particle maps at nside = 2048 up to z = 3.5, as well as halo catalog snapshots. The fiducial cosmology has 200 independent simulations, along with their stencil derivatives. An important part of CosmoGridV1 is the benchmark set of 28 simulations, which include larger boxes, higher particle counts, and higher redshift resolution of shells. They allow for testing if new types of analyses are sensitive to choices made in CosmoGridV1. We add baryon feedback effects on the map level, using shell-based baryon correction model. The shells are used to create maps of weak gravitational lensing, intrinsic alignment, and galaxy clustering, using the UFalcon code. The main part of CosmoGridV1 are the raw particle count shells that can be used to create full-sky maps for a given n(z). We also release projected maps for a Stage-III forecast, as well as maps used previously in KiDS-1000 deep learning constraints with CosmoGridV1. The data is available at http://www.cosmogrid. ai/.
A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion

Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map. In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map. Traditionally, geometric model-based techniques were used to tackle the SLAM problem, which tends to be error-prone under challenging environments. Recent advancements in computer vision, such as deep learning techniques, have provided a data-driven approach to tackle the Visual-SLAM problem. This review summarises recent advancements in the Visual-SLAM domain using various learning-based methods. We begin by providing a concise overview of the geometric model-based approaches, followed by technical reviews on the current paradigms in SLAM. Then, we present the various learning-based approaches to collecting sensory inputs from mobile robots and performing scene understanding. The current paradigms in deep-learning-based semantic understanding are discussed and placed under the context of Visual-SLAM. Finally, we discuss challenges and further opportunities in the direction of learning-based approaches in Visual-SLAM.
Pay Attention to Hard Trials

Performance of speaker recognition systems is evaluated on test trials. Although as crucial as rulers for tailors, trials have not been carefully treated so far, and most existing benchmarks compose trials by naive cross-pairing. In this paper, we argue that the cross-pairing approach produces overwhelming easy trials, which in turn leads to potential bias in system and technique comparison. To solve the problem, we advocate more attention to hard trials. We present an SVM-based approach to identifying hard trials and use it to construct new evaluation sets for VoxCeleb1 and SITW. With the new sets, we can re-evaluate the contribution of some recent technologies. The code and the identified hard trials will be published online at http://project.cslt. org.
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification

Machine learning methods must be trusted to make appropriate decisions in real-world environments, even when faced with out-of-distribution (OOD) samples. Many current approaches simply aim to detect OOD examples and alert the user when an unrecognized input is given. However, when the OOD sample significantly overlaps with the training data, a binary anomaly detection is not interpretable or explainable, and provides little information to the user. We propose a new model for OOD detection that makes predictions at varying levels of granularity as the inputs become more ambiguous, the model predictions become coarser and more conservative. Consider an animal classifier that encounters an unknown bird species and a car. Both cases are OOD, but the user gains more information if the classifier recognizes that its uncertainty over the particular species is too large and predicts bird instead of detecting it as OOD. Furthermore, we diagnose the classifiers performance at each level of the hierarchy improving the explainability and interpretability of the models predictions. We demonstrate the effectiveness of hierarchical classifiers for both fine- and coarse-grained OOD tasks.
Dynamics of wave packets in the quantum Lorentz gas

Title of Dissertation: Dynamics of wave packets in the quantum Lorentz gas Arseni Goussev, Doctor of Philosophy, 2005 Dissertation directed by: Prof. J. Robert Dorfman Department of Physics This dissertation addresses the dynamics of a quantum particle moving in an array of fixed scatterers. The system is known as the Lorentz gas. The scatterers are taken to be twoor three-dimensional hard-spheres. The quantum Lorentz gas is analyzed in two dynamical regimes: (i) semiclassical regime, and (ii) high-energy diffraction regime. In both regimes the dynamics of the quantum particle is found to be determined by properties characterizing chaotic dynamics of the counterpart classical Lorentz gas. Thus, this dissertation provides an attempt to more deeply understand the role that classical chaos plays in quantum mechanics of nonintegrable systems. In the semiclassical regime, the quantum particle is represented by a small Gaussian wave packet immersed in the array of scatterers. The de Broglie wavelength of the particle is considered to be much smaller than both the scatterer size and the typical separation between scatterers. It is found that for times, during which the wave packet size remains smaller than the scatterer size, the spreading of the quantum wave packet is exponential in time, and the spreading rate is determined by the sum of positive Lyapunov exponents of the corresponding classical system. The high-energy diffraction approximation allows one to analytically describe the dynamics of large wave packets in dilute scattering systems for times far beyond the Ehrenfest time. The latter is defined as the time during which the evolution of the wave packet is predominantly classical-like. The following two conditions are satisfied by the system in the high-energy diffraction regime: (i) the ratio of the particle’s de Broglie wavelength to the scatterer size is much smaller than unity, and (ii) this ratio is much larger than the ratio of the scatterer size to the typical separation between scatterers. The time-dependent autocorrelation function is calculated for wave packets in hard-disk and hard-sphere geometrically open billiard systems. The envelope of the autocorrelation function is shown to decay exponentially with time, with the decay rate determined by the mean Lyapunov exponents and the Kolmogorov-Sinai entropy of the counterpart classical system. Dynamics of wave packets in the quantum Lorentz gas
Data-driven invariant subspace identification for black-box switched linear systems

We present an algorithmic framework for the identification of candidate invariant subspaces for switched linear systems. Namely, the framework allows to compute an orthonormal basis in which the matrices of the system are close to block-triangular matrices, based on a finite set of observed one-step trajectories and with a priori confidence level. The link between the existence of an invariant subspace and a common block-triangularization of the system matrices is well known. Under some assumptions on the system, one can also infer the existence of an invariant subspace when the matrices are close to be block-triangular. Our approach relies on quadratic Lyapunov analysis and recent tools in scenario optimization. We present two applications of our results for problems of consensus and opinion dynamics; the first one allows to identify the disconnected components in a switching hidden network, while the second one identifies the stationary opinion vector of a switching gossip process with antagonistic interactions.
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning

With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP) that identifies the helpfulness score of multimodal product reviews has become a research hotspot. Previous work on this task focuses on attention-based modality fusion, information integration, and relation modeling, which primarily exposes the following drawbacks: 1) the model may fail to capture the really essential information due to its indiscriminate attention formulation; 2) lack appropriate modeling methods that takes full advantage of correlation among provided data. In this paper, we propose SANCL: Selective Attention and Natural Contrastive Learning for MRHP. SANCL adopts a probe-based strategy to enforce high attention weights on the regions of greater significance. It also constructs a contrastive learning framework based on natural matching properties in the dataset. Experimental results on two benchmark datasets with three categories show that SANCL achieves state-of-the-art baseline performance with lower memory consumption.
Population-Based Hierarchical Non-Negative Matrix Factorization for Survey Data

Motivated by the problem of identifying potential hierarchical population structure on modern survey data containing a wide range of complex data types, we introduce population-based hierarchical non-negative matrix factorization (PHNMF). PHNMF is a variant of hierarchical non-negative matrix factorization based on feature similarity. As such, it enables an automatic and interpretable approach for identifying and understanding hierarchical structure in a data matrix constructed from a wide range of data types. Our numerical experiments on synthetic and real survey data demonstrate that PHNMF can recover latent hierarchical population structure in complex data with high accuracy. Moreover, the recovered subpopulation structure is meaningful and can be useful for improving downstream inference.
Evolution, Structure, and Topology of Self-generated Turbulent Reconnection Layers

We present a 3D MHD simulation of two merging flux ropes exhibiting self-generated and self-sustaining turbulent reconnection (SGTR) that is fully 3D and fast. The exploration of SGTR is crucial for understanding the relationship between MHD turbulence and magnetic reconnection in astrophysical contexts including the solar corona. We investigate the pathway toward SGTR and apply novel tools to analyze the structure and topology of the reconnection layer. The simulation proceeds from 2.5D Sweet–Parker reconnection to 2.5D nonlinear tearing, followed by a dynamic transition to a final SGTR phase that is globally quasi-stationary. The transition phase is dominated by a kink instability of a large “cat-eye” flux rope and the proliferation of a broad stochastic layer. The reconnection layer has two general characteristic thickness scales, which correlate with the reconnection rate and differ by a factor of approximately six: an inner scale corresponding with current and vorticity densities, turbulent fluctuations, and outflow jets, and an outer scale associated with field line stochasticity. The effective thickness of the reconnection layer is the inner scale of the effective reconnection electric field produced by turbulent fluctuations, not the stochastic thickness. The dynamics within the reconnection layer are closely linked with flux rope structures that are highly topologically complicated. Explorations of the flux rope structures and distinctive intermediate regions between the inner core and stochastic separatrices (“SGTR wings”) are potentially key to understanding SGTR. The study concludes with a discussion on the apparent dualism between plasmoid-mediated and stochastic perspectives on SGTR.
Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance

Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point's movement. Scene flow estimation is used in various applications such as autonomous driving fields, activity recognition, and virtual reality fields. As it is challenging to annotate scene flow with ground truth for real-world data, this leaves no real-world dataset available to provide a large amount of data with ground truth for scene flow estimation. Therefore, many works use synthesized data to pre-train their network and real-world LiDAR data to finetune. Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network. Supervised odometry provides more accurate shared cost volume for scene flow. In addition, the proposed network has mask-weighted warp layers to get a more accurate predicted point cloud. The warp operation means applying an estimated pose transformation or scene flow to a source point cloud to obtain a predicted point cloud and is the key to refining scene flow from coarse to fine. When performing warp operations, the points in different states use different weights for the pose transformation and scene flow transformation. We classify the states of points as static, dynamic, and occluded, where the static masks are used to divide static and dynamic points, and the occlusion masks are used to divide occluded points. The mask-weighted warp layer indicates that static masks and occlusion masks are used as weights when performing warp operations. Our designs are proved to be effective in ablation experiments. The experiment results show the promising prospect of an odometry-assisted unsupervised learning method for 3D scene flow in real-world data.
A challenge-based survey of e-recruitment recommendation systems

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences. Therefore, e-recruitment recommendation systems could greatly impact job seekers' careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the companies' competitive edge in the market. Hence, the domain of e-recruitment recommendation deserves specific attention. Existing surveys on this topic tend to discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach, which we believe might be more practical to developers facing a concrete e-recruitment design task with a specific set of challenges, as well as to researchers looking for impactful research projects in this domain. We first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider promising in the e-recruitment recommendation domain.
Experiments and a User Study for Hierarchical Drawings of Graphs

We present a detailed hierarchical graph drawing technique that is based on the Path Based Framework (PBF). Extensive edge bundling is applied to draw all edges of the graph and the height of the drawing is minimized using compaction. We present experimental results and a user study for hierarchical drawings of graphs that show the usefulness of the new drawing technique. The drawings produced by the new framework are compared to drawings produced by the well known Sugiyama framework in terms of area, number of bends, number of crossings, and execution time. The new algorithm runs very fast and produces drawings that are readable and more efficient in terms of area and number of bends. Furthermore, since there are advantages (and disadvantages) to both frameworks, we performed a user study. The results show that the drawings produced by the new framework are well received in terms of clarity, readability, and usability. Hence, the new technique offers an interesting alternative to drawing hierarchical graphs, and is especially useful in applications where user defined paths are important and need to be highlighted.
A Note on the Efficient Evaluation of PAC-Bayes Bounds

When utilising PAC-Bayes theory for risk certiﬁcation, it is usually necessary to estimate and bound the Gibbs risk of the PAC-Bayes posterior. Many works in the literature employ a method for this which requires a large number of passes of the dataset, incurring high computational cost. This manuscript presents a very general alternative which makes computational savings on the order of the dataset size.