Miracle-dz/UniFAR-FTUs huggingface dataset:
Poor Man’s Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference

Machine translation quality estimation (QE) predicts human judgements of a translation hypothesis without seeing the reference. State-of-the-art QE systems based on pretrained language models have been achieving remarkable correlations with human judgements yet they are computationally heavy and require human annotations, which are slow and expensive to create. To address these limitations, we define the problem of metric estimation (ME) where one predicts the automated metric scores also without the reference. We show that even without access to the reference, our model can estimate automated metrics (ρ = 60% for BLEU, ρ = 51% for other metrics) at the sentence-level. Because automated metrics correlate with human judgements, we can leverage the ME task for pre-training a QE model. For the QE task, we find that pre-training on TER is better (ρ = 23%) than training for scratch (ρ = 20%).
CircNet: Meshing 3D Point Clouds with Circumcenter Detection

Reconstructing 3D point clouds into triangle meshes is a key problem in computational geometry and surface reconstruction. Point cloud triangulation solves this problem by providing edge information to the input points. Since no vertex interpolation is involved, it is beneficial to preserve sharp details on the surface. Taking advantage of learning-based techniques in triangulation, existing methods enumerate the complete combinations of candidate triangles, which is both complex and inefficient. In this paper, we leverage the duality between a triangle and its circumcenter, and introduce a deep neural network that detects the circumcenters to achieve point cloud triangulation. Specifically, we introduce multiple anchor priors to divide the neighborhood space of each point. The neural network then learns to predict the presences and locations of circumcenters under the guidance of those anchors. We extract the triangles dual to the detected circumcenters to form a primitive mesh, from which an edge-manifold mesh is produced via simple post-processing. Unlike existing learning-based triangulation methods, the proposed method bypasses an exhaustive enumeration of triangle combinations and local surface parameterization. We validate the efficiency, generalization, and robustness of our method on prominent datasets of both watertight and open surfaces. The code and trained models are provided at https://github.com/EnyaHermite/CircNet.
An Accelerated DC Programming Approach with Exact Line Search for The Symmetric Eigenvalue Complementarity Problem

In this paper, we are interested in developing an accelerated Difference-of-Convex (DC) programming algorithm based on the exact line search for efficiently solving the Symmetric Eigenvalue Complementarity Problem (SEiCP) and Symmetric Quadratic Eigenvalue Complementarity Problem (SQEiCP). We first proved that any SEiCP is equivalent to SEiCP with symmetric positive definite matrices only. Then, we established DC programming formulations for two equivalent formulations of SEiCP (namely, the logarithmic formulation and the quadratic formulation), and proposed the accelerated DC algorithm (BDCA) by combining the classical DCA with inexpensive exact line search by finding real roots of a binomial for acceleration. We demonstrated the equivalence between SQEiCP and SEiCP, and extended BDCA to SQEiCP. Numerical simulations of the proposed BDCA and DCA against KNITRO, FILTERED and MATLAB FMINCON for SEiCP and SQEiCP on both synthetic datasets and Matrix Market NEP Repository are reported. BDCA demonstrated dramatic acceleration to the convergence of DCA to get better numerical solutions, and outperformed KNITRO, FILTERED, and FMINCON solvers in terms of the average CPU time and average solution precision, especially for large-scale cases.
Limitations of Piecewise Linearity for Efficient Robustness Certification

Certiﬁed defenses against small-norm adversarial examples have received growing attention in recent years; though certiﬁed accuracies of state-of-the-art methods remain far below their non-robust counterparts, despite the fact that benchmark datasets have been shown to be well-separated at far larger radii than the literature generally attempts to certify. In this work, we offer insights that identify potential factors in this performance gap. Speciﬁcally, our analysis reveals that piecewise linearity imposes fundamental limitations on the tightness of leading certiﬁcation techniques. These limitations are felt in practical terms as a greater need for capacity in models hoped to be certiﬁed efﬁciently. Moreover, this is in addition to the capacity necessary to learn a robust boundary, studied in prior work. However, we argue that addressing the limitations of piecewise linearity through scaling up model capacity may give rise to potential difﬁculties—particularly regarding robust generalization— therefore, we conclude by suggesting that developing smooth activation functions may be the way forward for advancing the performance of certiﬁed neural networks.
Explaining the effects of non-convergent sampling in the training of Energy-Based Models

In this paper, we quantify the impact of using non-convergent Markov chains to train Energy-Based models (EBMs). In particular, we show analytically that EBMs trained with non-persistent short runs to estimate the gradient can perfectly reproduce a set of empirical statistics of the data, not at the level of the equilibrium measure, but through a precise dynamical process. Our results provide a first-principles explanation for the observations of recent works proposing the strategy of using short runs starting from random initial conditions as an efficient way to generate high-quality samples in EBMs, and lay the groundwork for using EBMs as diffusion models. After explaining this effect in generic EBMs, we analyze two solvable models in which the effect of the non-convergent sampling in the trained parameters can be described in detail. Finally, we test these predictions numerically on a ConvNet EBM and a Boltzmann machine.
Signatures of a magnetic superstructure phase induced by ultrahigh magnetic fields in a breathing pyrochlore antiferromagnet

Significance The search for exotic magnetic states, such as a quantum spin liquid and a magnetic superstructure, in geometrically frustrated magnets has been a central research topic in the recent 30 y. Theoretically, the mutual coupling of spin and lattice degrees of freedom has been proposed to induce rich magnetic phases in various spin models. Here, we observe an unconventional multistep magnetostructural transition in a breathing pyrochlore antiferromagnet in ultrahigh magnetic fields, signaling the emergence of a magnetic superstructure phase with a periodic array of collinear 3-up-1-down and canted antiferromagnetic states. This finding could be attributed to the interplay between the spin–lattice coupling and breathing anisotropy.
Entanglement gap in 1D long-range quantum spherical models

We investigate the finite-size scaling of the entanglement gap in the one-dimensional long-range quantum spherical model (QSM). We focus on the weak long-range QSM, for which the thermodynamic limit is well-defined. This model exhibits a continuous phase transition, separating a paramagnetic from a ferromagnet phase. The universality class of the transition depends on the long-range exponent α. We show that in the thermodynamic limit the entanglement gap is finite in the paramagnetic phase, and it vanishes in the ferromagnetic phase. In the ferromagnetic phase the entanglement gap is understood in terms of standard magnetic correlation functions. The half-system entanglement gap decays as δξ≃CαL−(1/2−α/4) , where the constant C α depends on the low-energy properties of the model and L is the system size. This reflects that the lower part of the dispersion is affected by the long range physics. Finally, multiplicative logarithmic corrections are absent in the scaling of the entanglement gap, in contrast with the higher-dimensional case.
Estimation of Source and Receiver Positions, Room Geometry and Reflection Coefficients From a Single Room Impulse Response

We propose an algorithm to estimate source and receiver positions, room geometry and reflection coefficients from a single room impulse response simultaneously. It is based on a symmetry analysis of the room impulse response. The proposed method utilizes the times of arrivals of the direct path, first order reflections and second order reflections. The proposed method is robust to erroneous pulses and non-specular reflections. It can be applied to any room with parallel walls as long as the required arrival times of reflections are available. In contrast to the state-of-art method, we do not restrict the location of source and receiver.
Dance2MIDI: Dance-driven multi-instruments music generation

Dance-driven music generation aims to generate musical pieces conditioned on dance videos. Previous works focus on monophonic or raw audio generation, while the multi-instruments scenario is under-explored. The challenges associated with the dance-driven multi-instrument music (MIDI) generation are twofold: 1) no publicly available multi-instruments MIDI and video paired dataset and 2) the weak correlation between music and video. To tackle these challenges, we build the first multi-instruments MIDI and dance paired dataset (D2MIDI). Based on our proposed dataset, we introduce a multi-instruments MIDI generation framework (Dance2MIDI) conditioned on dance video. Specifically, 1) to capture the relationship between dance and music, we employ the Graph Convolutional Network to encode the dance motion. This allows us to extract features related to dance movement and dance style, 2) to generate a harmonious rhythm, we utilize a Transformer model to decode the drum track sequence, leveraging a cross-attention mechanism, and 3) we model the task of generating the remaining tracks based on the drum track as a sequence understanding and completion task. A BERT-like model is employed to comprehend the context of the entire music piece through self-supervised learning. We evaluate the generated music of our framework trained on the D2MIDI dataset and demonstrate that our method achieves State-of-the-Art performance.
Dynamic Graphs Generators Analysis : an Illustrative Case Study

In this work, we investigate the analysis of generators for dynamic graphs, which are defined as graphs whose topology changes over time. We introduce a novel concept, called ''sustainability,'' to qualify the long-term evolution of dynamic graphs. A dynamic graph is considered sustainable if its evolution does not result in a static, empty, or periodic graph. To measure the dynamics of the sets of vertices and edges, we propose a metric, named ''Nervousness,'' which is derived from the Jaccard distance.As an illustration of how the analysis can be conducted, we design a parametrized generator, named D3G3 (Degree-Driven Dynamic Geometric Graphs Generator), which generates dynamic graph instances from an initial geometric graph. The evolution of these instances is driven by two rules that operate on the vertices based on their degree. By varying the parameters of the generator, different properties of the dynamic graphs can be produced.Our results show that in order to ascertain the sustainability of the generated dynamic graphs, it is necessary to study both the evolution of the order and the Nervousness for a given set of parameters.
ntLink: A Toolkit for De Novo Genome Assembly Scaffolding and Mapping Using Long Reads

With the increasing affordability and accessibility of genome sequencing data, de novo genome assembly is an important first step to a wide variety of downstream studies and analyses. Therefore, bioinformatics tools that enable the generation of high‐quality genome assemblies in a computationally efficient manner are essential. Recent developments in long‐read sequencing technologies have greatly benefited genome assembly work, including scaffolding, by providing long‐range evidence that can aid in resolving the challenging repetitive regions of complex genomes. ntLink is a flexible and resource‐efficient genome scaffolding tool that utilizes long‐read sequencing data to improve upon draft genome assemblies built from any sequencing technologies, including the same long reads. Instead of using read alignments to identify candidate joins, ntLink utilizes minimizer‐based mappings to infer how input sequences should be ordered and oriented into scaffolds. Recent improvements to ntLink have added important features such as overlap detection, gap‐filling, and in‐code scaffolding iterations. Here, we present three basic protocols demonstrating how to use each of these new features to yield highly contiguous genome assemblies, while still maintaining ntLink's proven computational efficiency. Further, as we illustrate in the alternate protocols, the lightweight minimizer‐based mappings that enable ntLink scaffolding can also be utilized for other downstream applications, such as misassembly detection. With its modularity and multiple modes of execution, ntLink has broad benefit to the genomics community, from genome scaffolding and beyond. ntLink is an open‐source project and is freely available from https://github.com/bcgsc/ntLink. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
Immersive Demonstrations are the Key to Imitation Learning

Achieving successful robotic manipulation is an essential step towards robots being widely used in industry and home settings. Recently, many learning-based methods have been proposed to tackle this challenge, with imitation learning showing great promise. However, imperfect demonstrations and a lack of feedback from teleoperation systems may lead to poor or even unsafe results. In this work we explore the effect of demonstrator force feedback on imitation learning, using a feedback glove and a robot arm to render fingertip-level and palm-level forces, respectively. 10 participants recorded 5 demonstrations of a pick-and-place task with 3 grippers, under conditions with no force feedback, fingertip force feedback, and fingertip and palm force feedback. Results show that force feedback significantly reduces demonstrator fingertip and palm forces, leads to a lower variation in demonstrator forces, and recorded trajectories that are quicker to execute. Using behavioral cloning, we find that agents trained to imitate these trajectories mirror these benefits, even though agents have no force data shown to them during training. We conclude that immersive demonstrations, achieved with force feedback, may be the key to unlocking safer, quicker-to-execute dexterous manipulation policies.
Spherically symmetric, self-gravitating dust is always ‘self-similar’

La poussiere self-gravitante a symetrie spherique est discutee du point de vue des perspectives newtoniennes et einsteiniennes. Il est suggere que toutes les solutions de Tolman sont en fait self-similaires dans le sens de Lie pour lequel une integrale exite le long d'une certaine courbe dans l'espace-temps. Le probleme de l'effondrement spherique classique avec la pression est egalement traite
Face Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networks

— Generative Networks have proved to be extremely effective in image restoration and reconstruction in the past few years. Generating faces from textual descriptions is one such application where the power of generative algorithms can be used. The task of generating faces can be useful for a number of applications such as finding missing persons, identifying criminals, etc. This paper discusses a novel approach to generating human faces given a textual description regarding the facial features. We use the power of state of the art natural language processing models to convert face descriptions into learnable latent vectors which are then fed to a generative adversarial network which generates faces corresponding to those features. While this paper focuses on high level descriptions of faces only, the same approach can be tailored to generate any image based on fine grained textual features.
Wasserstein distance as a new tool for discriminating cosmologies through the topology of large scale structure

In this work we test Wasserstein distance in conjunction with persistent homology, as a tool for discriminating large scale structures of simulated universes with different values of σ8 cosmological parameter (present root-mean-square matter fluctuation averaged over a sphere of radius 8 Mpc comoving). The Wasserstein distance (a.k.a. the pair-matching distance) was proposed to measure the difference between two networks in terms of persistent homology. The advantage of this approach consists in its non-parametric way of probing the topology of the Cosmic web, in contrast to graph-theoretical approach depending on linking length. By treating the halos of the Cosmic Web as points in a point cloud we calculate persistent homologies, build persistence (birth-death) diagrams and evaluate Wasserstein distance between them. The latter showed itself as a convenient tool to compare simulated Cosmic webs. We show that one can discern two Cosmic webs (simulated or real) with different σ8 parameter. It turns out that Wasserstein distance’s discrimination ability depends on redshift z, as well as on the dimensionality of considered homology features. We find that the highest discriminating power this tool obtains at z = 2 snapshots, among the considered z = 2, 1, and 0.1 ones.
Active Learning of Piecewise Gaussian Process Surrogates

Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning. In this paper, we develop a method for active learning of piecewise, Jump GP surrogates. Jump GPs are continuous within, but discontinuous across, regions of a design space, as required for applications spanning autonomous materials design, conﬁguration of smart factory systems, and many others. Although our active learning heuristics are appropriated from strategies originally designed for ordinary GPs, we demonstrate that additionally accounting for model bias, as opposed to the usual model uncertainty, is essential in the Jump GP context. Toward that end, we develop an estimator for bias and variance of Jump GP models. Illustrations, and evidence of the advantage of our proposed methods, are provided on a suite of synthetic benchmarks, and real-simulation experiments of varying complexity.
Contractions and extractions on twisted bialgebras and coloured Fock functors

We introduce a notion of extraction-contraction coproduct on twisted bialgebras, that is to say bialgebras in the category of linear species. If $P$ is a twisted bialgebra, a contraction-extraction coproduct sends $P[X]$ to $P[X/\sim]\otimes P[X]$ for any finite set $X$ and any equivalence relation $\sim$ on $X$, with a coassociativity constraint and compatibilities with the product and coproduct of $P$. We prove that if $P$ is a twisted bialgebra with an extraction-contraction coproduct, then $P\circ Com$ is a bialgebra in the category of coalgebraic species, that is to say species in the category of coalgebras.We then introduce a coloured version of the bosonic Fock functor. This induces a bifunctor which associates to any bialgebra $(V,\cdot,\delta_V)$ and to any twisted bialgebra $P$ with an extraction-contraction coproduct a comodule-bialgebra $F_V[P]$: this object inherits a product $m$ and two coproducts $\Delta$ and $\delta$, such that $(F_V[P],m,\Delta)$ is a bialgebra in the category of right $(F_V[P],m,\delta)$-comodules.As an example, this is applied to the twisted bialgebra of graphs. The coloured Fock functors then allow to extend the construction of the double bialgebra of graphs to double bialgebras of graphs which vertices are decorated by elements of any bialgebra $V$. Other examples (on mixed graphs, hypergraphs, noncrossing partitions.) will be given in a series of forthcoming papers.
Online Kernel Sliced Inverse Regression

Graphical abstract Abstract Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other methods have been studied in depth, but as far as we know, online supervised nonlinear dimension reduction methods have not been fully studied. In this article, an online kernel sliced inverse regression method is proposed. By introducing the approximate linear dependence condition and dictionary variable sets, we address the problem of increasing variable dimensions with the sample size in the online kernel sliced inverse regression method, and propose a reduced-order method for updating variables online. We then transform the problem into an online generalized eigen-decomposition problem, and use the stochastic optimization method to update the centered dimension reduction directions. Simulations and the real data analysis show that our method can achieve close performance to batch processing kernel sliced inverse regression.
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks

Cellular networks are becoming increasingly heterogeneous with higher base station (BS) densities and ever more frequency bands, making BS selection and band assignment key decisions in terms of rate and coverage. In this paper, we decompose the mobility-aware user association task into (i) forecasting of user rate and then (ii) convex utility maximization for user association accounting for the effects of BS load and handover overheads. Using a linear combination of normalized mean-squared error and normalized discounted cumulative gain as a novel loss function, a recurrent deep neural network is trained to reliably forecast the mobile users' future rates. Based on the forecast, the controller optimizes the association decisions to maximize the service rate-based network utility using our computationally efficient (speed up of 100x versus generic convex solver) algorithm based on the Frank-Wolfe method. Using an industry-grade network simulator developed by Meta, we show that the proposed model predictive control (MPC) approach improves the 5th percentile service rate by 3.5x compared to the traditional signal strength-based association, reduces the median number of handovers by 7x compared to a handover agnostic strategy, and achieves service rates close to a genie-aided scheme. Furthermore, our model-based approach is significantly more sample-efficient (needs 100x less training data) compared to model-free reinforcement learning (RL), and generalizes well across different user drop scenarios.
Fourier coefficients of bounded functions

The results of this paper are divided into two parts. First: inequalities for the Fourier coefficients of any bounded function ; second : an approximation theorem for the Fourier development of an arbitrary bounded function. Inequalities for Fourier coefficients have been discussed in a paper by Professor Szâsz. However, his work deals mainly with linear inequalities for complex coefficients. The inequalities to be investigated in this paper are not linear. Nevertheless, they are the best possible, for this reason : given any set of numbers which makes the inequality an equality, there exists a bounded function which has these numbers as its Fourier coefficients. A simple illustration will clarify this. Let f(x) be a bounded measurable function in ( — x, w) such that \f(x)\ g l . The Fourier coefficients of ƒ(x) are given by the formulae