Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Discrete Distributions by Dequantization

Media is generally stored digitally and is therefore discrete. Many successful deep distribution models in deep learning learn a density, i.e., the distribution of a continuous random variable. Naive optimization on discrete data leads to arbitrarily high likelihoods, and instead, it has become standard practice to add noise to datapoints. In this paper, we present a general framework for dequantization that captures existing methods as a special case. We derive two new dequantization objectives: importance-weighted (iw) dequantization and Renyi dequantization. In addition, we introduce autoregressive dequantization (ARD) for more flexible dequantization distributions. Empirically we find that iw and Renyi dequantization considerably improve performance for uniform dequantization distributions. ARD achieves a negative log-likelihood of 3.06 bits per dimension on CIFAR10, which to the best of our knowledge is state-of-the-art among distribution models that do not require autoregressive inverses for sampling.
Analysing Affective Behavior in the First ABAW 2020 Competition

The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valencearousal estimation, basic expression recognition and action unit detection. It is split into three Challenges, each one addressing a respective behavior task. For the Challenges, we provide a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one annotated for all these three tasks. In this paper, we describe this Competition, to be held in conjunction with the IEEE Conference on Face and Gesture Recognition, May 2020, in Buenos Aires, Argentina. We present the three Challenges, with the utilized Competition corpora. We outline the evaluation metrics, present both the baseline system and the top-3 performing teams’ methodologies per Challenge and finally present their obtained results. More information regarding the Competition, the leaderboard of each Challenge and details for accessing the utilized database, are provided in the Competition site: http://ibug.doc. ic.ac. uk/ resources/fg-2020-competition-affective-behavior-analysis.
On the Renyi entropy power and the Gagliardo-Nirenberg-Sobolev inequality on Riemannian manifolds

In this paper, we prove the concavity of the Renyi entropy power for nonlinear diffusion equation (NLDE) associated with the Laplacian and the Witten Laplacian on compact Riemannian manifolds with non-negative Ricci curvature or $CD(0,m)$-condition and on compact manifolds equipped with time dependent metrics and potentials. Our results can be regarded as natural extensions of a result due to Savar\'e and Toscani \cite{ST} on the concavity of the Renyi entropy for NLDE on Euclidean spaces. Moreover, we prove that the rigidity models for the Renyi entropy power are the Einstein or quasi-Einstein manifolds and a special $(K,m)$-Ricci flow with Hessian solitons. Inspired by Lu-Ni-Vazquez-Villani \cite{LNVV}, we prove the Aronson-Benilan estimates for NLDE on compact Riemannian manifolds with $CD(0,m)$-condition. We also prove the NIW formula which indicates an intrinsic relationship between the second order derivative of the Renyi entropy power $N_p$, the $p$-th Fisher information $I_p$ and the time derivative of the $W$-entropy associated with NLDE. Finally, we prove the entropy isoperimetric inequality for the Renyi entropy power and the Gagliardo-Nirenberg-Sobolev inequality on complete Riemannian manifolds with non-negative Ricci curvature or $CD(0, m)$-condition and maximal volume growth condition.
Lost in Embedding Space: Explaining Cross-Lingual Task Performance with Eigenvalue Divergence

Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces. In this work, we present a large-scale study focused on the correlations between language similarity and task performance, covering thousands of language pairs and four different tasks: BLI, machine translation, parsing, and POS tagging. We propose a novel language distance measure, Eigenvalue Divergence (EVD), which quantifies the degree of isomorphism between two monolingual spaces. We empirically show that 1) language similarity scores derived from embedding-based EVD distances are strongly associated with performance observed in different cross-lingual tasks, 2) EVD outperforms other standard embedding-based language distance measures across the board, at the same time being computationally more tractable and easier to interpret. Finally, we demonstrate that EVD captures information which is complementary to typologically driven language distance measures. We report that their combination yields even higher correlations with performance levels in all cross-lingual tasks.
D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge

Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning (ML) models by exploiting their local data samples and communication/computation resources. To deal with the “straggler’s dilemma” issue faced in this technique, this letter proposes a new device-to-device (D2D)-enabled data sharing approach, in which different edge devices share their data samples among each other over D2D communication links, in order to properly adjust their computation loads for increasing the training speed. Under this setup, we optimize the radio resource allocation for both D2D-enabled data sharing and distributed training, with the objective of minimizing the total training delay under fixed numbers of local and global iterations (for training). Numerical results show that the proposed D2D-enabled data sharing design significantly reduces the training delay, and also enhances the training accuracy when the data samples are non-independent and identically distributed (non-IID) among edge devices.
A Study of Pyramid Structure for Code Correction

We demonstrate the implementations of pyramid encoders in both multi-layer GRU and Transformer for seq2seq tasks. We apply the models to the code correction task on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Dataset(SARD), successfully repaired 90.1\% of faulted code in the test dataset, and show that a pyramid structure can greatly improve memory efficiency and therefore computation efficiency. We successfully carried out error type classification task on ITC benchmark examples (with only 685 code instances) using transfer learning with models pre-trained on Juliet Test Suite, pointing out a novel way of processing small datasets.
Learning to Structure Long-term Dependence for Sequential Recommendation

Sequential recommendation recommends items based on sequences of users' historical actions. The key challenge in it is how to effectively model the influence from distant actions to the action to be predicted, i.e., recognizing the long-term dependence structure; and it remains an underexplored problem. To better model the long-term dependence structure, we propose a GatedLongRec solution in this work. To account for the long-term dependence, GatedLongRec extracts distant actions of top-$k$ related categories to the user's ongoing intent with a top-$k$ gating network, and utilizes a long-term encoder to encode the transition patterns among these identified actions. As user intent is not directly observable, we take advantage of available side-information about the actions, i.e., the category of their associated items, to infer the intents. End-to-end training is performed to estimate the intent representation and predict the next action for sequential recommendation. Extensive experiments on two large datasets show that the proposed solution can recognize the structure of long-term dependence, thus greatly improving the sequential recommendation.
Unsupervised Sentiment Analysis for Code-mixed Data

Code-mixing is the practice of alternating between two or more languages. Mostly observed in multilingual societies, its occurrence is increasing and therefore its importance. A major part of sentiment analysis research has been monolingual, and most of them perform poorly on code-mixed text. In this work, we introduce methods that use different kinds of multilingual and cross-lingual embeddings to efficiently transfer knowledge from monolingual text to code-mixed text for sentiment analysis of code-mixed text. Our methods can handle code-mixed text through a zero-shot learning. Our methods beat state-of-the-art on English-Spanish code-mixed sentiment analysis by absolute 3\% F1-score. We are able to achieve 0.58 F1-score (without parallel corpus) and 0.62 F1-score (with parallel corpus) on the same benchmark in a zero-shot way as compared to 0.68 F1-score in supervised settings. Our code is publicly available.
Agricultural and societal perspectives on pasture-based livestock production systems in Germany

Milk production methods and pasture usage have gained increasing attention in recent years. This paper studies possible influences on the decision to adopt grazing practices as well as on the extent of these practices. German dairy farms were analyzed using a multivariate sample-selection model. Results indicate that specialized farms and farms with greater pasture acreage per cow are more likely to adopt grazing practices; farms with larger herds are less likely to adopt. For farmers utilizing grazing, length of daily pasture access depends on production-related variables, while the annual period depends only on farm specialization.
Safe Predictors for Enforcing Input-Output Specifications

We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training. Our method involves designing a constrained predictor for each set of compatible constraints, and combining them safely via a convex combination of their predictions. We demonstrate our approach on synthetic datasets and an aircraft collision avoidance problem.
Rating the performance of noisy teleportation using fluctuations in fidelity

Quantum teleportation is one of the most pioneering features of the quantum world. Typically, the quality of a teleportation protocol is solely judged by its average fidelity. In this work, we analyze the performance of teleportation in terms of both fidelity and the deviation in fidelity. Specifically, we define a quantity called teleportability score, which incorporates contributions from both the fidelity and its deviation. It also takes into account the sensitivity one requires for a protocol in which the teleportation of a quantum state is required in one or many intermediate steps. We compute the teleportability score in the noiseless scenario and find that it increases monotonically with the entanglement content of the considered pure resource states. The result remains the same even if we consider an $n$-chain repeater-like configuration. However, in the presence of noise, the teleportability score can sometime display a nonmonotonic behavior with respect to the entanglement content of the initially shared resource state. Specifically, under local bit-flip and bit-phase-flip noise, less entangled states can have higher teleportability score for certain choices of system parameters. In the presence of global depolarizing noise, for low entangled resource states and high sensitivity requirements, the noisy states can have better a teleportability score in comparison to the noiseless scenario.
Automatic marker-free registration of tree point-cloud data based on rotating projection

Point-cloud data acquired using a terrestrial laser scanner (TLS) play an important role in digital forestry research. Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information. However, it is time-consuming and difficult to place artificial reflectors in a forest with complex terrain for marker-based registration, a process that reduces registration automation and efficiency. In this study, we propose an automatic coarse-to-fine method for the registration of point-cloud data from multiple scans of a single tree. In coarse registration, point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional (2D) images, which are used to estimate the initial positions of multiple scans. Corresponding feature-point pairs are then extracted from these series of 2D images. In fine registration, point-cloud data slicing and fitting methods are used to extract corresponding central stem and branch centers for use as tie points to calculate fine transformation parameters. To evaluate the accuracy of registration results, we propose a model of error evaluation via calculating the distances between center points from corresponding branches in adjacent scans. For accurate evaluation, we conducted experiments on two simulated trees and a real-world tree. Average registration errors of the proposed method were 0.26m around on simulated tree point clouds, and 0.05m around on real-world tree point cloud.
A Deeper Look into Hybrid Images

$Hybrid$ $images$ was first introduced by Olivia et al., that produced static images with two interpretations such that the images changes as a function of viewing distance. Hybrid images are built by studying human processing of multiscale images and are motivated by masking studies in visual perception. The first introduction of hybrid images showed that two images can be blend together with a high pass filter and a low pass filter in such a way that when the blended image is viewed from a distance, the high pass filter fades away and the low pass filter becomes prominent. Our main aim here is to study and review the original paper by changing and tweaking certain parameters to see how they affect the quality of the blended image produced. We have used exhaustively different set of images and filters to see how they function and whether this can be used in a real time system or not.
Finite-time Analysis of Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards

We study an extension of the classic stochastic multi-armed bandit problem which involves Markovian rewards and multiple plays. In order to tackle this problem we consider an index based adaptive allocation rule which at each stage combines calculations of sample means, and of upper confidence bounds, using the Kullback-Leibler divergence rate, for the stationary expected reward of Markovian arms. For rewards generated from a one-parameter exponential family of Markov chains, we provide a finite-time upper bound for the regret incurred from this adaptive allocation rule, which reveals the logarithmic dependence of the regret on the time horizon, and which is asymptotically optimal. For our analysis we devise several concentration results for Markov chains, including a maximal inequality for Markov chains, that may be of interest in their own right. As a byproduct of our analysis we also establish, asymptotically optimal, finite-time guarantees for the case of multiple plays, and IID rewards drawn from a one-parameter exponential family of probability densities.
A convergent entropy diminishing finite volume scheme for a cross-diffusion system

We study a two-point flux approximation finite volume scheme for a cross-diffusion system. The scheme is shown to preserve the key properties of the continuous systems, among which the decay of the entropy. The convergence of the scheme is established thanks to compactness properties based on the discrete entropy - entropy dissipation estimate. Numerical results illustrate the behavior of our scheme.
BUT Opensat 2019 Speech Recognition System

The paper describes the BUT Automatic Speech Recognition (ASR) systems submitted for OpenSAT evaluations under two domain categories such as low resourced languages and public safety communications. The first was challenging due to lack of training data, therefore various architectures and multilingual approaches were employed. The combination led to superior performance. The second domain was challenging due to recording in extreme conditions such as specific channel, speaker under stress and high levels of noise. Data augmentation process was inevitable to get reasonably good performance.
Functional calculus and multi-analytic models on regular $\Lambda$-polyballs.

The goal of the present paper is to introduce and study noncommutative Hardy spaces associated with the regular $\Lambda$-polyball, to develop a functional calculus on noncommutative Hardy spaces for the completely non-coisometric (c.n.c.) $k$-tuples in ${\bf B}_\Lambda(H)$, and to study the characteristic functions and the associated multi-analytic models for the c.n. c. elements in the regular $\Lambda$-polyball. In addition, we show that the characteristic function is a complete unitary invariant for the class of c.n. c. $k$-tuples in ${\bf B}_\Lambda(H)$. These results extend the corresponding classical results of Sz. -Nagy--Foia\c s for contractions and the noncommutative versions for row contractions. In the particular case when $n_1=\cdots=n_k=1$ and $\Lambda_{ij}=1$, we obtain a functional calculus and operator model theory in terms of characteristic functions for $k$-tuples of contractions satisfying Brehmer condition.
Hybridization of interval methods and evolutionary algorithms for solving difficult optimization problems

Reliable global optimization is dedicated to finding a global minimum in the presence of rounding errors. The only approaches for achieving a numerical proof of global optimality are interval branch and bound methods that interleave branching of the search-space and pruning of the subdomains that cannot contain an optimal solution. It is of the utmost importance: i) to compute sharp enclosures of the objective function and the constraints on a given subdomain; ii) to find a good approximation (an upper bound) of the global minimum. State-of-the-art solvers are generally integrative methods, that is they embed local optimization algorithms to compute a good upper bound of the global minimum over each subspace. In this document, we propose a cooperative framework in which interval methods cooperate with evolutionary algorithms. The latter are stochastic algorithms in which a population of candidate solutions iteratively evolves in the search-space to reach satisfactory solutions. Evolutionary algorithms, endowed with operators that help individuals escape from local minima, are particularly suited for difficult problems on which traditional methods struggle to converge. Within our cooperative solver Charibde, the evolutionary algorithm and the interval-based algorithm run in parallel and exchange bounds, solutions and search-space via message passing. A novel strategy prevents premature convergence toward local minima. A comparison of Charibde with state-of-the-art solvers (GlobSol, IBBA, Ibex) on a benchmark of difficult problems shows that Charibde converges faster by an order of magnitude. New optimality results are provided for five multimodal problems, for which few solutions were available in the literature. Finally, we provide the first numerical proof of optimality for the open Lennard-Jones cluster problem with five atoms.
The Vera Rubin Observatory Legacy Survey of Space and Time and the Low Surface Brightness Universe

The 8.4m Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will start a ten-year survey of the southern hemisphere sky in 2023. LSST will revolutionise low surface brightness astronomy. It will transform our understanding of galaxy evolution, through the study of low surface brightness features around galaxies (faint shells, tidal tails, halos and stellar streams), discovery of low surface brightness galaxies and the first set of statistical measurements of the intracluster light over a significant range of cluster masses and redshifts.
First cosmology results using type Ia supernovae from the Dark Energy Survey: the effect of host galaxy properties on supernova luminosity

We present improved photometric measurements for the host galaxies of 206 spectroscopically confirmed type Ia supernovae discovered by the Dark Energy Survey Supernova Program (DES-SN) and used in the first DES-SN cosmological analysis. For the DES-SN sample, when considering a 5D (z, x1, c, α, β) bias correction, we find evidence of a Hubble residual ‘mass step’, where SNe Ia in high-mass galaxies (>1010M⊙) are intrinsically more luminous (after correction) than their low-mass counterparts by $\gamma =0.040\pm 0.019$ mag. This value is larger by 0.031 mag than the value found in the first DES-SN cosmological analysis. This difference is due to a combination of updated photometric measurements and improved star formation histories and is not from host-galaxy misidentification. When using a 1D (redshift-only) bias correction the inferred mass step is larger, with $\gamma =0.066\pm 0.020$ mag. The 1D−5D γ difference for DES-SN is $0.026\pm 0.009$ mag. We show that this difference is due to a strong correlation between host galaxy stellar mass and the x1 component of the 5D distance-bias correction. Including an intrinsic correlation between the observed properties of SNe Ia, stretch and colour, and stellar mass in simulated SN Ia samples, we show that a 5D fit recovers γ with −9 mmag bias compared to a +2 mmag bias for a 1D fit. This difference can explain part of the discrepancy seen in the data. Improvements in modelling correlations between galaxy properties and SN is necessary to ensure unbiased precision estimates of the dark energy equation of state as we enter the era of LSST.
VERONICA: Expressive and Precise Concurrent Information Flow Security

Methods for proving that concurrent software does not leak its secrets has remained an active topic of research for at least the past four decades. Despite an impressive array of work, the present situation remains highly unsatisfactory. With contemporary compositional proof methods one is forced to choose between expressiveness (the ability to reason about a wide variety of security policies), on the one hand, and precision (the ability to reason about complex thread interactions and program behaviours), on the other. Achieving both is essential and, we argue, requires a new style of compositional reasoning.We present VERONICA, the first program logic for proving concurrent programs information flow secure that supports compositional, high-precision reasoning about a wide range of security policies and program behaviours (e.g. expressive declassification, value-dependent classification, secret-dependent branching). Just as importantly, VERONICA embodies a new approach for engineering such logics that can be re-used elsewhere, called decoupled functional correctness (DFC). DFC leads to a simple and clean logic, even while achieving this unprecedented combination of features. We demonstrate the virtues and versatility of VERONICA by verifying a range of example programs, beyond the reach of prior methods.