Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment

In a warehouse environment, tasks appear dynamically. Consequently, a task management system that matches them with the workforce too early (e.g., weeks in advance) is necessarily sub-optimal. Also, the rapidly increasing size of the action space of such a system consists of a significant problem for traditional schedulers. Reinforcement learning, however, is suited to deal with issues requiring making sequential decisions towards a long-term, often remote, goal. In this work, we set ourselves on a problem that presents itself with a hierarchical structure: the task-scheduling, by a centralised agent, in a dynamic warehouse multi-agent environment and the execution of one such schedule, by decentralised agents with only partial observability thereof. We propose to use deep reinforcement learning to solve both the high-level scheduling problem and the low-level multi-agent problem of schedule execution. Finally, we also conceive the case where centralisation is impossible at test time and workers must learn how to cooperate in executing the tasks in an environment with no schedule and only partial observability.
Language vs Speaker Change: A Comparative Study

Spoken language change detection (LCD) refers to detecting language switching points in a multilingual speech signal. Speaker change detection (SCD) refers to locating the speaker change points in a multispeaker speech signal. The objective of this work is to understand the challenges in LCD task by comparing it with SCD task. Human subjective study for change detection is performed for LCD and SCD. This study demonstrates that LCD requires larger duration spectro-temporal information around the change point compared to SCD. Based on this, the work explores automatic distance based and model based LCD approaches. The model based ones include Gaussian mixture model and universal background model (GMM-UBM), attention, and Generative adversarial network (GAN) based approaches. Both the human and automatic LCD tasks infer that the performance of the LCD task improves by incorporating more and more spectro-temporal duration.
A Better Computational Framework for L 2 E Regression

Building on previous research of Chi and Chi (2022), the current paper revisits estimation in robust structured regression under the L 2 E criterion. We adopt the majorization-minimization (MM) principle to design a new algorithm for updating the vector of regression coeﬃcients. Our sharp majorization achieves faster convergence than the previous alternating projected gradient descent algorithm (Chi and Chi, 2022). In addition, we reparameterize the model by substituting precision for scale and estimate precision via a modiﬁed Newton’s method. This simpliﬁes and accelerates overall estimation. Finally, we introduce distance to set penalties to allow constrained estimation under nonconvex constraint sets. This tactic also improves performance in coeﬃcient estimation and structure recovery. We demonstrate the merits of the reﬁned framework through a rich set of simulation examples.
Quantum computing for software engineering: prospects

Quantum computers (QCs) are maturing. When QCs are powerful enough, they may be able to handle problems in chemistry, physics, and finance that are not classically solvable. However, the applicability of quantum algorithms to speed up Software Engineering (SE) tasks has not been explored. We examine eight groups of quantum algorithms that may accelerate SE tasks across the different phases of SE and sketch potential opportunities and challenges.
Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation

Label smoothing and vocabulary sharing are two widely used techniques in neural machine translation models. However, we argue that simply applying both techniques can be conflicting and even leads to sub-optimal performance. When allocating smoothed probability, original label smoothing treats the source-side words that would never appear in the target language equally to the real target-side words, which could bias the translation model. To address this issue, we propose Masked Label Smoothing (MLS), a new mechanism that masks the soft label probability of source-side words to zero. Simple yet effective, MLS manages to better integrate label smoothing with vocabulary sharing. Our extensive experiments show that MLS consistently yields improvement over original label smoothing on different datasets, including bilingual and multilingual translation from both translation quality and model’s calibration. Our code is released at https://github.com/PKUnlp-icler/MLS
Searching for Robust Neural Architectures via Comprehensive and Reliable Evaluation

in NAS are not sufﬁciently comprehensive and reliable. In particular, the common practice only considers adversarial noise and quantiﬁed metrics such as the Jacobian matrix, whereas, some studies indicated that the models are also vulnerable to other types of noises such as natural noise. In addition, existing methods taking adversarial noise as the evaluation just use the robust accuracy of the FGSM or PGD, but these adversarial attacks could not provide the adequately reliable evaluation, leading to the vulnerability of the models under stronger attacks. To alleviate the above problems, we propose a novel framework, called Auto Adversarial Attack and Defense (AAAD), where we employ neural architecture search methods, and four types of robustness evaluations are considered, including adversarial noise, natural noise, system noise and quantiﬁed metrics, thereby assisting in ﬁnding more robust architectures. Also, among the adversarial noise, we use the composite adversarial attack obtained by random search as the new metric to evaluate the robustness of the model architectures. The empirical results on the CIFAR10 dataset show that the searched efﬁcient attack could help ﬁnd more robust architectures.
Statistical Properties of Radio Halos in Galaxy Clusters and the Origin of Seed Electrons for Reacceleration

One of the most promising mechanisms for producing radio halos (RHs) in galaxy clusters is the reacceleration of cosmic-ray electrons by turbulence. However, the origin of the seed electrons for reacceleration is still poorly constrained. In the secondary scenario, most of the seed electrons are injected via collision of proton cosmic-rays, while nonthermal electrons are directly injected in the primary scenario. In this paper, we examine the two scenarios for seed electrons with the observed statistical properties of RHs by combining two methods: by following the temporal evolutions of the electron energy and the radial distributions in a cluster, as well as the merger history of clusters. We find that the RH lifetime largely depends on the seed origin, as it could be longer than the cosmological timescale in the secondary scenario. We study the condition for the onset of RHs with the observed RH fraction and the RH lifetime we obtained and find that long-lived RHs in the secondary scenario should originate from major mergers with a mass ratio of ξ ∼ 0.1, while the short lifetime in the primary scenario requires more frequent onsets from minor mergers with ξ ∼ 0.01. Our simple model of the turbulence acceleration can reproduce the observed radio luminosity–mass relation. The RH luminosity functions we obtained suggest that the expected RH number count with the ASKAP survey will detect ≈103 RHs in both scenarios.
Meta Mirror Descent: Optimiser Learning for Fast Convergence

Optimisers are an essential component for training machine learning models, and their design influences learning speed and generalisation. Several studies have attempted to learn more effective gradient-descent optimisers via solving a bi-level optimisation problem where generalisation error is minimised with respect to optimiser parameters. However, most existing optimiser learning methods are intuitively motivated, without clear theoretical support. We take a different perspective starting from mirror descent rather than gradient descent, and meta-learning the corresponding Bregman divergence. Within this paradigm, we formalise a novel meta-learning objective of minimising the regret bound of learning. The resulting framework, termed Meta Mirror Descent (MetaMD), learns to accelerate optimisation speed. Unlike many meta-learned optimisers, it also supports convergence and generalisation guarantees and uniquely does so without requiring validation data. We evaluate our framework on a variety of tasks and architectures in terms of convergence rate and generalisation error and demonstrate strong performance.
Large-S and Tensor-Network Methods for Strongly-Interacting Topological Insulators

The study of correlation effects in topological phases of matter can benefit from a multidisciplinary approach that combines techniques drawn from condensed matter, high-energy physics and quantum information science. In this work, we exploit these connections to study the strongly-interacting limit of certain lattice Hubbard models of topological insulators, which map onto four-Fermi quantum field theories with a Wilson-type discretisation and have been recently shown to be at reach of cold-atom quantum simulators based on synthetic spin-orbit coupling. We combine large-S and tensor-network techniques to explore the possible spontaneous symmetry-breaking phases that appear when the interactions of the topological insulators are sufficiently large. In particular, we show that varying the Wilson parameter r of the lattice discretisations leads to a novel Heisenberg–Ising compass model with critical lines that flow with the value of r.
Improved Pathwise Coordinate Descent for Power Penalties

Pathwise coordinate descent algorithms have been used to compute entire solution paths for lasso and other penalized regression problems quickly with great success. They improve upon cold start algorithms by solving the problems that make up the solution path sequentially for an ordered set of tuning parameter values, instead of solving each problem separately. However, extending pathwise coordinate descent algorithms to more the general bridge or power family of $\ell_q$ penalties is challenging. Faster algorithms for computing solution paths for these penalties are needed because $\ell_q$ penalized regression problems can be nonconvex and especially burdensome to solve. In this paper, we show that a reparameterization of $\ell_q$ penalized regression problems is more amenable to pathwise coordinate descent algorithms. This allows us to improve computation of the mode-thresholding function for $\ell_q$ penalized regression problems in practice and introduce two separate pathwise algorithms. We show that either pathwise algorithm is faster than the corresponding cold-start alternative, and demonstrate that different pathwise algorithms may be more likely to reach better solutions.
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization

Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a standing limitation of these models is that they are trained against limited references and with plain maximum-likelihood objectives. As for many other generative tasks, reinforcement learning (RL) offers the potential to improve the training of MDS models; yet, it requires a carefully-designed reward that can ensure appropriate leverage of both the reference summaries and the input documents. For this reason, in this paper we propose fine-tuning an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents. To implement the approach, we utilize RELAX (Grathwohl et al., 2018), a contemporary gradient estimator which is both low-variance and unbiased, and we fine-tune the baseline in a few-shot style for both stability and computational efficiency. Experimental results over the Multi-News and WCEP MDS datasets show significant improvements of up to +0.95 pp average ROUGE score and +3.17 pp METEOR score over the baseline, and competitive results with the literature. In addition, they show that the coverage of the input documents is increased, and evenly across all documents.
Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning

Most methods for conditional video synthesis use a single modality as the condition. This comes with major limitations. For example, it is problematic for a model conditioned on an image to generate a specific motion trajectory desired by the user since there is no means to provide motion information. Conversely, language information can describe the desired motion, while not precisely defining the content of the video. This work presents a multimodal video generation framework that benefits from text and images provided jointly or separately. We leverage the recent progress in quantized representations for videos and apply a bidirectional transformer with multiple modalities as inputs to predict a discrete video representation. To improve video quality and consistency, we propose a new video token trained with self-learning and an improved mask-prediction algorithm for sampling video tokens. We introduce text augmentation to improve the robustness of the textual representation and diversity of generated videos. Our framework can incorporate various visual modalities, such as segmentation masks, drawings, and partially occluded images. It can generate much longer sequences than the one used for training. In addition, our model can extract visual information as suggested by the text prompt, e.g., “an object in image one is moving northeast”, and generate corresponding videos. We run evaluations on three public datasets and a newly collected dataset labeled with facial attributes, achieving state-of-the-art generation results on all four11Code: https://github.com/snap-research/MMVID and Webpage.
Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry

In the pharmaceutical industry, the maintenance of production machines must be audited by the regulator. In this context, the problem of predictive maintenance is not when to maintain a machine, but what parts to maintain at a given point in time. The focus shifts from the entire machine to its component parts and prediction becomes a classification problem. In this paper, we focus on rolling-elements bearings and we propose a framework for predicting their degradation stages automatically. Our main contribution is a k-means bearing lifetime segmentation method based on high-frequency bearing vibration signal embedded in a latent low-dimensional subspace using an AutoEncoder. Given high-frequency vibration data, our framework generates a labeled dataset that is used to train a supervised model for bearing degradation stage detection. Our experimental results, based on the publicly available FEMTO Bearing run-to-failure dataset, show that our framework is scalable and that it provides reliable and actionable predictions for a range of different bearings.
Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks

Deep neural networks achieve outstanding results in a large variety of tasks, often outperforming human experts. However, a known limitation of current neural architectures is the poor accessibility to understand and interpret the network response to a given input. This is directly related to the huge number of variables and the associated non-linearities of neural models, which are often used as black boxes. When it comes to critical applications as autonomous driving, security and safety, medicine and health, the lack of interpretability of the network behavior tends to induce skepticism and limited trustworthiness, despite the accurate performance of such systems in the given task. Furthermore, a single metric, such as the classification accuracy, provides a non-exhaustive evaluation of most realworld scenarios. In this paper, we want to make a step forward towards interpretability in neural networks, providing new tools to interpret their behavior. We present Agglomerator, a framework capable of providing a representation of part-whole hierarchies from visual cues and organizing the input distribution matching the conceptual-semantic hierarchical structure between classes. We evaluate our method on common datasets, such as SmallNORB, MNIST, FashionMNIST, CIFAR-10, and CIFAR-100, providing a more interpretable model than other state-of-the-art approaches.
Deep Neural Network-Aided Soft-Demapping in Coherent Optical Systems: Regression Versus Classification

We examine here what type of predictive modelling, classification, or regression, using neural networks (NN), fits better the task of soft-demapping based post-processing in coherent optical communications, where the transmission channel is nonlinear and dispersive. For the first time, we present possible drawbacks in using each type of predictive task in a machine learning context, considering the nonlinear coherent optical channel equalization/soft-demapping problem. We study two types of equalizers based on the feed-forward and recurrent NNs, for several transmission scenarios, in linear and nonlinear regimes of the optical channel. We point out that even though from the information theory perspective the cross-entropy loss (classification) is the most suitable option for our problem, the NN models based on the cross-entropy loss function can severely suffer from learning problems. The latter translates into the fact that regression-based learning is typically superior in terms of delivering higher Q-factor and achievable information rates. In short, we show by empirical evidence that loss functions based on cross-entropy may not be necessarily the most suitable option for training communication systems in practical scenarios when overfitting- and vanishing gradients-related problems come into play.
Catalytic activities, molecular connections, and biological functions of plant RNA exosome complexes

Abstract RNA exosome complexes provide the main 3′–5′-exoribonuclease activities in eukaryotic cells and contribute to the maturation and degradation of virtually all types of RNA. RNA exosomes consist of a conserved core complex that associates with exoribonucleases and with multimeric cofactors that recruit the enzyme to its RNA targets. Despite an overall high level of structural and functional conservation, the enzymatic activities and compositions of exosome complexes and their cofactor modules differ among eukaryotes. This review highlights unique features of plant exosome complexes, such as the phosphorolytic activity of the core complex, and discusses the exosome cofactors that operate in plants and are dedicated to the maturation of ribosomal RNA, the elimination of spurious, misprocessed, and superfluous transcripts, or the removal of mRNAs cleaved by the RNA-induced silencing complex and other mRNAs prone to undergo silencing.
Examining the psychometric properties of the Chinese Behavioral Regulation in Exercise Questionnaire-3: A bi-factor approach

The Behavioral Regulation in Exercise Questionnaire (BREQ) was revised to its third iteration (BREQ-3) and has been widely used to measure different types of exercise motivation, including amotivation, external regulation, introjected regulation, identified regulation, integrated regulation, and intrinsic motivation. However, the Chinese version has not been similarly revised. The aim of this study was to develop and examine the psychometric properties of the Chinese BREQ-3 using alternative structural equation models. Specifically, this study aimed to translate the English BREQ-3 into Chinese to examine the best representation of the factor configuration of Chinese BREQ-3, measurement invariance for the best-fitted model, and the concurrent validity evidence and reliability for the Chinese BREQ-3. Undergraduate students (N = 825) from mainland China completed a battery of online questionnaires. After including two general motivation factors (controlled motivation and autonomous motivation), we discovered that the majority of items on the identified regulation, integrated regulation, and intrinsic motivation subscales no longer loaded on or had very low loadings on their specific factors, implying that these items essentially represent a unidimensional construct. Invariance testing supported the comparison between latent factor means across gender based on the bi-factor exploratory structural equation model (BESEM). Concurrent validity evidence was found for amotivation, controlled motivation, and autonomous motivation. The hierarchical omega, explained common variance (ECV), item explained common variance (I_ECV), and percentage of uncontaminated correlations (PUC) indicated that the external regulation and introjected regulation subscales had a multidimensional structure, while the identified regulation, integrated regulation, and intrinsic motivation subscales had a unidimensional structure (autonomous motivation). We advocate calculating amotivation, external regulation, introjected regulation, and a single autonomous motivation (excluding item 19) score when utilizing the Chinese BREQ-3.
MAINE: a web tool for multi-omics feature selection and rule-based data exploration

Abstract Summary Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE—Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. Availability and implementation MAINE is freely available at maine.ibemag. pl as an online web application. Supplementary information Supplementary data are available at Bioinformatics online.