Miracle-dz/UniFAR-FTUs huggingface dataset:
Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions

In this paper, we develop a new type of accelerated algorithms to solve some classes of maximally monotone equations as well as monotone inclusions. Instead of using Nesterov's accelerating approach, our methods rely on a so-called Halpern-type fixed-point iteration in [32], and recently exploited by a number of researchers, including [24, 70]. Firstly, we derive a new variant of the anchored extra-gradient scheme in [70] based on Popov's past extra-gradient method to solve a maximally monotone equation $G(x) = 0$. We show that our method achieves the same $\mathcal{O}(1/k)$ convergence rate (up to a constant factor) as in the anchored extra-gradient algorithm on the operator norm $\Vert G(x_k)\Vert$, , but requires only one evaluation of $G$ at each iteration, where $k$ is the iteration counter. Next, we develop two splitting algorithms to approximate a zero point of the sum of two maximally monotone operators. The first algorithm originates from the anchored extra-gradient method combining with a splitting technique, while the second one is its Popov's variant which can reduce the per-iteration complexity. Both algorithms appear to be new and can be viewed as accelerated variants of the Douglas-Rachford (DR) splitting method. They both achieve $\mathcal{O}(1/k)$ rates on the norm $\Vert G_{\gamma}(x_k)\Vert$ of the forward-backward residual operator $G_{\gamma}(\cdot)$ associated with the problem. We also propose a new accelerated Douglas-Rachford splitting scheme for solving this problem which achieves $\mathcal{O}(1/k)$ convergence rate on $\Vert G_{\gamma}(x_k)\Vert$ under only maximally monotone assumptions. Finally, we specify our first algorithm to solve convex-concave minimax problems and apply our accelerated DR scheme to derive a new variant of the alternating direction method of multipliers (ADMM).
Promoting Coordination Through Electing First-moveAgent in Multi-Agent Reinforcement Learning

Learning to coordinate among multiple agents is an essential problem in multi-agent systems. Multi-agent reinforcement learning has long been a go-to tool in the complicated collaborative environment. However, most existing works are constrained by the assumption that all agents take actions simultaneously. In this paper, we endow the hierarchical order of play for the agents through electing a ﬁrst-move agent and other agents take the best response to the ﬁrst-move agent to obtain better coordination. We propose the algo-rithm EFA-DQN to implicitly model the coordination and learn the coordinated behaviour in multi-agent systems. To verify the feasibility and demonstrate the eﬀectiveness and eﬃciency of our algorithm, we conduct extensive experiments on several multi-agent tasks with diﬀerent numbers of agents: Cooperative Navigation, Physical Deception, and The Google Football. The empirical results across the various scenarios show that our method achieves competitive advantages in terms of better performance and faster convergence, which demonstrates that our algorithm has broad prospects for addressing many complex real-world problems.
Bifiltrations and persistence paths for 2–Morse
functions

This paper studies the homotopy-type of bi-filtrations of compact manifolds induced as the pre-image of filtrations of the plane for generic smooth functions f : M -->R^2. The primary goal of the paper is to allow for a simple description of the multi-graded persistent homology associated to such filtrations. The main result of the paper is a description of the evolution of the bi-filtration of f in terms of cellular attachments. An analogy of Morse-Conley equation and Morse inequalities along so called persistence paths are derived. A scheme for computing path-wise barcodes is proposed.
Transformer-based Multi-task Learning for Disaster Tweet Categorisation

Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can contribute to the situational awareness of emergency responders, who have a need for them to be categorised according to information types (i.e. the type of aid services the messages are requesting). We introduce a transformer-based multi-task learning (MTL) technique for classifying information types and estimating the priority of these messages. We evaluate the effectiveness of our approach with a variety of metrics by submitting runs to the TREC Incident Streams (IS) track: a research initiative specifically designed for disaster tweet classification and prioritisation. The results demonstrate that our approach achieves competitive performance in most metrics as compared to other participating runs. Subsequently, we find that an ensemble approach combining disparate transformer encoders within our approach helps to improve the overall effectiveness to a significant extent, achieving state-of-the-art performance in almost every metric. We make the code publicly available so that our work can be reproduced and used as a baseline for the community for future work in this domain.
Appearance Editing with Free-viewpoint Neural Rendering

We present a neural rendering framework for simultaneous view synthesis and appearance editing of a scene from multi-view images captured under known environment illumination. Existing approaches either achieve view synthesis alone or view synthesis along with relighting, without direct control over the scene's appearance. Our approach explicitly disentangles the appearance and learns a lighting representation that is independent of it. Specifically, we independently estimate the BRDF and use it to learn a lighting-only representation of the scene. Such disentanglement allows our approach to generalize to arbitrary changes in appearance while performing view synthesis. We show results of editing the appearance of a real scene, demonstrating that our approach produces plausible appearance editing. The performance of our view synthesis approach is demonstrated to be at par with state-of-the-art approaches on both real and synthetic data.
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training

With the rise of large-scale pre-trained language models, open-domain question-answering (ODQA) has become an important research topic in NLP. Based on the popular pre-training fine-tuning approach, we posit that an additional in-domain pre-training stage using a large-scale, natural, and diverse question-answering (QA) dataset can be beneficial for ODQA. Consequently, we propose a novel QA dataset based on the Common Crawl project in this paper. Using the readily available schema.org annotation, we extract around 130 million multilingual question-answer pairs, including about 60 million English data-points. With this previously unseen number of natural QA pairs, we pre-train popular language models to show the potential of large-scale in-domain pre-training for the task of question-answering. In our experiments, we find that pre-training question-answering models on our Common Crawl Question Answering dataset (CCQA) achieves promising results in zero-shot, low resource and fine-tuned settings across multiple tasks, models and benchmarks.
Toward Annotator Group Bias in Crowdsourcing

Crowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations. Though there are a few works investigating individual annotator bias, the group effects in annotators are largely overlooked. In this work, we reveal that annotators within the same demographic group tend to show consistent group bias in annotation tasks and thus we conduct an initial study on annotator group bias. We first empirically verify the existence of annotator group bias in various real-world crowdsourcing datasets. Then, we develop a novel probabilistic graphical framework GroupAnno to capture annotator group bias with an extended Expectation Maximization (EM) algorithm. We conduct experiments on both synthetic and real-world datasets. Experimental results demonstrate the effectiveness of our model in modeling annotator group bias in label aggregation and model learning over competitive baselines.
Modeling Endorsement for Multi-Document Abstractive Summarization

A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s). While such content may appear at the beginning of a single document, essential information is frequently reiterated in a set of documents related to a particular topic, resulting in an endorsement effect that increases information salience. In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization. Our method generates a synopsis from each document, which serves as an endorser to identify salient content from other documents. Strongly endorsed text segments are used to enrich a neural encoder-decoder model to consolidate them into an abstractive summary. The method has a great potential to learn from fewer examples to identify salient content, which alleviates the need for costly retraining when the set of documents is dynamically adjusted. Through extensive experiments on benchmark multi-document summarization datasets, we demonstrate the effectiveness of our proposed method over strong published baselines. Finally, we shed light on future research directions and discuss broader challenges of this task using a case study.
Data-driven Heuristics for DC optimal transmission switching problem

The goal of Optimal Transmission Switching (OTS) problem for power systems is to identify a topology of the power grid that minimizes the cost of the system operation while satisfying the operational and physical constraints. Among the most popular methods to solve OTS is to construct approximation via integer linear programming formulations, which often come with big-M inequalities. These big-M inequalities increase, considerably, the difficulty of solving the resulting formulations. Moreover, choosing big-M values optimally is as hard as solving OTS itself. In this paper, we devise two data-driven big-M bound strengthening methods which take network structure, power demands and generation costs into account. We illustrate the robustness of our methods to load changes and impressive runtime improvements of mixed-integer solvers achieved by our methods with extensive experiments on benchmark instances. The speedup by one of the proposed methods is almost 13 times with respect to the exact method.
Learning Semantics: An Opportunity for Effective 6G Communications

Recently, semantic communications are envisioned as a key enabler of future 6G networks. Back to Shannon’s information theory, the goal of communication has long been to guarantee the correct reception of transmitted messages irrespective of their meaning. However, in general, whenever communication occurs to convey a meaning, what matters is the receiver’s understanding of the transmitted message and not necessarily its correct reconstruction. Hence, semantic communications introduce a new paradigm: transmitting only relevant information sufficient for the receiver to capture the meaning intended can save significant communication bandwidth. Thus, this work explores the opportunity offered by semantic communications for beyond 5G networks. In particular, we focus on the benefit of semantic compression. We refer to semantic message as a sequence of well-formed symbols learned from the "meaning" underlying data, which have to be interpreted at the receiver. This requires a reasoning unit, here artificial, on a knowledge base: a symbolic knowledge representation of the specific application. Therefore, we present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications. We first discuss theoretical aspects and successfully design objective functions, which help learn effective semantic encoders and decoders. Eventually, we show promising numerical results for the scenario of text transmission, especially when the sender and receiver speak different languages.
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems

Over the last few years, there has been a move towards data curation for multilingual task-oriented dialogue (ToD) systems that can serve people speaking different languages. However, existing multilingual ToD datasets either have a limited coverage of languages due to the high cost of data curation, or ignore the fact that dialogue entities barely exist in countries speaking these languages. To tackle these limitations, we introduce a novel data curation method that generates GlobalWoZ — a large-scale multilingual ToD dataset globalized from an English ToD dataset for three unexplored use cases of multilingual ToD systems. Our method is based on translating dialogue templates and filling them with local entities in the target-language countries. Besides, we extend the coverage of target languages to 20 languages. We will release our dataset and a set of strong baselines to encourage research on multilingual ToD systems for real use cases.
Influencing Towards Stable Multi-Agent Interactions

Learning in multi-agent environments is difficult due to the non-stationarity introduced by an opponent's or partner's changing behaviors. Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to proactively influence the other agent's strategy to stabilize -- which can restrain the non-stationarity caused by the other agent. We learn a low-dimensional latent representation of the other agent's strategy and the dynamics of how the latent strategy evolves with respect to our robot's behavior. With this learned dynamics model, we can define an unsupervised stability reward to train our robot to deliberately influence the other agent to stabilize towards a single strategy. We demonstrate the effectiveness of stabilizing in improving efficiency of maximizing the task reward in a variety of simulated environments, including autonomous driving, emergent communication, and robotic manipulation. We show qualitative results on our website: https://sites.google. com/view/stable-marl/.
Reachability-based Control Synthesis under Signal Temporal Logic Specifications

In this paper, we investigate the controller design problem for linear disturbed systems under signal temporal logic (STL) specifications imposing both spatial and temporal constraints on system behavior. We first implement zonotope-based techniques to partition the state space into finite cells, then propose an evaluation mechanism to rearrange the time constraints of the STL specification, and finally decompose the global STL formula into finite local STL formulas. In this way, each cell has a local control design problem, which is further formulated into a local optimization problem. To deal with each local optimization problem, we take advantage of the properties of zonotopes and reachability analysis to design local controller. By solving all local optimization problems, all local controllers are combined to guarantee the global STL specification.
Beyond classification: directly training spiking neural networks for semantic segmentation

Spiking neural networks (SNNs) have recently emerged as the low-power alternative to artificial neural networks (ANNs) because of their sparse, asynchronous, and binary event-driven processing. Due to their energy efficiency, SNNs have a high possibility of being deployed for real-world, resource-constrained systems such as autonomous vehicles and drones. However, owing to their non-differentiable and complex neuronal dynamics, most previous SNN optimization methods have been limited to image recognition. In this paper, we explore the SNN applications beyond classification and present semantic segmentation networks configured with spiking neurons. Specifically, we first investigate two representative SNN optimization techniques for recognition tasks (i.e., ANN-SNN conversion and surrogate gradient learning) on semantic segmentation datasets. We observe that, when converted from ANNs, SNNs suffer from high latency and low performance due to the spatial variance of features. Therefore, we directly train networks with surrogate gradient learning, resulting in lower latency and higher performance than ANN-SNN conversion. Moreover, we redesign two fundamental ANN segmentation architectures (i.e., Fully Convolutional Networks and DeepLab) for the SNN domain. We conduct experiments on three semantic segmentation benchmarks including PASCAL VOC2012 dataset, DDD17 event-based dataset, and synthetic segmentation dataset combined CIFAR10 and MNIST datasets. In addition to showing the feasibility of SNNs for semantic segmentation, we show that SNNs can be more robust and energy-efficient compared to their ANN counterparts in this domain.
Identifying Similar Test Cases That Are Specified in Natural Language

Software testing is still a manual process in many industries, despite the recent improvements in automated testing techniques. As a result, test cases (which consist of one or more test steps that need to be executed manually by the tester) are often specified in natural language by different employees and many redundant test cases might exist in the test suite. This increases the (already high) cost of test execution. Manually identifying similar test cases is a time-consuming and error-prone task. Therefore, in this paper, we propose an unsupervised approach to identify similar test cases. Our approach uses a combination of text embedding, text similarity and clustering techniques to identify similar test cases. We evaluate five different text embedding techniques, two text similarity metrics, and two clustering techniques to cluster similar test steps and three techniques to identify similar test cases from the test step clusters. Through an evaluation in an industrial setting, we showed that our approach achieves a high performance to cluster test steps (an F-score of 87.39%) and identify similar test cases (an F-score of 86.13%). Furthermore, a validation with developers indicates several different practical usages of our approach (such as identifying redundant test cases), which help to reduce the testing manual effort and time.
Quantum Yield and Charge Diffusion in the Nancy Grace Roman Space Telescope Infrared Detectors

Weak gravitational lensing is a powerful tool for studying the growth of structure across cosmic time. The shear signal required for weak lensing analyses is very small, so any undesirable detector-level effects which distort astronomical images can significantly contaminate the inferred shear. The Nancy Grace Roman Space Telescope (Roman) will fly a focal plane with 18 Teledyne H4RG-10 near-infrared (IR) detector arrays; these have never before been used for weak lensing and they present different instrument calibration challenges relative to charge-coupled devices. A pair of previous investigations demonstrated that spatiotemporal correlations of flat field images can effectively separate the brighter-fatter effect (BFE) and interpixel capacitance (IPC). A third paper in the series introduced a Fourier-space treatment of these correlations which allowed the authors to expand to higher orders in BFE, IPC, and classical nonlinearity. This work expands the previous formalism to include quantum yield and charge diffusion. We test the updated formalism on simulations and show that we can recover input visible characterization values to within a few percent. We then apply the formalism to visible and IR flat field data from three Roman flight candidate detectors. We find that BFE is present in all detectors and that the magnitude of its central pixel value is comparable between visible data and IR data. We fit a 2D Gaussian model to the charge diffusion at 0.5 μm wavelength, and find variances of C 11 = 0.1066 ± 0.0011 pix2 in the horizontal direction, C 22 = 0.1136 ± 0.0012 pix2 in the vertical direction, and a covariance of C 12 = 0.0001 ± 0.0007 pix2 (stat) for SCA 20829. Last, we convert the asymmetry of the charge diffusion into an equivalent shear signal using the sensitivity coefficients for the Roman survey, and find a contamination of the shear correlation function to be ξ + ∼ 10−6 for each detector. This exceeds Roman’s allotted error budget for the measurement by a factor of (10) in power (amplitude squared) but can likely be mitigated through standard methods for fitting the point-spread function (PSF) since for weak lensing applications the charge diffusion can be treated as a contribution to the PSF. Further work considering the impact of charge diffusion and quantum yield on shear measurements will follow once all detectors covering the Roman focal plane are selected.
Guiding Visual Question Generation

In traditional Visual Question Generation (VQG), most images have multiple concepts (e.g. objects and categories) for which a question could be generated, but models are trained to mimic an arbitrary choice of concept as given in their training data. This makes training difficult and also poses issues for evaluation – multiple valid questions exist for most images but only one or a few are captured by the human references. We present Guiding Visual Question Generation - a variant of VQG which conditions the question generator on categorical information based on expectations on the type of question and the objects it should explore. We propose two variant families: (i) an explicitly guided model that enables an actor (human or automated) to select which objects and categories to generate a question for; and (ii) 2 types of implicitly guided models that learn which objects and categories to condition on, based on discrete variables. The proposed models are evaluated on an answer-category augmented VQA dataset and our quantitative results show a substantial improvement over the current state of the art (over 9 BLEU-4 increase). Human evaluation validates that guidance helps the generation of questions that are grammatically coherent and relevant to the given image and objects.
Thermally driven winds in ULXs

The presence of radiatively driven outflows is well established in ultraluminous X-ray sources (ULXs). These outflows are optically thick and can reprocess a significant fraction of the accretion luminosity. Assuming isotropic emission, escaping radiation from the outflow’s photosphere has the potential to irradiate the outer disc. Here, we explore how the atmosphere of the outer disc would respond to such irradiation, and specifically whether unstable heating may lead to significant mass loss via thermally-driven winds. We find that, for a range of physically relevant system parameters, this mass loss may actually switch off the inflow entirely and potentially drive limit-cycle behaviour (likely modulated on the timescale of the outer disc). In ULXs harbouring neutron stars, magnetic fields tend to have a slight destabilizing effect; for the strongest magnetic fields and highest accretion rates, this can push otherwise stable systems into the unstable regime. We explore the prevalence of the instability in a simulated sample of ULXs obtained from a binary population synthesis calculation. We find that almost all neutron star and black hole ULXs with Eddington-scaled accretion rates of ¤ <0 < 100 should be able to drive powerful outflows from their outer discs. Several known ULXs are expected to lie in this regime; the persistence of accretion in these sources implies the irradiation may be anisotropic which can be reconciled with the inferred reprocessed (optical) emission if some of this originates in the wind photosphere or irradiation of the secondary star.