Miracle-dz/UniFAR-FTUs huggingface dataset:
Answer-Driven Visual State Estimator for Goal-Oriented Visual Dialogue

A goal-oriented visual dialogue involves multi-turn interactions between two agents, Questioner and Oracle. During which, the answer given by Oracle is of great significance, as it provides golden response to what Questioner concerns. Based on the answer, Questioner updates its belief on target visual content and further raises another question. Notably, different answers drive into different visual beliefs and future questions. However, existing methods always indiscriminately encode answers after much longer questions, resulting in a weak utilization of answers. In this paper, we propose an Answer-Driven Visual State Estimator (ADVSE) to impose the effects of different answers on visual states. First, we propose an Answer-Driven Focusing Attention (ADFA) to capture the answer-driven effect on visual attention by sharpening question-related attention and adjusting it by answer-based logical operation at each turn. Then based on the focusing attention, we get the visual state estimation by Conditional Visual Information Fusion (CVIF), where overall information and difference information are fused conditioning on the question-answer state. We evaluate the proposed ADVSE to both question generator and guesser tasks on the large-scale GuessWhat? dataset and achieve the state-of-the-art performances on both tasks. The qualitative results indicate that the ADVSE boosts the agent to generate highly efficient questions and obtains reliable visual attentions during the reasonable question generation and guess processes.
Multi-agent Social Reinforcement Learning Improves Generalization

Social learning is a key component of human and animal intelligence. By taking cues from the behavior of experts in their environment, social learners can acquire sophisticated behavior and rapidly adapt to new circumstances. This paper investigates whether independent reinforcement learning (RL) agents in a multi-agent environment can use social learning to improve their performance using cues from other agents. We find that in most circumstances, vanilla model-free RL agents do not use social learning, even in environments in which individual exploration is expensive. We analyze the reasons for this deficiency, and show that by introducing a model-based auxiliary loss we are able to train agents to lever-age cues from experts to solve hard exploration tasks. The generalized social learning policy learned by these agents allows them to not only outperform the experts with which they trained, but also achieve better zero-shot transfer performance than solo learners when deployed to novel environments with experts. In contrast, agents that have not learned to rely on social learning generalize poorly and do not succeed in the transfer task. Further,we find that by mixing multi-agent and solo training, we can obtain agents that use social learning to out-perform agents trained alone, even when experts are not avail-able. This demonstrates that social learning has helped improve agents' representation of the task itself. Our results indicate that social learning can enable RL agents to not only improve performance on the task at hand, but improve generalization to novel environments.
An uncountable Mackey–Zimmer theorem

The Mackey-Zimmer theorem classifies ergodic group extensions $X$ of a measure-preserving system $Y$ by a compact group $K$, by showing that such extensions are isomorphic to a group skew-product $X \equiv Y \rtimes_\rho H$ for some closed subgroup $H$ of $K$. An analogous theorem is also available for ergodic homogeneous extensions $X$ of $Y$, namely that they are isomorphic to a homogeneous skew-product $Y \rtimes_\rho H/M$. These theorems have many uses in ergodic theory, for instance playing a key role in the Host-Kra structural theory of characteristic factors of measure-preserving systems. The existing proofs of the Mackey-Zimmer theorem require various "countability", "separability", or "metrizability" hypotheses on the group $\Gamma$ that acts on the system, the base space $Y$, and the group $K$ used to perform the extension. In this paper we generalize the Mackey-Zimmer theorem to "uncountable" settings in which these hypotheses are omitted, at the cost of making the notion of a measure-preserving system and a group extension more abstract. However, this abstraction is partially counteracted by the use of a "canonical model" for abstract measure-preserving systems developed in a companion paper. In subsequent work we will apply this theorem to also obtain uncountable versions of the Host-Kra structural theory.
Approximation of the Double Travelling Salesman Problem with Multiple Stacks

The Double Travelling Salesman Problem with Multiple Stacks, DTSPMS, deals with the collect and delivery of n commodities in two distinct cities, where the pickup and the delivery tours are related by LIFO constraints. During the pickup tour, commodities are loaded into a container of k rows, or stacks, with capacity c. This paper focuses on computational aspects of the DTSPMS, which is NP-hard. We first review the complexity of two critical subproblems: deciding whether a given pair of pickup and delivery tours is feasible and, given a loading plan, finding an optimal pair of pickup and delivery tours, are both polynomial under some conditions on k and c. We then prove a (3k)/2 standard approximation for the MinMetrickDTSPMS, where k is a universal constant, and other approximation results for various versions of the problem. We finally present a matching-based heuristic for the 2DTSPMS, which is a special case with k=2 rows, when the distances are symmetric. This yields a 1/2-o(1), 3/4-o(1) and 3/2+o(1) standard approximation for respectively Max2DTSPMS, its restriction Max2DTSPMS-(1,2) with distances 1 and 2, and Min2DTSPMS-(1,2), and a 1/2-o(1) differential approximation for Min2DTSPMS and Max2DTSPMS.
A novel black-hole mass scaling relation based on Coronal lines and supported by accretion predictions.

Getting insights on the shape and nature of the ionizing continuum in astronomical objects is often done via indirect methods as high energy photons are absorbed by our Galaxy. This work explores the ionization continuum of active galactic nuclei (AGN) using the ubiquitous coronal lines. Using bona-fide BH mass estimates from reverberation mapping and the line ratio [Si VI] 1.963 micron/Br$\gamma_{\rm broad}$ as tracer of the AGN ionizing continuum, a novel BH-mass scaling relation of the form log($M_{\rm BH}) = (6.40\pm 0.17) - (1.99\pm 0.37) \times$ log ([Si VI]/Br$\gamma_{\rm broad})$, over the BH mass interval, $10^6 - 10^8$ M$_{\odot}$ with dispersion 0.47 dex is found. Following on the thin accretion disc approximation and after surveying a basic parameter space for coronal lines production, we believe that a key parameter driving this anti-correlation is the effective temperature of the accretion disc, this being effectively sampled by the coronal line gas. Accordingly, the observed anti-correlation becomes formally in line with the thin accretion disc prediction $T_{\rm{disc}} \propto {M_{\rm BH}}^{-1/4}$.
Non-equilibrium ionization and radiative transport in an evolving supernova remnant

We present numerical simulations of the evolution of a supernova (SN) remnant expanding into a uniform background medium with density $n_H = 1.0$ cm$^{-3}$ and temperature of $10^4$ K. We include a dynamically evolving non-equilibrium ionisation (NEI) network (consisting of all the ions of H, He, C, N, O, Ne, Mg, Si, S, Fe), frequency dependent radiation transfer (RT), thermal conduction, and a simple dust evolution model, all intra-coupled to each other and to the hydrodynamics. We assume spherical symmetry. Photo-ionisation, radiation losses, photo-heating, charge-exchange heating/cooling and radiation pressure are calculated on-the-fly depending on the local radiation field and ion fractions. We find that the dynamics and energetics (but not the emission spectra) of the SN remnants can be well modelled by collisional equilibrium cooling curves even in the absence of non-equilibrium cooling and radiative transport. We find that the effect of precursor ionising radiation at different stages of SN remnant are dominated by rapid cooling of the shock and differ from steady state shocks. The predicted column densities of different ions such as N II, C IV and N V, can be higher by up to several orders of magnitude compared to steady state shocks. We also present some high esolution emission spectra that can be compared with the observed remnants to obtain important information about the physical and chemical states of the remnant, as well as constrain the background ISM.
Neural encoding with visual attention

Visual perception is critically influenced by the focus of attention. Due to limited resources, it is well known that neural representations are biased in favor of attended locations. Using concurrent eye-tracking and functional Magnetic Resonance Imaging (fMRI) recordings from a large cohort of human subjects watching movies, we first demonstrate that leveraging gaze information, in the form of attentional masking, can significantly improve brain response prediction accuracy in a neural encoding model. Next, we propose a novel approach to neural encoding by including a trainable soft-attention module. Using our new approach, we demonstrate that it is possible to learn visual attention policies by end-to-end learning merely on fMRI response data, and without relying on any eye-tracking. Interestingly, we find that attention locations estimated by the model on independent data agree well with the corresponding eye fixation patterns, despite no explicit supervision to do so. Together, these findings suggest that attention modules can be instrumental in neural encoding models of visual stimuli.
BRON - Linking Attack Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations

Many public sources of cyber threat and vulnerability information exist to serve the defense of cyber systems. This paper proposes BRON which is a composite of MITRE's ATT&CK MATRIX, NIST's Common Weakness Enumerations (CWE), Common Vulnerabilities and Exposures (CVE), and Common Attack Pattern Enumeration and Classification, CAPEC. BRON preserves all entries and relations while enabling bi-directional, relational path tracing. It exploits attack patterns to trace between the objectives and means of attacks to the vulnerabilities and affected software and hardware configurations they target. We inventory and analyze BRON's sources to gauge any gap between information on attacks and information on attack targets. We also analyze BRON for information that is a by-product of its mission.
WeChat Neural Machine Translation Systems for WMT20

We participate in the WMT 2020 shared newstranslation task on Chinese→English. Our system is based on the Transformer (Vaswaniet al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese→English system achieves 36.9 case-sensitive BLEU score, which is thehighest among all submissions.
Action Units Recognition by Pairwise Deep Architecture

In this paper, we propose a new automatic Action Units (AUs) recognition method used in a competition, Affective Behavior Analysis in-the-wild (ABAW). Our method tackles a problem of AUs label inconsistency among subjects by using pairwise deep architecture. While the baseline score is 0.31, our method achieved 0.67 in validation dataset of the competition.
Persistent homology advances interpretable machine learning for nanoporous materials

Machine learning for nanoporous materials design and discovery has emerged as a promising alternative to more time-consuming experiments and simulations. The challenge with this approach is the selection of features that enable universal and interpretable materials representations across multiple prediction tasks. We use persistent homology to construct holistic representations of the materials structure. We show that these representations can also be augmented with other generic features such as word embeddings from natural language processing to capture chemical information. We demonstrate our approach on multiple metal-organic framework datasets by predicting a variety of gas adsorption targets. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from commonly used manually curated features. Persistent homology features allow us to locate the pores that correlate best to adsorption at different pressures, contributing to understanding atomic level structure-property relationships for materials design.
Dual Attention Model for Citation Recommendation

Based on an exponentially increasing number of academic articles, discovering and citing comprehensive and appropriate resources has become a non-trivial task. Conventional citation recommender methods suffer from severe information loss. For example, they do not consider the section of the paper that the user is writing and for which they need to find a citation, the relatedness between the words in the local context (the text span that describes a citation), or the importance on each word from the local context. These shortcomings make such methods insufficient for recommending adequate citations to academic manuscripts. In this study, we propose a novel embedding-based neural network called “dual attention model for citation recommendation (DACR)” to recommend citations during manuscript preparation. Our method adapts embedding of three semantic information: words in the local context, structural contexts, and the section on which a user is working. A neural network model is designed to maximize the similarity between the embedding of the three input (local context words, section and structural contexts) and the target citation appearing in the context. The core of the neural network model is composed of self-attention and additive attention, where the former aims to capture the relatedness between the contextual words and structural context, and the latter aims to learn the importance of them. The experiments on real-world datasets demonstrate the effectiveness of the proposed approach.
Supercomputing with MPI meets the Common Workflow Language standards: an experience report

Use of standards-based workflows is still somewhat unusual by high-performance computing users. In this paper we describe the experience of using the Common Workflow Language (CWL) standards to describe the execution, in parallel, of MPI-parallelised applications. In particular, we motivate and describe the simple extension to the specification which was required, as well as our implementation of this within the CWL reference runner. We discuss some of the unexpected benefits, such as simple use of HPC-oriented performance measurement tools, and CWL software requirements interfacing with HPC module systems. We close with a request for comment from the community on how these features could be adopted within versions of the CWL standards.
Leptonic CP Violation and its Origin

This PhD thesis deals with the current experimental hint for large CP violation in the leptonic sector. We first quantify its global statistical significance, by combining the results from relevant neutrino oscillation experiments. As the hint is indirect, other new physics apart from three massive neutrinos could be present, masking the results as direct leptonic CP violation has not yet been conclusively observed. Thus, we move on to checking the robustness of the hint against the framework in which the experimental data is interpreted; in other words, its physical origin. To that purpose, we confront with data the scenario that is less bounded by other experiments: neutral current Non-Standard Interactions (NSI) among neutrinos and matter. In the end, CP violation induced by neutrino masses and leptonic mixing is found to be quite robust, due to the large amount of neutrino flavour transition data collected along three decades. Nevertheless, the next generation LBL accelerator experiments are aimed at precision measurements that could be more severely affected. Luckily, in the last years the COHERENT experiment has provided independent constraints on NSI. In the final chapter of the thesis, the data from the COHERENT experiment is analysed and integrated into the global analyses from previous chapters. These first results could be greatly improved by increasing the statistics of the signal and/or by performing measurements with different nuclei sensitive to different NSI models. For that, the European Spallation Source is an ideal future facility, whose prospects for bounding NSI are also explored. In summary, this thesis takes a global approach to rigorously assess whether cutting-edge leptonic flavour measurements are pointing towards a new strong violation of a symmetry of Nature.
A Survey on Explainability in Machine Reading Comprehension

This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference challenges evolved and the steps which were taken to tackle these challenges. We also present the evaluation methodologies to assess the performance of explainable systems. In addition, we identify persisting open research questions and highlight critical directions for future work.
Can You Trust Your Pose? Confidence Estimation in Visual Localization

Camera pose estimation in large-scale environments is still an open question and, despite recent promising results, it may still fail in some situations. The research so far has focused on improving subcomponents of estimation pipelines, to achieve more accurate poses. However, there is no guarantee for the result to be correct, even though the correctness of pose estimation is critically important in several visual localization applications, such as in autonomous navigation. In this paper we bring to attention a novel research question, pose confidence estimation, where we aim at quantifying how reliable the visually estimated pose is. We develop a novel confidence measure to fulfill this task and show that it can be flexibly applied to different datasets, indoor or outdoor, and for various visual localization pipelines. We also show that the proposed techniques can be used to accomplish a secondary goal: improving the accuracy of existing pose estimation pipelines. Finally, the proposed approach is computationally light-weight and adds only a negligible increase to the computational effort of pose estimation.
A Compare Aggregate Transformer for Understanding Document-grounded Dialogue

Unstructured documents serving as external knowledge of the dialogues help to generate more informative responses. Previous research focused on knowledge selection (KS) in the document with dialogue. However, dialogue history that is not related to the current dialogue may introduce noise in the KS processing. In this paper, we propose a Compare Aggregate Transformer (CAT) to jointly denoise the dialogue context and aggregate the document information for response generation. We designed two different comparison mechanisms to reduce noise (before and during decoding). In addition, we propose two metrics for evaluating document utilization efficiency based on word overlap. Experimental results on the CMU_DoG dataset show that the proposed CAT model outperforms the state-of-the-art approach and strong baselines.
Use of Early Life-Stages of Zebrafish to Assess Toxicity of Sediments Contaminated by Organotin Compounds

ABSTRACT This study examined the response of early life-stages (ELS) of zebrafish to organotin-contaminated sediment from Lake Huruslahti (HL) in Central Finland. A dilution series (0, 10, 33, and 100%) of the native (HL) and the sediment spiked with tributyltin (TBT) determined a dose-response of zebrafish ELS to organotin-contaminated sediment. Sediment elutriates were assessed by bacterial bioluminescence assay and microscopical pathologies of 1–3 days post-fertilization zebrafish (1–3dpfZF). Brain aromatase (cyp19a1b) and tissue vitellogenin (vtg1) were assayed from early-juvenile zebrafish (20dpfZF) exposed to intact sediment. In vivo modulation of cyp19a1b and vtg1 transcripts in 20dpfZF was targeted to screen potential sediment contamination by TBT, an anti-estrogenic chemical. Bioluminescence assay (EC5030min = 34.6% of HL-100) determined that the HL-100 elutriate was toxic. Exposed developing embryos (1–3dpf) showed toxicities, including lowered heart rate. Results showed down-regulation of cyp19a1b and up-regulation of vtg1 in 20dpfZF exposed to TBT-spiked sediment (185–1620 µg/kg d.w.).
DASGIL: Domain Adaptation for Semantic and Geometric-Aware Image-Based Localization

Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc. Image retrieval for localization is an efficient and effective solution to the problem. In this paper, we propose a novel multi-task architecture to fuse the geometric and semantic information into the multi-scale latent embedding representation for visual place recognition. To use the high-quality ground truths without any human effort, the effective multi-scale feature discriminator is proposed for adversarial training to achieve the domain adaptation from synthetic virtual KITTI dataset to real-world KITTI dataset. The proposed approach is validated on the Extended CMU-Seasons dataset and Oxford RobotCar dataset through a series of crucial comparison experiments, where our performance outperforms state-of-the-art baselines for retrieval-based localization and large-scale place recognition under the challenging environment.
Lognormal Distributions and the Concept of Community Equilibrium

The lognormal distribution of individuals among species fits many sets of ecological data remarkably well. Dispute exists however, as to whether this distribution is a mathematical artifact of large numbers or whether it has a biological meaning. In analysing general lognormal curves we argue that the characteristics discovered by Preston relating to the canonical lognormal are in fact merely mathematical properties of the whole family of lognormal curves. We develop a model that assumes that a community is composed of groups of species each differently adapted to a habitat. By integrating the distributions of these groups a lognormal distribution results if the community is in equilibrium. If the community is disturbed however, many species change their abundance and then species abundance curves show multiple maxima and no longer conform to a single lognormal curve.