Miracle-dz/UniFAR-FTUs huggingface dataset:
Epistemic Modality and Coordination under Uncertainty

Communication facilitates coordination, but coordination might fail if there's too much uncertainty. I discuss a scenario in which vagueness-driven uncertainty undermines the possibility of publicly sharing a belief. I then show that asserting an epistemic modal sentence, 'Might p', can reveal the speaker's uncertainty, and that this may improve the chances of coordination despite the lack of a common epistemic ground. This provides a game-theoretic rationale for epistemic modality. The account draws on a standard relational semantics for epistemic modality, Stalnaker's theory of assertion as informative update, and a Bayesian framework for reasoning under uncertainty.
Learning to Inference with Early Exit in the Progressive Speech Enhancement

In real scenarios, it is often necessary and significant to control the inference speed of speech enhancement systems under different conditions. To this end, we propose a stage-wise adaptive inference approach with early exit mechanism for progressive speech enhancement. Specifically, in each stage, once the spectral distance between adjacent stages lowers the empirically preset threshold, the inference will terminate and output the estimation, which can effectively accelerate the inference speed. To further improve the performance of existing speech enhancement systems, PL-CRN++ is proposed, which is an improved version over our preliminary work PL-CRN and combines stage recurrent mechanism and complex spectral mapping. Extensive experiments are conducted on the TIMIT corpus, the results demonstrate the superiority of our system over state-of-the-art baselines in terms of PESQ, ESTOI and DNSMOS. Moreover, by adjusting the threshold, we can easily control the inference efficiency while sustaining the system performance.
Quantum Computing - from NISQ to PISQ

Given the impeding timeline of developing good quality quantum processing units, it is the moment to rethink the approach to advance quantum computing research. Rather than waiting for quantum hardware technologies to mature, we need to start assessing in tandem the impact of the occurrence of quantum computing in various scientific fields.However, to this purpose, we need to use a complementary but quite different approach than proposed by the NISQ vision, which is heavily focused on and burdened by the engineering challenges.That is why we propose and advocate the PISQ-approach: Perfect Intermediate-Scale Quantum computing based on the already known concept of perfect qubits.This will allow researchers to focus much more on the development of new applications by defining the algorithms in terms of perfect qubits and evaluate them on quantum computing simulators that are executed on supercomputers.It is not the long-term solution but will currently allow universities to research on quantum logic and algorithms and companies can already start developing their internal know-how on quantum solutions.
Learning Versus Unlearning: An Experiment on Retractions

Widely discredited ideas nevertheless persist. Why do people fail to “unlearn”? We study one explanation: beliefs are resistant to retractions (the revoking of earlier information). Our experimental design identies unlearning—i.e., updating from retractions—and enables its comparison with learning from equivalent new information. Across dierent kinds of retractions—for instance, those consistent or contradictory with the prior, or those occurring when prior beliefs are either extreme or moderate—subjects do not fully unlearn from retractions and update less from them than from equivalent new information. is phenomenon is not explained by most of the well-studied violations of Bayesian updating, which yield diering predictions in our design. However, it is consistent with diculties in conditional reasoning, which have been documented in other domains and circumstances.
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge

Treatment effect estimation from observational data is a fundamental problem in causal inference. There are two very different schools of thought that have tackled this problem. On one hand, Pearlian framework commonly assumes structural knowledge (provided by an expert) in form of directed acyclic graphs and provides graphical criteria such as back-door criterion to identify valid adjustment sets. On other hand, potential outcomes (PO) framework commonly assumes that all observed features satisfy ignorability (i.e., no hidden confounding), which in general is untestable. In prior works that attempted to bridge these frameworks, there is an observational criteria to identify an anchor variable and if a subset of covariates (not involving the anchor variable) passes a suitable conditional independence criteria, then that subset is a valid back-door. Our main result strengthens these prior results by showing that under a different expert-driven structural knowledge -- that one variable is a direct causal parent of treatment variable -- remarkably, testing for subsets (not involving the known parent variable) that are valid back-doors is equivalent to an invariance test. Importantly, we also cover the non-trivial case where entire set of observed features is not ignorable (generalizing the PO framework) without requiring knowledge of all parents of treatment variable. Our key technical idea involves generation of a synthetic sub-sampling (or environment) variable that is a function of the known parent variable. In addition to designing an invariance test, this sub-sampling variable allows us to leverage Invariant Risk Minimization, and thus, connects finding valid adjustments (in non-ignorable observational setting) to representation learning. We demonstrate effectiveness and tradeoffs of our approaches on a variety of synthetic data as well as real causal effect estimation benchmarks.
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization

When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy. In this work, we leverage recent methodological advances in Bayesian optimization over high-dimensional search spaces and multi-objective Bayesian optimization to efficiently explore these trade-offs for a production-scale on-device natural language understanding model at Facebook.
Large sets of generating tuples for Lie groups

We prove that for a connected, semisimple linear Lie group G the spaces of generating pairs of elements or subgroups are well-behaved in a number of ways: the set of pairs of elements generating a dense subgroup is Zariski-open in the compact case, Euclidean-open in general, and always dense. Similarly, for sufficiently generic circle subgroups Hi, i = 1, 2 of G, the space of conjugates of Hi that generate a dense subgroup is always Zariski-open and dense. Similar statements hold for pairs of Lie subalgebras of the Lie algebra Lie(G).
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images

Accurate liver and lesion segmentation from computed tomography (CT) images are highly demanded in clinical practice for assisting the diagnosis and assessment of hepatic tumor disease. However, automatic liver and lesion segmentation from contrast-enhanced CT volumes is extremely challenging due to the diversity in contrast, resolution, and quality of images. Previous methods based on UNet for 2D slice-by-slice or 3D volume-by-volume segmentation either lack sufficient spatial contexts or suffer from high GPU computational cost, which limits the performance. To tackle these issues, we propose a novel context-aware PolyUNet for accurate liver and lesion segmentation. It jointly explores structural diversity and consecutive t-adjacent slices to enrich feature expressive power and spatial contextual information while avoiding the overload of GPU memory consumption. In addition, we utilize zoom out/in and two-stage refinement strategy to exclude the irrelevant contexts and focus on the specific region for the fine-grained segmentation. Our method achieved very competitive performance at the MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge among all tasks with a single model and ranked the $3^{rd}$, $12^{th}$, $2^{nd}$, and $5^{th}$ places in the liver segmentation, lesion segmentation, lesion detection, and tumor burden estimation, respectively.
Gravitational waves from resonant amplification of curvature perturbations during inflation

Parametric resonance in a single-field inflationary model with a periodic structure on the potential gives rise to curvature perturbations with large amplitudes on small scales, which could result in observable primordial black holes (PBHs) and concomitant gravitational waves (GWs) induced by curvature perturbations in the radiation-dominated era. In such a model, GWs associated with the PBH formation were investigated in ref. [1]. In this paper, we consider a stochastic GW background sourced by inflaton perturbations resonantly amplified during inflation. We compute the energy spectra of induced GWs produced both during inflation and in the radiation-dominated era, and find that the peak of the energy spectrum of the former is much higher than that of the latter, but is located at a lower frequency. Moreover, the energy spectrum of induced GWs produced during inflation exhibits a unique oscillating character in the ultraviolet region. Both the stochastic GW backgrounds are expected to be detected by future space-based laser interferometers.
Four new planetesimals around typical and pre-main-sequence  stars (PLATYPUS) debris discs at 8.8 mm

Millimetre continuum observations of debris discs can provide insights into the physical and dynamical properties of the unseen planetesimals that these discs host. The material properties and collisional models of planetesimals leave their signature on the grain size distribution, which can be traced through the millimetre spectral index. We present 8.8 mm observations of the debris discs HD 48370, CPD-72 2713, HD 131488, and HD 32297 using the Australian Telescope Compact Array (ATCA) as part of the PLanetesimals Around TYpical Pre-main-seqUence Stars (PLATYPUS) survey. We detect all four targets with a characteristic beam size of 5 arcsec and derive a grain size distribution parameter that is consistent with collisional cascade models and theoretical predictions for parent planetesimal bodies where binding is dominated by self-gravity. We combine our sample with 19 other millimetre-wavelength-detected debris discs from the literature and calculate a weighted mean grain size power-law index that is close to analytical predictions for a classical steady-state collisional cascade model. We suggest the possibility of two distributions of q in our debris disc sample; a broad distribution (where q ∼ 3.2–3.7) for ‘typical’ debris discs (gas-poor/non-detection), and a narrow distribution (where q < 3.2) for bright gas-rich discs. Or alternatively, we suggest that there exists an observational bias between the grain size distribution parameter and absolute flux that may be attributed to the detection rates of faint debris discs at $\rm \sim$cm wavelengths.
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects

Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system. As it involves reasoning about interventions performed in the physical world, recourse is fundamentally a causal problem. Existing methods compute the effect of recourse actions using a causal model learnt from data under the assumption of no hidden confounding and modelling assumptions such as additive noise. Building on the seminal work of Balke and Pearl (1994), we propose an alternative approach for discrete random variables which relaxes these assumptions and allows for unobserved confounding and arbitrary structural equations. The proposed approach only requires specification of the causal graph and confounding structure and bounds the expected counterfactual effect of recourse actions. If the lower bound is above a certain threshold, i.e., on the other side of the decision boundary, recourse is guaranteed in expectation.
nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving. While there is a growing body of ML-based motion planners, the lack of established datasets and metrics has limited the progress in this area. Existing benchmarks for autonomous vehicle motion prediction have focused on short-term motion forecasting, rather than long-term planning. This has led previous works to use open-loop evaluation with L2-based metrics, which are not suitable for fairly evaluating long-term planning. Our benchmark overcomes these limitations by introducing a large-scale driving dataset, lightweight closed-loop simulator, and motion-planning-specific metrics. We provide a high-quality dataset with 1500h of human driving data from 4 cities across the US and Asia with widely varying traffic patterns (Boston, Pittsburgh, Las Vegas and Singapore). We will provide a closed-loop simulation framework with reactive agents and provide a large set of both general and scenario-specific planning metrics. We plan to release the dataset at NeurIPS 2021 and organize benchmark challenges starting in early 2022.
SENT: Sentence-level Distant Relation Extraction via Negative Training

Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications that need the exact relation type. Directly using bag labels for sentence-level training will introduce much noise, thus severely degrading performance. In this work, we propose the use of negative training (NT), in which a model is trained using complementary labels regarding that “the instance does not belong to these complementary labels”. Since the probability of selecting a true label as a complementary label is low, NT provides less noisy information. Furthermore, the model trained with NT is able to separate the noisy data from the training data. Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction. SENT not only filters the noisy data to construct a cleaner dataset, but also performs a re-labeling process to transform the noisy data into useful training data, thus further benefiting the model’s performance. Experimental results show the significant improvement of the proposed method over previous methods on sentence-level evaluation and de-noise effect.
An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN

A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: (i) high power consumption, and (ii) the need to integrate an accelerometer sensor in a wearable device (which is not required in certain wearables). This paper proposes a low-power non-accelerometer-based PPG motion artifacts removal method outperforming the accuracy of the existing methods. We use Cycle Generative Adversarial Network to reconstruct clean PPG signals from noisy PPG signals. Our novel machine-learning-based technique achieves 9.5 times improvement in motion artifact removal compared to the state-of-the-art without using extra sensors such as an accelerometer, which leads to 45% improvement in energy efficiency.
Aluminium storage using nitrogen-doped graphene nanoribbons from first principles

Pristine graphene interacts relatively weakly with Al, which is a specie of importance for novel generations of metal-ion batteries. We employ DFT calculations to explore the possibility of enhancing Al interaction with graphene. We investigate non-doped and N-doped graphene nanoribbons, address the impact of the edge sites, which are always present to some extent in real samples, and N-containing defects on the material's reactivity towards Al. The results are compared to that of pristine graphene. We show that introduction of edges does not affect the energetics of Al adsorption significantly by itself. On the other hand, N-doping of graphene nanoribbons is found to affect the adsorption energy of Al to the extent that strongly depends on the type of N-containing defect. While graphitic and pyrrolic N induce minimal changes, the introduction of edge NO group and doping with in plane pyridinic N result in Al adsorption nearly twice as strong as on pristine graphene. The obtained results could guide the further design of advanced materials for Al-ion rechargeable batteries.
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks

Learning node representations on temporal graphs is a fundamental step to learn real-word dynamic graphs efficiently. Real-world graphs have the nature of continuously evolving over time, such as changing edges weights, removing and adding nodes and appearing and disappearing of edges, while previous graph representation learning methods focused generally on static graphs. We present ConvDySAT as an enhancement of DySAT, one of the state-of-the-art dynamic methods, by augmenting convolution neural networks with the self-attention mechanism, the employed method in DySAT to express the structural and temporal evolution. We conducted single-step link prediction on a communication network and rating network, Experimental results show significant performance gains for ConvDySAT over various state-of-the-art methods.
Applied Topological Analysis of Crystal Structures with the Program Package ToposPro

Basic concepts of computer topological analysis of crystal structures realized in the current version of the program package ToposPro are considered. Applications of the ToposPro methods to various classes of chemical compounds—coordination polymers, molecular crystals, supramolecular ensembles, inorganic ionic compounds, intermetallics, fast-ion conductors, microporous materials—are illustrated by many examples. It is shown that chemically and crystallographically different structures can be automatically treated in a similar way with the ToposPro approaches.
Bio‐inspired “green” nanocomposite using hydroxyapatite synthesized from eggshell waste and soy protein

Eco-friendly and inexpensive “green” nanocomposites with enhanced functional performances were developed by combining nanoscale hydroxyapatite (HA) synthesized from eggshell waste (nEHA) and protein-based polymer extracted from defatted soybean residues. nEHA was synthesized from chicken eggshells using an energy efficient microwave-assisted wet chemical precipitation method. Transmission electron microscopy, X-ray diffraction, and energy-dispersive X-ray spectroscopy studies confirmed the nanometer scale (diameter: 4–14 nm and length: 5–100 nm) of calcium-deficient (Ca/P ratio ∼1.53) needle-like HA. Uniform dispersion of nEHA in soy protein isolate (SPI) solution was obtained by modifying nEHA surface using a polyelectrolyte (sodium polyacrylate) dispersant via irreversible adsorption. Green nanocomposite films were prepared from SPI and surface-modified nEHA with the help of a natural plasticizer “glycerol” by solution casting. Significant improvements in tensile modulus and strength were achieved owing to the inclusion of uniformly dispersed nEHA in SPI sheets. Overall, this work provides a green pathway of fabricating nanocomposites using naturally occurring renewable polymer and inorganic moieties from eggshell waste that emphasizes the possibilities for replacing some petroleum-based polymers in packaging and other applications. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 43477.
Testing large-scale structure measurements against Fisher matrix predictions

We compare Baryonic Acoustic Oscillation (BAO) and Redshift Space Distortion (RSD) measurements from recent galaxy surveys with their Fisher matrix based predictions. Measurements of the position of the BAO signal lead to constraints on the comoving angular diameter distance DM and the Hubble distance DH that agree well with their Fisher matrix based expectations. However, RSD-based measurements of the growth rate fσ8 do not agree with the predictions made before the surveys were undertaken, even when repeating those predictions using the actual survey parameters. We show that this is due to a combination of effects including degeneracies with the geometric parameters DM and DH , and optimistic assumptions about the scale to which the linear signal can be extracted. We show that measurements using current data and large-scale modelling techniques extract an equivalent amount of signal to that in the linear regime for k ≲ 0.08 h Mpc-1, remarkably independent of the sample properties and redshifts covered.
Speeding Up OPFython with Numba

A graph-inspired classifier, known as Optimum-Path Forest (OPF), has proven to be a state-of-the-art algorithm comparable to Logistic Regressors, Support Vector Machines in a wide variety of tasks. Recently, its Python-based version, denoted as OPFython, has been proposed to provide a more friendly framework and a faster prototyping environment. Nevertheless, Python-based algorithms are slower than their counterpart C-based algorithms, impacting their performance when confronted with large amounts of data. Therefore, this paper proposed a simple yet highly efficient speed up using the Numba package, which accelerates Numpy-based calculations and attempts to increase the algorithm’s overall performance. Experimental results showed that the proposed approach achieved better results than the naïve Python-based OPF and speeded up its distance measurement calculation.
Fire!

In this paper, we provide an epistemic analysis of a simple variant of the fundamental consistent broadcasting primitive for byzantine fault-tolerant asynchronous distributed systems. Our Firing Rebels with Relay (FRR) primitive enables agents with a local preference for acting/not acting to trigger an action (FIRE) at all correct agents, in an all-or-nothing fashion. By using the epistemic reasoning framework for byzantine multi-agent systems introduced in our TARK’19 paper, we develop the necessary and sufficient state of knowledge that needs to be acquired by the agents in order to FIRE. It involves eventual common hope (a modality related to belief), which we show to be attained already by achieving eventual mutual hope in the case of FRR. We also identify subtle variations of the necessary and sufficient state of knowledge for FRR for different assumptions on the local preferences.
The Quest to Simulate Excited-State Dynamics of Transition Metal Complexes

This Perspective describes current computational efforts in the field of simulating photodynamics of transition metal complexes. We present the typical workflows and feature the strengths and limitations of the different contemporary approaches. From electronic structure methods suitable to describe transition metal complexes to approaches able to simulate their nuclear dynamics under the effect of light, we give particular attention to build a bridge between theory and experiment by critically discussing the different models commonly adopted in the interpretation of spectroscopic experiments and the simulation of particular observables. Thereby, we review all the studies of excited-state dynamics on transition metal complexes, both in gas phase and in solution from reduced to full dimensionality.