Miracle-dz/UniFAR-FTUs huggingface dataset:
A competition of critics in human decision-making

Recent experiments and theories of human decision-making suggest positive and negative errors are processed and encoded differently by serotonin and dopamine, with serotonin possibly serving to oppose dopamine and protect against risky decisions. We introduce a temporal difference (TD) model of human decision-making to account for these features. Our model involves two critics, an optimistic learning system and a pessimistic learning system, whose predictions are integrated in time to control how potential decisions compete to be selected. Our model predicts that human decision-making can be decomposed along two dimensions: the degree to which the individual is sensitive to (1) risk and (2) uncertainty. In addition, we demonstrate that the model can learn about reward expectations and uncertainty, and provide information about reaction time despite not modeling these variables directly. Lastly, we simulate a recent experiment to show how updates of the two learning systems could relate to dopamine and serotonin transients, thereby providing a mathematical formalism to serotonin’s hypothesized role as an opponent to dopamine. This new model should be useful for future experiments on human decision-making.
Plasma as a tool for enhancing insulation properties of polymer composites

The performance of electrical insulating polymers measured by comparative tracking index can be improved by flame retardant fillers in the polymer–matrix, but is not necessary effective due to persisting surface layers. We demonstrated a method for selective etching of polyphenol–matrix composites which leads to improvement of the tracking properties. Selective etching was performed by low pressure inductively coupled oxygen plasma generated at 35 Pa. Species created in discharge were monitored in situ during the treatment by optical emission spectroscopy (OES). Their surface reactions were established indirectly from X-ray photoelectron spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDXS) after plasma treatment. The effective etching was done by oxygen species which removed off surface polymer layer within 10 to 15 s during plasma pulse operation to prevent surface overheating. The selective polymer removal improved tracking properties of the 70% glass filler reinforced composites up to 56%, whereas the performance level decreased with the decrease in filler content. This technology provides an important enhancement of polymer–matrix composites' tracking properties and resistance of composites to electrical short-circuit fire.
AutoCaption: Image Captioning with Neural Architecture Search

Image captioning transforms complex visual information into abstract natural language for representation, which can help computers understanding the world quickly. However, due to the complexity of the real environment, it needs to identify key objects and realize their connections, and further generate natural language. The whole process involves a visual understanding module and a language generation module, which brings more challenges to the design of deep neural networks than other tasks. Neural Architecture Search (NAS) has shown its important role in a variety of image recognition tasks. Besides, RNN plays an essential role in the image captioning task. We introduce a AutoCaption method to better design the decoder module of the image captioning where we use the NAS to design the decoder module called AutoRNN automatically. We use the reinforcement learning method based on shared parameters for automatic design the AutoRNN efficiently. The search space of the AutoCaption includes connections between the layers and the operations in layers both, and it can make AutoRNN express more architectures. In particular, RNN is equivalent to a subset of our search space. Experiments on the MSCOCO datasets show that our AutoCaption model can achieve better performance than traditional hand-design methods. Our AutoCaption obtains the best published CIDEr performance of 135.8% on COCO Karpathy test split. When further using ensemble technology, CIDEr is boosted up to 139.5%.
The COVID-19 Infodemic: Twitter versus Facebook

The global spread of the novel coronavirus is affected by the spread of related misinformation—the so-called COVID-19 Infodemic—that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here, we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the prevalence of popular low-credibility sources and suspicious videos. A minority of accounts and pages exert a strong influence on each platform. These misinformation “superspreaders” are often associated with the low-credibility sources and tend to be verified by the platforms. On both platforms, there is evidence of coordinated sharing of Infodemic content. The overt nature of this manipulation points to the need for societal-level solutions in addition to mitigation strategies within the platforms. However, we highlight limits imposed by inconsistent data-access policies on our capability to study harmful manipulations of information ecosystems.
A contribution to condition numbers of the multidimensional total least squares problem with linear equality constraint

This paper is devoted to condition numbers of the multidimensional total least squares problem with linear equality constraint (TLSE). Based on the perturbation theory of invariant subspace, the TLSE problem is proved to be equivalent to a multidimensional unconstrained weighed total least squares problem in the limit sense. With a limit technique, Kronecker-product-based formulae for normwise, mixed and componentwise condition numbers of the minimum Frobenius norm TLSE solution are given. Compact upper bounds of these condition numbers are provided to reduce the storage and computation cost. All expressions and upper bounds of these condition numbers unify the ones for the single-dimensional TLSE problem and multidimensional total least squares problem. Some numerical experiments are performed to illustrate our results.
Explicit RKF-Compact Scheme for Pricing Regime Switching American Options with Varying Time Step

In this research work, an explicit Runge-Kutta-Fehlberg time integration with a fourth-order compact finite difference scheme in space is employed for solving the regime-switching pricing model. First, we recast the free boundary problem into a system of nonlinear partial differential equations with a multi-fixed domain. We further introduce a transformation based on the square root function with a fixed free boundary from which a high order analytical approximation is obtained for computing the derivative of the optimal exercise boundary in each regime. The high order analytical approximation is achieved by the method of extrapolation. As such, it enables us to employ fourth-order spatial discretization and an adaptive time integration with Dirichlet boundary conditions for obtaining the numerical solution of the asset option, option Greeks, and the optimal exercise boundary for each regime. In the set of equations, Hermite interpolation with Newton basis is used to estimate the coupled assets options and option Greeks. A numerical experiment is carried out with two- and four-regimes examples and results are compared with the existing methods. The results obtained from the numerical experiment show that the present method provides better performance in terms of computational speed and more accurate solutions with a large step size.
Optimizing Noisy-Intermediate Scale Quantum Circuits: A Block-Based Synthesis

The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block optimization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the final circuit by stitching all the blocks together. We perform our analysis and show the fidelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noise simulations, and large-size circuits on analytical models. Using a set of NISQ benchmarks, we show that QGo can reduce the number of CNOT gates by 29.9% on average and up to 50% when compared with industrial compilers such as t|ket>. When executed on the IBM Athens system, shorter depth leads to higher circuit fidelity. We also demonstrate the scalability of our QGo technique to optimize circuits of 60+ qubits. Our technique is the first demonstration of successfully employing and scaling synthesis in the compilation toolchain for large circuits. Overall, our approach is robust for direct incorporation in production compiler toolchains.
Benchmarking Automatic Detection of Psycholinguistic Characteristics for Better Human-Computer Interaction

When two people pay attention to each other and are interested in what the other has to say or write, they almost instantly adapt their writing/speaking style to match the other. For a successful interaction with a user, chatbots and dialog systems should be able to do the same. We propose framework consisting of five psycholinguistic textual characteristics for better human-computer interaction. We describe annotation processes for collecting the data, and benchmark five binary classification tasks, experimenting with different training sizes and model architectures. We perform experiments in English, Spanish, German, Chinese, and Arabic. The best architectures noticeably outperform several baselines and achieve macro-averaged F1-scores between 72% and 96% depending on the language and the task. Similar results are achieved even with a small amount of training data. The proposed framework proved to be fairly easy to model for various languages even with small amount of manually annotated data if right architectures are used. At the same time, it showed potential for improving user satisfaction if applied in existing commercial chatbots.
Breathing mode in two-dimensional binary self-bound Bose-gas droplets

In this work, we present the study of the stationary structures and the breathing mode behavior of a two-dimensional self-bound binary Bose droplet. We employ an analytical approach using a variational ansatz with a super-Gaussian trial order parameter and compare it with the numerical solutions of the extended Gross-Pitaevskii equation. We find that the super-Gaussian is superior to the often used Gaussian ansatz in describing the stationary and dynamical properties of the system. We find that for sufficiently large non-rotating droplets the breathing mode is energetically favourable compared to the self-evaporating process. For small self-bound systems our results differ based on the ansatz. Inducing angular momentum by imprinting multiply quantized vortices at the droplet center, this preference for the breathing mode persists independent of the norm.
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks usually utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, limited representations of given spatial graph structure with incomplete adjacent connections may restrict effective spatial-temporal dependencies learning of those models. Furthermore, existing methods were out at elbows when solving complicated spatial-temporal data: they usually utilize separate modules for spatial and temporal correlations, or they only use independent components capturing localized or global heterogeneous dependencies. To overcome those limitations, our paper proposes a novel Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. First, a data-driven method of generating “temporal graph” is proposed to compensate several genuine correlations that spatial graph may not reflect. STFGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, treated for different time periods in parallel. Meanwhile, by integrating this fusion graph module and a novel gated convolution module into a unified layer parallelly, STFGNN could handle long sequences by learning more spatial-temporal dependencies with layers stacked. Experimental results on several public traffic datasets demonstrate that our method achieves state-of-the-art performance consistently than other baselines.
A Contrast Synthesized Thalamic Nuclei Segmentation Scheme using Convolutional Neural Networks

Thalamic nuclei have been implicated in several neurological diseases. WMn-MPRAGE images have been shown to provide better intra-thalamic nuclear contrast compared to conventional MPRAGE images but the additional acquisition results in increased examination times. In this work, we investigated 3D Convolutional Neural Network (CNN) based techniques for thalamic nuclei parcellation from conventional MPRAGE images. Two 3D CNNs were developed and compared for thalamic nuclei parcellation using MPRAGE images: a) a native contrast segmentation (NCS) and b) a synthesized contrast segmentation (SCS) using WMn-MPRAGE images synthesized from MPRAGE images. We trained the two segmentation frameworks using MPRAGE images (n=35) and thalamic nuclei labels generated on WMn-MPRAGE images using a multi-atlas based parcellation technique. The segmentation accuracy and clinical utility were evaluated on a cohort comprising of healthy subjects and patients with alcohol use disorder (AUD) (n=45). The SCS network yielded higher Dice scores in the Medial geniculate nucleus (P=.003) and Centromedian nucleus (P=.01) with lower volume differences for Ventral anterior (P=.001) and Ventral posterior lateral (P=.01) nuclei when compared to the NCS network. A Bland-Altman analysis revealed tighter limits of agreement with lower coefficient of variation between true volumes and those predicted by the SCS network. The SCS network demonstrated a significant atrophy in Ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P=0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS network showed spurious atrophy of the Ventral posterior lateral nucleus. CNN-based contrast synthesis prior to segmentation can provide fast and accurate thalamic nuclei segmentation from conventional MPRAGE images.
The Causal Learning of Retail Delinquency

This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to Theory

In this work, we report the practical and theoretical aspects of Explainable AI (XAI) identified in some fundamental literature. Although there is a vast body of work on representing the XAI backgrounds, most of the corpuses pinpoint a discrete direction of thoughts. Providing insights into literature in practice and theory concurrently is still a gap in this field. This is important as such connection facilitates a learning process for the early stage XAI researchers and give a bright stand for the experienced XAI scholars. Respectively, we first focus on the categories of black-box explanation and give a practical example. Later, we discuss how theoretically explanation has been grounded in the body of multidisciplinary fields. Finally, some directions of future works are presented.
CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models

While medical images such as computed tomography (CT) are stored in DICOM format in hospital PACS, it is still quite routine in many countries to print a film as a transferable medium for the purposes of self-storage and secondary consultation. Also, with the ubiquitousness of mobile phone cameras, it is quite common to take pictures of the CT films, which unfortunately suffer from geometric deformation and illumination variation. In this work, we study the problem of recovering a CT film, which marks the first attempt in the literature, to the best of our knowledge. We start with building a large-scale head CT film database CTFilm20K, consisting of approximately 20,000 pictures, using the widely used computer graphics software Blender. We also record all accompanying information related to the geometric deformation (such as 3D coordinate, depth, normal, and UV maps) and illumination variation (such as albedo map). Then we propose a deep framework to disentangle geometric deformation and illumination variation using the multiple maps extracted from the CT films to collaboratively guide the recovery process. Extensive experiments on simulated and real images demonstrate the superiority of our approach over the previous approaches. We plan to open source the simulated images and deep models for promoting the research on CT film recovery (https://anonymous.4open.science/r/e6b1f6e3-9b36-423f-a225-55b7d0b55523/).
Dynamical Formation Scenarios for GW190521 and Prospects for Decihertz Gravitational-wave Astronomy with GW190521-like Binaries

The gravitational-wave (GW) detection of GW190521 has provided new insights on the mass distribution of black holes and new constraints for astrophysical formation channels. With independent claims of GW190521 having significant premerger eccentricity, we investigate what this implies for GW190521-like binaries that form dynamically. The Laser Interferometer Space Antenna (LISA) will also be sensitive to GW190521-like binaries if they are circular from an isolated formation channel. However, GW190521-like binaries that form dynamically may skip the LISA band entirely. To this end, we simulate GW190521 analogs that dynamically form via post-Newtonian binary–single scattering. From these scattering experiments, we find that GW190521-like binaries may enter the LIGO-Virgo band with significant eccentricity as suggested by recent studies, though well below an eccentricity of e 10 Hz ≲ 0.7. Eccentric GW190521-like binaries further motivate the astrophysical science case for a decihertz GW observatory, such as the kilometer-scale version of the Midband Atomic Gravitational-wave Interferometric Sensor. We carry out a Fisher analysis to estimate how well the eccentricity of GW190521-like binaries can be constrained with such a decihertz detector. These eccentricity constraints would also provide additional insights into the possible environments that GW190521-like binaries form in.
Shape noise and dispersion in precision weak lensing

We analyse the first measurements from precision weak lensing (PWL): a new methodology for measuring individual galaxy-galaxy weak lensing through velocity information. Our goal is to understand the observed shear distribution from PWL, which is broader than can be explained by the statistical measurement errors. We identify two possible sources of scatter to explain the observed distribution: a shape noise term associated with the underlying assumption of circular stable rotation, and an astrophysical signal consistent with a log-normal dispersion around the stellar-to-halo mass relation (SHMR). We have modelled the observed distribution as the combination of these two factors and quantified their most likely values given our data. For the current sample, we measure an effective shape noise of $\sigma_\gamma = 0.024 \pm 0.007$, highlighting the low noise impact of the method and positioning PWL as $\sim 10$ times more precise than conventional weak lensing. We also measure an average dispersion in shears of $\xi_\gamma = 0.53^{+0.26}_{-0.28}$\,dex over the range of $8.5 < \log M_\star < 11$. This measurement is higher than expected, which is suggestive of a relatively high dispersion in halo mass and/or profile.
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike. Prior attempts on the challenging Semantic Scene Completion task - which entails the inference of dense 3D structure and associated semantic labels from "sparse" representations - have been, to a degree, successful in small indoor scenes when provided with dense point clouds or dense depth maps often fused with semantic segmentation maps from RGB images. However, the performance of these systems drop drastically when applied to large outdoor scenes characterized by dynamic and exponentially sparser conditions. Likewise, processing of the entire sparse volume becomes infeasible due to memory limitations and workarounds introduce computational inefficiency as practitioners are forced to divide the overall volume into multiple equal segments and infer on each individually, rendering real-time performance impossible. In this work, we formulate a method that subsumes the sparsity of large-scale environments and present S3CNet, a sparse convolution based neural network that predicts the semantically completed scene from a single, unified LiDAR point cloud. We show that our proposed method outperforms all counterparts on the 3D task, achieving state-of-the art results on the SemanticKITTI benchmark. Furthermore, we propose a 2D variant of S3CNet with a multi-view fusion strategy to complement our 3D network, providing robustness to occlusions and extreme sparsity in distant regions. We conduct experiments for the 2D semantic scene completion task and compare the results of our sparse 2D network against several leading LiDAR segmentation models adapted for bird's eye view segmentation on two open-source datasets.