Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Temporal Graph Convolution with Graph Structure Self-Learning for Early MCI Detection

Graph neural networks (GNNs) have been successfully applied to early mild cognitive impairment (EMCI) detection, with the usage of elaborately designed features constructed from blood oxygen level-dependent (BOLD) time series. However, few works explored the feasibility of using BOLD signals directly as features. Meanwhile, existing GNN-based methods primarily rely on hand-crafted explicit brain topology as the adjacency matrix, which is not optimal and ignores the implicit topological organization of the brain. In this paper, we propose a spatial temporal graph convolutional network with a novel graph structure self-learning mechanism for EMCI detection. The proposed spatial temporal graph convolution block directly exploits BOLD time series as input features, which provides an interesting view for rsfMRI-based preclinical AD diagnosis. Moreover, our model can adaptively learn the optimal topological structure and refine edge weights with the graph structure self-learning mechanism. Results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database show that our method outperforms state-of-the-art approaches. Biomarkers consistent with previous studies can be extracted from the model, proving the reliable interpretability of our method.
Emergency action termination for immediate reaction in hierarchical reinforcement learning

Hierarchical decomposition of control is unavoidable in large dynamical systems. In reinforcement learning (RL), it is usually solved with subgoals defined at higher policy levels and achieved at lower policy levels. Reaching these goals can take a substantial amount of time, during which it is not verified whether they are still worth pursuing. However, due to the randomness of the environment, these goals may become obsolete. In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level. If the actions, i.e. lower level goals, become inadequate, they are replaced by more appropriate ones. This way we combine the advantages of hierarchical RL, which is fast training, and flat RL, which is immediate reactivity. We study our approach experimentally on seven benchmark environments.
Constraints on dark matter-electron scattering from molecular cloud ionization

We demonstrate that ionization of $\text{H}_2$ by dark matter in dense molecular clouds can provide strong constraints on the scattering strength of dark matter with electrons. Molecular clouds have high UV-optical attenuation, shielding them from ultraviolet and X-ray photons. Their chemical and thermal evolution are governed by low-energy cosmic rays. Dark matter with mass $\gtrsim 4$ MeV can ionize $\text{H}_2$, contributing to the observed ionization rate. We require that the dark matter-induced ionization rate of $\text{H}_2$ not exceed the observed cosmic ray ionization rate, $\zeta^{\text{H}_2}$, in diffuse molecular clouds as well as dense molecular clouds such as L1551 in the Taurus cloud complex. This allow us to place strong constraints on the DM-electron cross section, $\bar{\sigma}_e$, that complement existing astrophysical constraints and probe the strongly interacting parameter space where terrestrial and underground direct detection experiments lose sensitivity. We show that constraints from molecular clouds combined with planned balloon and satellite-based experiments would strongly constrain the fractional abundance of dark matter that interacts strongly with electrons. We comment on future modeling and observational efforts that may improve our bounds.
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning

In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-dimensional inputs. However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their adoption, particularly in critical applications where the decisions an agent makes can have significant consequences. Yet, existing RL-based systems are essentially competency-unaware in that they lack the necessary interpretation mechanisms to allow human operators to have an insightful, holistic view of their competency. In this paper, we extend a recently-proposed framework for explainable RL that is based on analyses of"interestingness. "Our new framework provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms. We also propose novel mechanisms for assessing RL agents' competencies that: 1) identify agent behavior patterns and competency-controlling conditions by clustering agent behavior traces solely using interestingness data; and 2) identify the task elements mostly responsible for an agent's behavior, as measured through interestingness, by performing global and local analyses using SHAP values. Overall, our tools provide insights about RL agent competence, both their capabilities and limitations, enabling users to make more informed decisions about interventions, additional training, and other interactions in collaborative human-machine settings.
Cross-Kerr-effect induced by coupled Josephson qubits in circuit quantum electrodynamics

We propose a scheme for implementing cross-Kerr nonlinearity between two superconducting transmission line resonators (TLRs) via their interactions with a coupler constructed by two superconducting transmon qubits connected to each other through a superconducting quantum interference device. When suitably driven, the coupler can induce very strong cross phase modulation (XPM) between the two TLRs due to its $N$-type level structure and the consequent electromagnetically induced transparency in its lowest states. The flexibility of our design can lead to various inter-TLR coupling configurations. The obtained cross-Kerr coefficient is large enough to allow many important quantum operations in which only few photons are involved. We further show that this scheme is very robust against fluctuations in solid-state quantum circuits. Our numerical calculations imply that the absorption and the dispersion of the TLRs resulting from the decoherence of the coupler are very small compared with the proposed XPM strength.
StrokeGAN+: Few-Shot Semi-Supervised Chinese Font Generation with Stroke Encoding

The generation of Chinese fonts has a wide range of applications. The currently predominated methods are mainly based on deep generative models, especially the generative adversarial networks (GANs). However, existing GAN-based models usually suffer from the well-known mode collapse problem. When mode collapse happens, the kind of GAN-based models will be failure to yield the correct fonts. To address this issue, we introduce a one-bit stroke encoding and a few-shot semi-supervised scheme (i.e., using a few paired data as semi-supervised information) to explore the local and global structure information of Chinese characters respectively, motivated by the intuition that strokes and characters directly embody certain local and global modes of Chinese characters. Based on these ideas, this paper proposes an effective model called \textit{StrokeGAN+}, which incorporates the stroke encoding and the few-shot semi-supervised scheme into the CycleGAN model. The effectiveness of the proposed model is demonstrated by amounts of experiments. Experimental results show that the mode collapse issue can be effectively alleviated by the introduced one-bit stroke encoding and few-shot semi-supervised training scheme, and that the proposed model outperforms the state-of-the-art models in fourteen font generation tasks in terms of four important evaluation metrics and the quality of generated characters. Besides CycleGAN, we also show that the proposed idea can be adapted to other existing models to improve their performance. The effectiveness of the proposed model for the zero-shot traditional Chinese font generation is also evaluated in this paper.
The dynamical Franz-Keldysh effect in the deep ultraviolet probed by transient absorption and dispersion of diamond using a miniature beamline

The deep ultraviolet, the bandgap region of dielectrics, is not readily accessible for established methods of ultrafast spectroscopy. Here, a miniature beamline, where a tailored deep ultraviolet field is used immediately after the noncollinear generation without subsequent optical elements, is introduced for transient absorption and dispersion spectroscopy. The near-bandgap region of diamond in the presence of a few-femtosecond pump pulse is explored where the delayed dynamical Franz-Keldysh effect and the almost instantaneous optical Kerr effect coexist.
Principles for generation of reverberation

In modern neuroscience, memory has been postulated to stored in neural circuits as sequential spike train and Reverberation is one of the specific example.Former research has made much progress on phenomenon description. However, the mechanism of reverberation has been unclear yet. In this study, combining electrophysiological record and numerical simulation, we confirmed a formerly unrealized neuron property that is necessary for the burst generation in reverberation. Secondly, we find out the mechanism of sequential pattern generation which clearly explained by network topology and asynchronous neurotransmitter release. In addition, we also developed a pipeline that could design the network fire in manually set order. Thirdly, we explored the dynamics of STDP learning and chased down the effects of STDP Rule in reverberation. With these understandings, we developed a STDP based learning rule which could drive the network to remember any presupposed sequence. These results indicated that neuron circuit can remember malformation through STDP rule. Those information are stored in synapse connections. By this way, animals remember information as spike sequence pattern.
A Faster Small Treewidth SDP Solver

Semidefinite programming is a fundamental tool in optimization and theoretical computer science. It has been extensively used as a black-box for solving many problems, such as embedding, complexity, learning, and discrepancy. One natural setting of semidefinite programming is the small treewidth setting. The best previous SDP solver under small treewidth setting is due to [ZL18], which takes n1.5 · τ6. 5 time. In this work, we show how to solve a semidefinite programming with n× n variables, m constraints and τ treewidth in n · τ2ω+1/2 time, where ω denotes the exponent of matrix multiplication. We give the first SDP solver that runs in time in linear in number of variables under this setting. In addition, we improve the running time that solves a linear programming with τ treewidth from nτ2 [Ye20, DLY21] to nτ (ω+1)/2.
Overparameterized random feature regression with nearly orthogonal data

We investigate the properties of random feature ridge regression (RFRR) given by a two-layer neural network with random Gaussian initialization. We study the non-asymptotic behaviors of the RFRR with nearly orthogonal deterministic unit-length input data vectors in the overparameterized regime, where the width of the first layer is much larger than the sample size. Our analysis shows high-probability non-asymptotic concentration results for the training errors, cross-validations, and generalization errors of RFRR centered around their respective values for a kernel ridge regression (KRR). This KRR is derived from an expected kernel generated by a nonlinear random feature map. We then approximate the performance of the KRR by a polynomial kernel matrix obtained from the Hermite polynomial expansion of the activation function, whose degree only depends on the orthogonality among different data points. This polynomial kernel determines the asymptotic behavior of the RFRR and the KRR. Our results hold for a wide variety of activation functions and input data sets that exhibit nearly orthogonal properties. Based on these approximations, we obtain a lower bound for the generalization error of the RFRR for a nonlinear student-teacher model.
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression

Increasingly high-stakes decisions are made using neural networks in order to make predictions. Specifically, meteorologists and hedge funds apply these techniques to time series data. When it comes to prediction, there are certain limitations for machine learning models (such as lack of expressiveness, vulnerability of domain shifts and overconfidence) which can be solved using uncertainty estimation. There is a set of expectations regarding how uncertainty should ``behave". For instance, a wider prediction horizon should lead to more uncertainty or the model's confidence should be proportional to its accuracy. In this paper, different uncertainty estimation methods are compared to forecast meteorological time series data and evaluate these expectations. The results show how each uncertainty estimation method performs on the forecasting task, which partially evaluates the robustness of predicted uncertainty.
Polarization changes of pulsars due to wave propagation through magnetospheres

We study the propagation effects of radio waves in a pulsar magnetosphere, composed of relativistic electron-positron pair plasmas streaming along the magnetic field lines and corotating with the pulsar. We critically examine the various physical effects that can potentially influence the observed wave intensity and polarization, including resonant cyclotron absorption, wave mode coupling due to pulsar rotation, wave propagation through quasi-tangential regions (where the photon ray is nearly parallel to the magnetic field) and mode circularization due to the difference in the electron/positron density/velocity distributions. We numerically integrate the transfer equations for wave polarization in the rotating magnetosphere, taking account of all the propagation effects in a self-consistent manner. For typical magnetospheric plasma parameters produced by pair cascade, we find that the observed radio intensity and polarization profiles can be strongly modified by the propagation effects. For a relatively large impact parameter (the minimum angle between the magnetic dipole axis and the line of sight), the polarization angle profile is similar to the prediction from the Rotating Vector Model, except for a phase shift and an appreciable circular polarization. For a smaller impact parameter, the linear polarization position angle may exhibit a sudden 90° jump due to the quasi-tangential propagation effect, accompanied by a complex circular polarization profile. Some applications of our results are discussed, including the origin of non-Gaussian pulse profiles, the relationship between the position angle profile and circular polarization in conal-double pulsars, and the orthogonal polarization modes.
A Federated Approach to Predicting Emojis in Hindi Tweets

The use of emojis affords a visual modality to, often private, textual communication.The task of predicting emojis however provides a challenge for machine learning as emoji use tends to cluster into the frequently used and the rarely used emojis.Much of the machine learning research on emoji use has focused on high resource languages and has conceptualised the task of predicting emojis around traditional server-side machine learning approaches.However, traditional machine learning approaches for private communication can introduce privacy concerns, as these approaches require all data to be transmitted to a central storage.In this paper, we seek to address the dual concerns of emphasising high resource languages for emoji prediction and risking the privacy of people’s data.We introduce a new dataset of 118k tweets (augmented from 25k unique tweets) for emoji prediction in Hindi, and propose a modification to the federated learning algorithm, CausalFedGSD, which aims to strike a balance between model performance and user privacy. We show that our approach obtains comparative scores with more complex centralised models while reducing the amount of data required to optimise the models and minimising risks to user privacy.
FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection

Due to its importance in facial behaviour analysis, facial action unit (AU) detection has attracted increasing attention from the research community. Leveraging the online knowledge distillation framework, we propose the "FAN-Trans" method for AU detection. Our model consists of a hybrid network of convolution and transformer blocks to learn per-AU features and to model AU co-occurrences. The model uses a pre-trained face alignment network as the feature extractor. After further transformation by a small learnable add-on convolutional subnet, the per-AU features are fed into transformer blocks to enhance their representation. As multiple AUs often appear together, we propose a learnable attention drop mechanism in the transformer block to learn the correlation between the features for different AUs. We also design a classifier that predicts AU presence by considering all AUs’ features, to explicitly capture label dependencies. Finally, we make the attempt of adapting online knowledge distillation in the training stage for this task, further improving the model’s performance. Experiments on the BP4D and DISFA datasets demonstrating the effectiveness of proposed method.
Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities

Connecting observations of core-collapse supernova explosions to the properties of their massive star progenitors is a long-sought, and challenging, goal of supernova science. Recently, Barker et al. presented bolometric light curves for a landscape of progenitors from spherically symmetric neutrino-driven core-collapse supernova (CCSN) simulations using an effective model. They find a tight relationship between the plateau luminosity of the Type II-P CCSN light curve and the terminal iron-core mass of the progenitor. Remarkably, this allows us to constrain progenitor properties with photometry alone. We analyze a large observational sample of Type II-P CCSN light curves and estimate a distribution of iron-core masses using the relationship of Barker et al. The inferred distribution matches extremely well with the distribution of iron-core masses from stellar evolutionary models and namely, contains high-mass iron cores that suggest contributions from very massive progenitors in the observational data. We use this distribution of iron-core masses to infer minimum and maximum masses of progenitors in the observational data. Using Bayesian inference methods to locate optimal initial mass function parameters, we find Mmin=9.8−0.27+0.37 and Mmax=24.0−1.9+3.9 solar masses for the observational data.
Fair Curing and Network Design in SIS Epidemic Processes

This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee the fast extinction of epidemic spread in a susceptible-infected-susceptible (SIS) model. We consider a Markov process-based SIS epidemic model. We call a curing policy fair if demographic groups are cured with comparable speeds. We propose a fair curing policy based on the curing policy of Drakopoulos et al. Since these optimization problems are NP-hard, finding optimal policies is intractable for large graphs. We provide approximation guarantees on both curing and fair curing policies. Moreover, when the total infection rate is large, the original curing policy includes a waiting period in which no measures are taken to mitigate the spread until the rate slows down. To avoid the waiting period, we study network design problems to reduce the total infection rate by deleting edges or reducing the weight of edges. Then the curing processes become continuous since the total infection rate is restricted by network design. We provide algorithms with provable guarantees for the considered network design problems. In summary, the proposed fair curing and network design algorithms together provide an effective, fair, and computationally efficient approach that mitigates SIS epidemic spread in networks.
Helping the Weak Makes You Strong: Simple Multi-Task Learning Improves Non-Autoregressive Translators

Recently, non-autoregressive (NAR) neural machine translation models have received increasing attention due to their efficient parallel decoding.However, the probabilistic framework of NAR models necessitates conditional independence assumption on target sequences, falling short of characterizing human language data.This drawback results in less informative learning signals for NAR models under conventional MLE training, thereby yielding unsatisfactory accuracy compared to their autoregressive (AR) counterparts.In this paper, we propose a simple and model-agnostic multi-task learning framework to provide more informative learning signals.During training stage, we introduce a set of sufficiently weak AR decoders that solely rely on the information provided by NAR decoder to make prediction, forcing the NAR decoder to become stronger or else it will be unable to support its weak AR partners.Experiments on WMT and IWSLT datasets show that our approach can consistently improve accuracy of multiple NAR baselines without adding any additional decoding overhead.
On the lithium abundance of the visual binary components
 ξ
 Boo A (
 G8V
 ) and
 ξ
 Boo B (
 K5V
 )

A spectroscopic investigation of the lithium resonance doublet in $\xi$ Boo A and $\xi$ Boo B in terms of both abundance and isotopic ratio is presented. We obtained new $R$=130\,000 spectra with a signal-to-noise ratio (S/N) per pixel of up to 3200 using the 11.8m LBT and PEPSI. From fits with synthetic line profiles based on 1D-LTE MARCS model atmospheres and 3D-NLTE corrections, we determine the abundances of both isotopes. For $\xi$ Boo A, we find A(Li) = 2.40$\pm$0.03\,dex and $^6$Li/$^7$Li<1.5$\pm$1.0\,\%\ in 1D-LTE, which increases to $\approx$2.45 for the 3D-NLTE case. For $\xi$ Boo B we obtain A(Li) = 0.37$\pm$0.09\,dex in 1D-LTE with an unspecified $^6$Li/$^7$Li level. Therefore, no $^6$Li is seen on any of the two stars. We consider a spot model for the Li fit for $\xi$ Boo B and find A(Li) = 0.45$\pm$0.09\,dex. The $^7$Li abundance is 23 times higher for $\xi$ Boo A than the Sun's, but three times lower than the Sun's for $\xi$ Boo B while both fit the trend of single stars in the similar-aged M35 open cluster. Effective temperatures are redetermined from the TiO band head strength. We note that the best-fit global metallicities are --0.13$\pm$0.01\,dex for $\xi$ Boo A but +0.13$\pm$0.02\,dex for $\xi$ Boo B. Lithium abundance for the K5V benchmark star 61\,Cyg\,A was obtained to A(Li)$\approx$0.53\,dex when including a spot model but to $\approx$0.15\,dex without a spot model.