Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal Interaction between the Subsurface Rotation Rate Residuals and Radial Magnetic Field in Different Timescales

We studied the presence and spatiotemporal characteristics and evolution of the variations in the differential rotation rates and radial magnetic fields in the Schwabe and quasi-biennial-oscillation (QBO) timescales. To achieve these objectives, we used rotation rate residuals and radial magnetic field data from the Michelson Doppler Imager on the Solar and Heliospheric Observatory and the Helioseismic and Magnetic Imager on the Solar Dynamics Observatory, extending from 1996 May to 2020 August, covering solar cycles 23 and 24, respectively. Under the assumption that the radial surface magnetic field is nonlocal and the differential rotation is symmetric around the equator, our results suggest that the source region of the Schwabe cycle is confined between ∼30° N and S throughout the convection zone. As for the source region of the QBO, our results suggest that it is below 0.78 R ⊙.
Improving Speech Recognition on Noisy Speech via Speech Enhancement with Multi-Discriminators CycleGAN

This paper presents our latest investigations on improving automatic speech recognition for noisy speech via speech enhancement. We propose a novel method named Multi-discriminators CycleGAN to reduce noise of input speech and therefore improve the automatic speech recognition performance. Our proposed method leverages the CycleGAN framework for speech enhancement without any parallel data and improve it by introducing multiple discriminators that check different frequency areas. Furthermore, we show that training multiple generators on homogeneous subset of the training data is better than training one generator on all the training data. We evaluate our method on CHiME-3 data set and observe up to 10.03 % relatively WER improvement on the development set and up to 14.09 % on the evaluation set.
Neural network acceleration of large-scale structure theory calculations

We make use of neural networks to accelerate the calculation of power spectra required for the analysis of galaxy clustering and weak gravitational lensing data. For modern perturbation theory codes, evaluation time for a single cosmology and redshift can take on the order of two seconds. In combination with the comparable time required to compute linear predictions using a Boltzmann solver, these calculations are the bottleneck for many contemporary large-scale structure analyses. In this work, we construct neural network-based surrogate models for Lagrangian perturbation theory (LPT) predictions of matter power spectra, real and redshift space galaxy power spectra, and galaxy-matter cross power spectra that attain ∼ 0.1% (at one sigma) accuracy over a broad range of scales in a ωCDM parameter space. The neural network surrogates can be evaluated in approximately one millisecond, a factor of 1000 times faster than the full Boltzmann code and LPT computations. In a simulated full-shape redshift space galaxy power spectrum analysis, we demonstrate that the posteriors obtained using our surrogates are accurate compared to those obtained using the full LPT model. We make our surrogate models public at https://github.com/sfschen/EmulateLSS, so that others may take advantage of the speed gains they provide to enable rapid iteration on analysis settings, something that is essential in complex contemporary large-scale structure analyses.
Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution

—The video super-resolution (VSR) method based on the recurrent convolutional network has strong temporal modeling capability for video sequences. However, the temporal receptive ﬁeld of di ﬀ erent recurrent units in the unidirectional recurrent network is unbalanced. Earlier reconstruction frames receive less spatio-temporal information, resulting in fuzziness or artifacts. Although the bidirectional recurrent network can alleviate this problem, it requires more memory space and fails to perform many tasks with low latency requirements. To solve the above problems, we propose an end-to-end information prebuilt recurrent reconstruction network (IPRRN), consisting of an information prebuilt network (IPNet) and a recurrent reconstruction network (RRNet). By integrating su ﬃ cient information from the front of the video to build the hidden state needed for the initially recurrent unit to help restore the earlier frames, the information prebuilt network balances the input information di ﬀ erence at di ﬀ erent time steps. In addition, we demonstrate an e ﬃ cient recurrent reconstruction network, which outperforms the existing unidirectional recurrent schemes in all aspects. Many experiments have veriﬁed the e ﬀ ectiveness of the network we propose, which can e ﬀ ectively achieve better quantitative and qualitative evaluation performance compared to the existing state-of-the-art methods.
Contextual Exploration Using a Linear Approximation Method Based on Satisficing

Deep reinforcement learning has enabled human-level or even super-human performance in various types of games. However, the amount of exploration required for learning is often quite large. Deep reinforcement learning also has super-human performance in that no human being would be able to achieve such amounts of exploration. To address this problem, we focus on the \textit{satisficing} policy, which is a qualitatively different approach from that of existing optimization algorithms. Thus, we propose Linear RS (LinRS), which is a type of satisficing algorithm and a linear extension of risk-sensitive satisficing (RS), for application to a wider range of tasks. The generalization of RS provides an algorithm to reduce the volume of exploratory actions by adopting a different approach from existing optimization algorithms. LinRS utilizes linear regression and multiclass classification to linearly approximate both the action value and proportion of action selections required in the RS calculation. The results of our experiments indicate that LinRS reduced the number of explorations and run time compared to those of existing algorithms in contextual bandit problems. These results suggest that a further generalization of satisficing algorithms may be useful for complex environments, including those that are to be handled with deep reinforcement learning.
Weakly Supervised Mapping of Natural Language to SQL through Question Decomposition

Natural Language Interfaces to Databases (NLIDBs), where users pose queries in Natural Language (NL), are crucial for enabling non-experts to gain insights from data. Developing such interfaces, by contrast, is dependent on experts who often code heuristics for mapping NL to SQL. Alternatively, NLIDBs based on machine learning models rely on supervised examples of NL to SQL mappings (NL-SQL pairs) used as training data. Such examples are again procured using experts, which typically involves more than a one-off interaction. Namely, each data domain in which the NLIDB is deployed may have different characteristics and therefore require either dedicated heuristics or domain-specific training examples. To this end, we propose an alternative approach for training machine learning-based NLIDBs, using weak supervision . We use the recently proposed question decomposition representation called QDMR, an intermediate between NL and formal query languages. Recent work has shown that non-experts are generally successful in translating NL to QDMR. We consequently use NL-QDMR pairs, along with the question answers, as supervision for automatically synthesizing SQL queries. The NL questions and synthesized SQL are then used to train NL-to-SQL models, which we test on five benchmark datasets. Extensive experiments show that our solution, requiring zero expert annotations, performs competitively with models trained on expert annotated data.
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward

Discovering hazardous scenarios is crucial in testing and further improving driving policies. However, conducting efficient driving policy testing faces two key challenges. On the one hand, the probability of naturally encountering hazardous scenarios is low when testing a well-trained autonomous driving strategy. Thus, discovering these scenarios by purely real-world road testing is extremely costly. On the other hand, a proper determination of accident responsibility is necessary for this task. Collecting scenarios with wrong-attributed responsibilities will lead to an overly conservative autonomous driving strategy. To be more specific, we aim to discover hazardous scenarios that are autonomous-vehicle responsible (AV-responsible), i.e., the vulnerabilities of the under-test driving policy. To this end, this work proposes a Safety Test framework by finding Av-Responsible Scenarios (STARS) based on multi-agent reinforcement learning. STARS guides other traffic participants to produce Av-Responsible Scenarios and make the under-test driving policy misbehave via introducing Hazard Arbitration Reward (HAR). HAR enables our framework to discover diverse, complex, and AV-responsible hazardous scenarios. Experimental results against four different driving policies in three environments demonstrate that STARS can effectively discover AV-responsible hazardous scenarios. These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
Probability Density Estimation Based Imitation Learning

Imitation Learning (IL) is an effective learning paradigm exploiting the interactions between agents and environments. It does not require explicit reward signals and instead tries to recover desired policies using expert demonstrations. In general, IL methods can be categorized into Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL). In this work, a novel reward function based on probability density estimation is proposed for IRL, which can significantly reduce the complexity of existing IRL methods. Furthermore, we prove that the theoretically optimal policy derived from our reward function is identical to the expert policy as long as it is deterministic. Consequently, an IRL problem can be gracefully transformed into a probability density estimation problem. Based on the proposed reward function, we present a"watch-try-learn"style framework named Probability Density Estimation based Imitation Learning (PDEIL), which can work in both discrete and continuous action spaces. Finally, comprehensive experiments in the Gym environment show that PDEIL is much more efficient than existing algorithms in recovering rewards close to the ground truth.
An Extension of the Lindström-Gessel-Viennot Theorem

Consider a weighted directed acyclic graph $G$ having an upward planar drawing. We give a formula for the total weight of the families of non-intersecting paths on $G$ with any given starting and ending points. While the Lindström-Gessel-Viennot theorem gives the signed enumeration of these weights (according to the connection type), our result provides the straight count, expressing it as a determinant whose entries are signed counts of lattice paths with given starting and ending points.
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals

The quantitative analysis of non-invasive electrophysiology signals from electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the identification of temporal patterns such as evoked responses, transient bursts of neural oscillations but also blinks or heartbeats for data cleaning. Several works have shown that these patterns can be extracted efficiently in an unsupervised way, e.g., using Convolutional Dictionary Learning. This leads to an event-based description of the data. Given these events, a natural question is to estimate how their occurrences are modulated by certain cognitive tasks and experimental manipulations. To address it, we propose a point process approach. While point processes have been used in neuroscience in the past, in particular for single cell recordings (spike trains), techniques such as Convolutional Dictionary Learning make them amenable to human studies based on EEG/MEG signals. We develop a novel statistical point process model-called driven temporal point processes (DriPP)-where the intensity function of the point process model is linked to a set of point processes corresponding to stimulation events. We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Simulations reveal that model parameters can be identified from long enough signals. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses-both evoked and induced-and isolates non-task specific temporal patterns.
Exceptional thermoelectric power factors in hyperdoped, fully dehydrogenated nanocrystalline silicon thin films

Single-crystalline silicon is well known to be a poor thermoelectric material due to its high thermal conductivity. Most excellent research has focused on ways to decrease its thermal conductivity while retaining acceptably large power factors (PFs). Less effort has been spent to enhance the PF in poly and nanocrystalline silicon, instead. Here we show that in boron-hyperdoped nanocrystalline thin films PF may be increased up to 33 mW K$^{-2}$m$^{-1}$ at 300 K when hydrogen embedded in the film during deposition is removed. The result makes nanocrystalline Si a realistic competitor of Bi$_2$Te$_3$ for low-temperature heat harvesting, also due to its greater geo-availability and lower cost.
Analysis of super-resolution single molecule localization microscopy data: A tutorial

The diffraction of light imposes a fundamental limit on the resolution of light microscopes. This limit can be circumvented by creating and exploiting independent behaviors of the sample at length scales below the diffraction limit. In super-resolution single molecule localization microscopy (SMLM), the independence arises from individual fluorescent labels stochastically switching between dark and fluorescent states, which in turn allows the pinpointing of fluorophores post experimentally using a sequence of acquired sparse image frames. Finally, the resulting list of fluorophore coordinates is utilized to produce high resolution images or to gain quantitative insight into the underlying biological structures. Therefore, image processing and post-processing are essential stages of SMLM. Here, we review the latest progress on SMLM data processing and post-processing. Author to whom correspondence should be addressed: mfazel@asu.edu
Surfer100: Generating Surveys From Web Resources, Wikipedia-style

Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely. As a result, methods for automatically producing content are valuable tools to address this information overload. We show that recent advances in pretrained language modeling can be combined for a two-stage extractive and abstractive approach for Wikipedia lead paragraph generation. We extend this approach to generate longer Wikipedia-style summaries with sections and examine how such methods struggle in this application through detailed studies with 100 reference human-collected surveys. This is the first study on utilizing web resources for long Wikipedia-style summaries to the best of our knowledge.
Process Tomography on a 7-Qubit Quantum Processor via Tensor Network Contraction Path Finding

Quantum process tomography (QPT), where a quantum channel is reconstructed through the analysis of repeated quantum measurements, is an important tool for validating the operation of a quantum processor. We detail the combined use of an existing QPT approach based on tensor networks (TNs) and unsupervised learning with TN contraction path finding algorithms in order to use TNs of arbitrary topologies for reconstruction. Experiments were conducted on the 7-qubit IBM Quantum Falcon Processor ibmq casablanca, where we demonstrate this technique by matching the topology of the tensor networks used for reconstruction with the topology of the processor, allowing us to extend past the characterisation of linear nearest neighbour circuits. Furthermore, we conduct single-qubit gate set tomography (GST) on each individual qubit to correct for separable errors during the state preparation and measurement phases of QPT, which are separate from the channel under consideration but may negatively impact the quality of its reconstruction. We are able to report a fidelity of 0.89 against the ideal unitary channel of a single-cycle random quantum circuit performed on ibmq casablanca, after obtaining just 1.1× 10 measurements for the reconstruction of this 7-qubit process. This represents more than five orders of magnitude fewer total measurements than the number needed to conduct full, traditional QPT on a 7-qubit process.
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training these large dense models requires significant amounts of computing resources. In this paper, we propose and develop a family of language models named GLaM (Generalist Language Model), which uses a sparsely activated mixture-of-experts architecture to scale the model capacity while also incurring substantially less training cost compared to dense variants. The largest GLaM has 1.2 trillion parameters, which is approximately 7x larger than GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half of the computation flops for inference, while still achieving better overall zero-shot and one-shot performance across 29 NLP tasks.
New Cosmological Solutions of a Nonlocal Gravity Model

A nonlocal gravity model (2) was introduced and considered recently, and two exact cosmological solutions in flat space were presented. The first solution is related to some radiation effects generated by nonlocal dynamics on dark energy background, while the second one is a nonsingular time symmetric bounce. In the present paper, we investigate other possible exact cosmological solutions and find some the new ones in nonflat space. Used nonlocal gravity dynamics can change the background topology. To solve the corresponding equations of motion, we first look for a solution of the eigenvalue problem □(R−4Λ)=q(R−4Λ). We also discuss possible extension of this model with a nonlocal operator, symmetric under □⟷□−1, and its connection with another interesting nonlocal gravity model.
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition

Recently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention. Most of the work utilizes image information through region-level visual representations obtained from a pretrained object detector and relies on an attention mechanism to model the interactions between image and text representations. However, it is difficult to model such interactions as image and text representations are trained separately on the data of their respective modality and are not aligned in the same space. As text representations take the most important role in MNER, in this paper, we propose Image-text Alignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized. ITA first aligns the image into regional object tags, image-level captions and optical characters as visual contexts, concatenates them with the input texts as a new cross-modal input, and then feeds it into a pretrained textual embedding model. This makes it easier for the attention module of a pretrained textual embedding model to model the interaction between the two modalities since they are both represented in the textual space. ITA further aligns the output distributions predicted from the cross-modal input and textual input views so that the MNER model can be more practical in dealing with text-only inputs and robust to noises from images. In our experiments, we show that ITA models can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets, even without image information.
Deep Q-Network with Proximal Iteration

We employ Proximal Iteration for value-function optimization in reinforcement learning. Proximal Iteration is a computationally efﬁcient technique that enables us to bias the optimization procedure towards more desirable solutions. As a concrete application of Proximal Iteration in deep reinforcement learning, we endow the objective function of the Deep Q-Network (DQN) agent with a proximal term to ensure that the online-network component of DQN remains in the vicinity of the target network. The resultant agent, which we call DQN with Proximal Iteration, or DQNPro, exhibits signiﬁcant improvements over the original DQN on the Atari benchmark. Our results accentuate the power of employing sound optimization techniques for deep reinforcement learning.
Recursive Construction of a Nash Equilibrium in a Two-Player Nonzero-Sum Stopping Game with Asymmetric Information

We study a discrete-time finite-horizon two-players nonzero-sum stopping game where the filtration of Player 1 is richer than the filtration of Player 2. A major difficulty which is caused by the information asymmetry is that Player 2 may not know whether Player 1 has already stopped the game or not. Furthermore, the classical backward-induction approach is not applicable in the current setup. This is because when the informed player decides not to stop, he reveals information to the uninformed player and hence the decision of the uninformed player at time $t$ may not be determined by the play after time $t$, but also on the play before time $t$. In the current work we initially show that the expected utility of Player 2 will remain the same even if he knows whether Player 1 has already stopped. Then, this result is applied in order to prove that, under appropriate conditions, a recursive construction in the style of Hamad\'ene and Zhang (2010) converges to a pure-strategy Nash equilibrium.
Early Stopping for Deep Image Prior

Deep image prior (DIP) and its variants have showed remarkable potential for solving inverse problems in computer vision, without any extra training data. Practical DIP models are often substantially overparameterized. During the fitting process, these models learn mostly the desired visual content first, and then pick up the potential modeling and observational noise, i.e., overfitting. Thus, the practicality of DIP often depends critically on good early stopping (ES) that captures the transition period. In this regard, the majority of DIP works for vision tasks only demonstrates the potential of the models -- reporting the peak performance against the ground truth, but provides no clue about how to operationally obtain near-peak performance without access to the groundtruth. In this paper, we set to break this practicality barrier of DIP, and propose an efficient ES strategy, which consistently detects near-peak performance across several vision tasks and DIP variants. Based on a simple measure of dispersion of consecutive DIP reconstructions, our ES method not only outpaces the existing ones -- which only work in very narrow domains, but also remains effective when combined with a number of methods that try to mitigate the overfitting. The code is available at https://github.com/sun-umn/Early_Stopping_for_DIP.