Miracle-dz/UniFAR-FTUs huggingface dataset:
Implications for Additional Plasma Heating Driving the Extreme-ultraviolet Late Phase of a Solar Flare with Microwave Imaging Spectroscopy

Extreme-ultraviolet late phase (ELP) refers to the second extreme-ultraviolet (EUV) radiation enhancement observed in certain solar flares, which usually occurs tens of minutes to several hours after the peak of soft X-ray emission. The coronal loop system that hosts the ELP emission is often different from the main flaring arcade, and the enhanced EUV emission therein may imply an additional heating process. However, the origin of the ELP remains rather unclear. Here we present the analysis of a C1.4 flare that features such an ELP, which is also observed in microwave wavelengths by the Expanded Owens Valley Solar Array. Similar to the case of the ELP, we find a gradual microwave enhancement that occurs about 3 minutes after the main impulsive phase microwave peaks. Radio sources coincide with both foot points of the ELP loops and spectral fits on the time-varying microwave spectra demonstrate a clear deviation of the electron distribution from the Maxwellian case, which could result from injected nonthermal electrons or nonuniform heating to the footpoint plasma. We further point out that the delayed microwave enhancement suggests the presence of an additional heating process, which could be responsible for the evaporation of heated plasma that fills the ELP loops, producing the prolonged ELP emission.
Improving negation detection with negation-focused pre-training

Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text. Recent works show that state-of-the-art NLP models underperform on samples containing negation in various tasks, and that negation detection models do not transfer well across domains. We propose a new negation-focused pre-training strategy, involving targeted data augmentation and negation masking, to better incorporate negation information into language models. Extensive experiments on common benchmarks show that our proposed approach improves negation detection performance and generalizability over the strong baseline NegBERT (Khandelwal and Sawant, 2020).
Empathetic Response Generation with State Management

The goal of empathetic response generation is to enhance the ability of dialogue systems to perceive and express emotions in conversations. Current approaches to this task mainly focus on improving the response generation model by recognizing the emotion of the user or predicting a target emotion to guide the generation of responses. such models only exploit partial information (the user’s emotion or the target emotion used as a guiding signal) and do not consider multiple information together. In addition to the emotional style of the response, the intent of the response is also very important for empathetic responding. Thus, we propose a novel empathetic response generation model that can consider multiple state information including emotions and intents simultaneously. Speciﬁcally, we introduce a state management method to dynamically up-date the dialogue states, in which the user’s emotion is ﬁrst recognized, then the target emotion and intent are obtained via predeﬁned shift patterns with the user’s emotion as input. The obtained information is used to control the response generation. Experimental results show that dynamically managing different information can help the model generate more empathetic responses compared with several baselines under both automatic and human evaluations. Our code will be available at https://github.com/A-Rain/State_ Management_EmpDialogue .
Quadratic symmetry algebras and spectrum of the 3D nondegenerate quantum superintegrable system

In this paper, we present the quadratic associative symmetry algebra of the 3D nondegenerate maximally quantum superintegrable system. This is the complete symmetry algebra of the system. It is demonstrated that the symmetry algebra contains suitable quadratic subalgebras, each of which is generated by three generators with relevant structure constants, which may depend on central elements. We construct corresponding Casimir operators and present ﬁnite-dimensional unirreps and structure functions via the realizations of these subalgebras in the context of deformed oscillators. By imposing constraints on the structure functions, we obtain the spectrum of the 3D nondegenerate superintegrable system. We also show that this model is multiseparable and admits separation of variables in cylindrical polar and paraboloidal coordinates. We derive the physical spectrum by solving the Schrödinger equation of the system and compare the result with those obtained from algebraic derivations.
Golden rhythms as a theoretical framework for cross-frequency organization.

While brain rhythms appear fundamental to brain function, why brain rhythms consistently organize into the small set of discrete frequency bands observed remains unknown. Here we propose that rhythms separated by factors of the golden ratio (ϕ=(1+5)/2)) optimally support segregation and cross-frequency integration of information transmission in the brain. Organized by the golden ratio, pairs of transient rhythms support multiplexing by reducing interference between separate communication channels, and triplets of transient rhythms support integration of signals to establish a hierarchy of cross-frequency interactions. We illustrate this framework in simulation and apply this framework to propose four hypotheses.
Clustered Graph Matching for Label Recovery and Graph Classification

Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We consider matching the shuffled network to averages of the networks in the vertex-aligned collection at different levels of granularity. We demonstrate both in theory and practice that if the graphs come from different network classes, then clustering the networks into classes followed by matching the new graph to cluster-averages can yield higher fidelity matching performance than matching to the global average graph. Moreover, by minimizing the graph matching objective function with respect to each cluster average, this approach simultaneously classifies and recovers the vertex labels for the shuffled graph. These theoretical developments are further reinforced via an illuminating real data experiment matching human connectomes.
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition

Improving the accuracy of single-channel automatic speech recognition (ASR) in noisy conditions is challenging. Strong speech enhancement front-ends are available, however, they typically require that the ASR model is retrained to cope with the processing artifacts. In this paper we explore a speaker reinforcement strategy for improving recognition performance without retraining the acoustic model (AM). This is achieved by remixing the enhanced signal with the unprocessed input to alleviate the processing artifacts. We evaluate the proposed approach using a DNN speaker extraction based speech denoiser trained with a perceptually motivated loss function. Results show that (without AM retraining) our method yields about 23% and 25% relative accuracy gains compared with the unprocessed for the monoaural simulated and real CHiME-4 evaluation sets, respectively, and outperforms a state-of-the-art reference method.
Single-Image 3D Face Reconstruction under Perspective Projection

In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process. This approximation performs well when the distance between camera and face is far enough. However, in some scenarios that the face is very close to camera or moving along the camera axis, the methods suffer from the inaccurate reconstruction and unstable temporal fitting due to the distortion under the perspective projection. In this paper, we aim to address the problem of single-image 3D face reconstruction under perspective projection. Specifically, a deep neural network, Perspective Network (PerspNet), is proposed to simultaneously reconstruct 3D face shape in canonical space and learn the correspondence between 2D pixels and 3D points, by which the 6DoF (6 Degrees of Freedom) face pose can be estimated to represent perspective projection. Besides, we contribute a large ARKitFace dataset to enable the training and evaluation of 3D face reconstruction solutions under the scenarios of perspective projection, which has 902,724 2D facial images with ground-truth 3D face mesh and annotated 6DoF pose parameters. Experimental results show that our approach outperforms current state-of-the-art methods by a significant margin.
Bubble rise in a Hele-Shaw cell: bridging the gap between viscous and inertial regimes

Abstract The rise of a single bubble confined between two vertical plates is investigated over a wide range of Reynolds numbers. In particular, we focus on the evolution of the bubble speed, aspect ratio and drag coefficient during the transition from the viscous to the inertial regime. For sufficiently large bubbles, a simple model based on power balance captures the transition for the bubble velocity and matches all the experimental data despite strong time variations of bubble aspect ratio at large Reynolds numbers. Surprisingly, bubbles in the viscous regime systematically exhibit an ellipse elongated along its direction of motion while bubbles in the inertia-dominated regime are always flattened perpendicularly to it.
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search

Despite the broad range of algorithms for Approximate Nearest Neighbor Search, most empirical evaluations of algorithms have focused on smaller datasets, typically of 1 million points~\citep{Benchmark}. However, deploying recent advances in embedding based techniques for search, recommendation and ranking at scale require ANNS indices at billion, trillion or larger scale. Barring a few recent papers, there is limited consensus on which algorithms are effective at this scale vis-\`a-vis their hardware cost. This competition compares ANNS algorithms at billion-scale by hardware cost, accuracy and performance. We set up an open source evaluation framework and leaderboards for both standardized and specialized hardware. The competition involves three tracks. The standard hardware track T1 evaluates algorithms on an Azure VM with limited DRAM, often the bottleneck in serving billion-scale indices, where the embedding data can be hundreds of GigaBytes in size. It uses FAISS~\citep{Faiss17} as the baseline. The standard hardware track T2 additional allows inexpensive SSDs in addition to the limited DRAM and uses DiskANN~\citep{DiskANN19} as the baseline. The specialized hardware track T3 allows any hardware configuration, and again uses FAISS as the baseline. We compiled six diverse billion-scale datasets, four newly released for this competition, that span a variety of modalities, data types, dimensions, deep learning models, distance functions and sources. The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold. Additionally, for track T3, separate leaderboards were created based on recall as well as cost-normalized and power-normalized query throughput.
Detecting the Role of an Entity in Harmful Memes: Techniques and their Limitations

Harmful or abusive online content has been increasing over time and it has been raising concerns among social media platforms, government agencies, and policymakers. Such harmful or abusive content has a significant negative impact on society such as cyberbullying led to suicides, COVID-19 related rumors led to hundreds of deaths. The content that is posted and shared online can be textual, visual, a combination of both, or a meme. In this paper, we provide our study on detecting the roles of entities in harmful memes, which is part of the CONSTRAINT-2022 shared task. We report the results on the participated system. We further provide a comparative analysis on different experimental settings (i.e., unimodal, multimodal, attention, and augmentation).
Topological electronic structure of YbMg 2 Bi 2 and CaMg 2 Bi 2

Zintl compounds have been extensively studied for their outstanding thermoelectric properties, but their electronic structure remains largely unexplored. Here, we present a detailed investigation of the electronic structure of the isostructural thermopower materials YbMg 2 Bi 2 and CaMg 2 Bi 2 using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT). The ARPES results show a signiﬁcantly smaller Fermi surface and Fermi velocity in CaMg 2 Bi 2 than in YbMg 2 Bi 2 . Our ARPES results also reveal that in the case of YbMg 2 Bi 2 , Yb-4 f states reside well below the Fermi level and likely have a negligible impact on transport properties. To properly model the position of 4 f -states, as well as the overall electronic structure, a Hubbard U at the Yb sites and spin-orbit coupling (SOC) have to be included in the DFT calculations. Interestingly, the theoretical results reveal that both materials belong to a Z 2 topological class and host robust topological surface states around E F . Due to the intrinsic hole doping, the topological states reside above the Fermi level, inaccessible by ARPES. Our results also suggest that in addition to SOC, vacancies and the resulting hole doping play an important role in the transport properties of these materials.
Updating non-standard neutrinos properties with Planck-CMB data and full-shape analysis of BOSS and eBOSS galaxies

Using the latest observational data from Planck-CMB and its combination with the pre-reconstructed full-shape (FS) galaxy power spectrum measurements from the BOSS DR12 sample and eBOSS LRG DR16 sample, we report the observational constraints on the cosmic neutrino properties given by the extended ΛCDM scenario: ΛCDM + N eff + ∑ mν + c 2 eff + c 2 vis + ξν , and its particular case ΛCDM + c 2 eff + c 2 vis + ξν , where N eff, ∑ mν , c 2 eff, c 2 vis, ξν are the effective number of species, the total neutrino mass, the sound speed in the neutrinos rest frame, the viscosity parameter and the degeneracy parameter quantifying a cosmological leptonic asymmetry, respectively. We observe that the combination of FS power spectrum measurements with the CMB data significantly improves the parametric space of the models compared to the CMB data alone case. We find no evidence for neutrinos properties other than the ones predicted by the standard cosmological theory. Our most robust observational constraints are given by CMB + BOSS analysis. For the generalized extended ΛCDM scenario, we find c 2 eff = 0.3304+0.0064 -0.0075, c 2 vis = 0.301+0.037 -0.033, ξν < 0.05, N eff = 2.90 ± 0.15 at 68% CL, with ∑ mν < 0.116 eV at 95% CL. These are the strongest limits ever reported for these extended ΛCDM scenarios.
ISA-bEL: Intelligent Search Algorithm based on Entity Linking

—Nowadays, the way in which the people in- teract with computers has changed. Text- or voice-based interfaces are being widely applied in different industries. Among the most used ways of processing the user input are those based on intents or retrieval algorithms. In these solutions, important information of the user could be lost in the process. For the proposed natural language processing pipeline the entities are going to take a principal role, under the assumption that entities are where the purpose of the user resides. Entities fed with context will be projected to a speciﬁc domain supported by a knowledge graph, resulting in what has been named as linked entities. These linked entities serve then as a key for searching a top level aggregation concept within our knowledge graph.
Energy decay analysis for Porous elastic system with microtemperature : A second spectrum approach

In this work, we analyze porous elastic system with microtemperature from second spectrum viewpoint. Indeed, by using the classical Faedo-Galerkin method combined with the a priori estimates, we prove the existence and uniqueness of a global solution of this problem. Then we prove that this solution is exponentially stable without assuming the condition of equal wave speeds. Then, we introduce a finite element approximation and we prove that the associated discrete energy decays. Finally, we obtain some a priori error estimates assuming additional regularity on the solution and we present some numerical results which demonstrate the accuracy of the approximation and the behaviour of the solution
ConceptDistil: Model-Agnostic Distillation of Concept Explanations

Concept-based explanations aims to ﬁll the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for speciﬁc models (e.g, neural networks) or data types (e.g., images), and by either try-ing to extract concepts from an already trained network or training self-explainable models through multi-task learning. In this work, we propose ConceptDistil, a method to bring concept explanations to any black-box classiﬁer using knowledge distillation. ConceptDistil is decomposed into two components: (1) a concept model that predicts which domain concepts are present in a given instance, and (2) a distillation model that tries to mimic the predictions of a black-box model using the concept model predictions. We validate ConceptDistil in a real world use-case, showing that it is able to optimize both tasks, bringing concept-explainability to any black-box model.
A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using Template Model Builder

A very common way to estimate the parameters of a hidden Markov model (HMM) is the relatively straightforward computation of maximum likelihood (ML) estimates. For this task, most users rely on user‐friendly implementation of the estimation routines via an interpreted programming language such as the statistical software environment R. Such an approach can easily require time‐consuming computations, in particular for longer sequences of observations. In addition, selecting a suitable approach for deriving confidence intervals for the estimated parameters is not entirely obvious, and often the computationally intensive bootstrap methods have to be applied. In this tutorial, we illustrate how to speed up the computation of ML estimates significantly via the R package TMB. Moreover, this approach permits simple retrieval of standard errors at the same time. We illustrate the performance of our routines using different data sets: first, two smaller samples from a mobile application for tinnitus patients and a well‐known data set of fetal lamb movements with 87 and 240 data points, respectively. Second, we rely on larger data sets of simulated data of sizes 2000 and 5000 for further analysis. This tutorial is accompanied by a collection of scripts, which are all available in the Supporting Information. These scripts allow any user with moderate programming experience to benefit quickly from the computational advantages of TMB.
Sub-Word Alignment is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine Translation

We leverage embedding duplication between aligned sub-words to extend the Parent-Child transfer learning method, so as to improve low-resource machine translation. We conduct experiments on benchmark datasets of My-En, Id-En and Tr-En translation scenarios. The test results show that our method produces substantial improvements, achieving the BLEU scores of 22.5, 28.0 and 18.1 respectively. In addition, the method is computationally efficient which reduces the consumption of training time by 63.8%, reaching the duration of 1.6 hours when training on a Tesla 16GB P100 GPU. All the models and source codes in the experiments will be made publicly available to support reproducible research.
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses

—Semi-supervised Federated Learning (SSFL) has recently drawn much attention due to its practical consideration, i.e., the clients may only have unlabeled data. In practice, these SSFL systems implement semi-supervised training by assigning a “guessed” label to the unlabeled data near the labeled data to convert the unsupervised problem into a fully supervised problem. However, the inherent properties of such semi-supervised training techniques create a new attack surface. In this paper, we discover and reveal a simple yet powerful poisoning attack against SSFL. Our attack utilizes the natural characteristic of semi-supervised learning to cause the model to be poisoned by poisoning unlabeled data. Speciﬁcally, the adversary just needs to insert a small number of maliciously crafted unlabeled samples (e.g., only 0.1% of the dataset) to infect model performance and misclassiﬁcation. Extensive case studies have shown that our attacks are effective on different datasets and common semi-supervised learning methods. To mitigate the attacks, we propose a defense, i.e., a minimax optimization-based client selection strategy, to enable the server to select the clients who hold the correct label information and high-quality updates. Our defense further employs a quality-based aggregation rule to strengthen the contributions of the selected updates. Evaluations under different attack conditions show that the proposed defense can well alleviate such unlabeled poisoning attacks. Our study unveils the vulnerability of SSFL to unlabeled poisoning attacks and provides the community with potential defense methods.
Introduction to Soar

This paper is the recommended initial reading for a functional overview of Soar, version 9.6. It includes an abstract overview of the architectural structure of Soar including its processing, memories, learning modules, their interfaces, and the representations of knowledge used by those modules. From there it describes the processing supported by those modules, including decision making, impasses and substates, procedure learning via chunking, reinforcement learning, semantic memory, episodic memory, and spatial-visual reasoning. It then reviews the levels of decision making and variety of learning in Soar, and analysis of Soar as an architecture supporting general human-level AI. Following the references is an appendix that contains short descriptions of recent Soar agents and a glossary of the terminology we use in describing Soar.