Miracle-dz/UniFAR-FTUs huggingface dataset:
ASCA X-Ray Observations of the NGC 2023 and NGC 2024 Regions

This paper presents the properties of X-ray sources in the regions of NGC 2023 and NGC 2024 observed with ASCA. We found 14 point-like sources in the NGC 2023 field. In the NGC 2024 field, on the other hand, we found extended X-ray emission in the central dust lane of NGC 2024, and resolved 5 point-like sources around the nebula. From the positional coincidence and moderate column density of Nn ~ 1.6 x 10 22 H cm" 2 , the extended source is mostly attributable to an integrated emission from X-ray emitting young stellar objects (YSOs) found with ROSAT. However, the plasma temperature of ~ 4 keV is higher than the ROSAT results as well as those of usual YSOs. Possible counterparts have been found for the other resolved sources, except two; one has large absorption at low energies and the other has moderate absorption. The plasma temperatures and luminosities from the point sources, except for an O-type star ( Ori and a star exhibiting X-ray flares, are estimated to be ~ 1-5 keV and 10 306 -10 315 erg s" 1 in the 0.5-10.0 keV energy band (assuming the distance of 400 pc), respectively. We found no X-ray peak at the position of a Class 0 protostar, NGC 2023-MM1.
Thermal Fluctuations of a Single-Domain Particle

A sufficiently fine ferromagnetic particle has a uniform vector magnetization whose magnitude is essentially constant, but whose direction fluctuates because of thermal agitation. The fluctuations are important in superparamagnetism and in magnetic aftereffect. The problem is approached here by methods familiar in the theory of stochastic processes. The "Langevin equation" of the problem is assumed to be Gilbert's equation of motion augmented by a "random-field" term. Consideration of a statistical ensemble of such particles leads to a "Fokker-Planck" partial differential equation, which describes the evolution of the probability density of orientations. The random-field concept, though convenient, can be avoided by use of the fluctuation-dissipation theorem. The Fokker-Planck equation, in general, is complicated by the presence of gyroscopic terms. These drop out in the case of axial symmetry: then the problem of finding nonequilibrium solutions can be restated as a minimization problem, susceptible to approximate treatments. The case of energy barriers large in comparison with $\mathrm{kT}$ is treated both by approximate minimization and by an adaptation of Kramers' treatment of the escape of particles over barriers. The limits of validity of the discrete-orientation approximation are discussed.
PoE: A Panel of Experts for Generalized Automatic Dialogue Assessment

Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure the quality of such conversational agents, a model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across multiple domains. Despite significant progress, existing ADEMs tend to perform well only on data that are similar to its training data (overfit to its training domain). This calls for a domain-generalized metric that can assess dialogues of different characteristics. To this end, we propose a Panel of Experts (PoE), a multitask network that consists of a shared transformer encoder and a collection of lightweight adapters. The shared encoder captures the general knowledge of dialogues across domains, while each adapter specializes in one specific domain and serves as a domain expert. To validate the idea, we construct a high-quality multi-domain dialogue dataset leveraging data augmentation and pseudo-labeling. The PoE network is comprehensively assessed on 16 dialogue evaluation datasets spanning a wide range of dialogue domains. It achieves state-of-the-art performance in terms of mean Spearman correlation over all the evaluation datasets. It exhibits better zero-shot generalization than existing state-of-the-art ADEMs and the ability to easily adapt to new domains with few-shot transfer learning.
On Event Individuation for Document-Level Information Extraction

As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE. In this position paper, we call into question the suitability of template filling for this purpose. We argue that the task demands definitive answers to thorny questions of event individuation -- the problem of distinguishing distinct events -- about which even human experts disagree. Through an annotation study and error analysis, we show that this raises concerns about the usefulness of template filling metrics, the quality of datasets for the task, and the ability of models to learn it. Finally, we consider possible solutions.
Intermittency in impact oscillators close to resonance

We examine the change in behaviour of the solutions of a simple one degree of freedom, periodically forced, impact oscillator following a grazing bifurcation in which an impact of zero velocity occurs following a change in one of the parameters of the system. It is shown that such a bifurcation leads to intermittent chaotic behaviour with low velocity impacts followed by an irregular sequence of high velocity impacts. We also show that there is a natural, discontinuous one-dimensional map associated with this relating one low velocity impact to the next and the properties of this map are analysed. We also construct the bifurcation diagram of the change in behaviour and show that this contains a series of periodic windows, with the period of the solutions increasing monotonically by one in each successive window as the bifurcation point is approached. By restricting our attention to the resonant case where the forcing frequency is twice the natural frequency of the oscillator it is possible to make asymptotic estimates of the form of the intermittent chaotic behaviour and these estimates are compared with some numerical calculations.
Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering

In many real-world scenarios, the absence of external knowledge source like Wikipedia restricts question answering systems to rely on latent internal knowledge in limited dialogue data. In addition, humans often seek answers by asking several questions for more comprehensive information. As the dialog becomes more extensive, machines are challenged to refer to previous conversation rounds to answer questions. In this work, we propose to leverage latent knowledge in existing conversation logs via a neural Retrieval-Reading system, enhanced with a TFIDF-based text summarizer refining lengthy conversational history to alleviate the long context issue. Our experiments show that our Retrieval-Reading system can exploit retrieved background knowledge to generate significantly better answers. The results also indicate that our context summarizer significantly helps both the retriever and the reader by introducing more concise and less noisy contextual information.
Unified Relationship between Cold Plasma Ejections and Flare Energies Ranging from Solar Microflares to Giant Stellar Flares

We often find spectral signatures of chromospheric cold plasma ejections accompanied by flares in a wide range of spatial scales in the solar and stellar atmospheres. However, the relationship between physical quantities (such as mass, kinetic energy, and velocity) of cold ejecta and flare energy has not been investigated in a unified manner for the entire range of flare energies to date. This study analyzed the spectra of cold plasma ejections associated with small-scale flares and solar flares (energy 1025–1029 erg) to supply smaller energy samples. We performed Hα imaging spectroscopy observation by the Solar Dynamics Doppler Imager on the Solar Magnetic Activity Research Telescope. We determined the physical quantities of the ejecta by cloud model fitting to the Hα spectrum. We determined the flare energy by differential emission measure analysis using the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory for small-scale flares and by estimating the bolometric energy for large-scale flares. As a result, we found that the ejection mass M and the total flare energy E tot follow a relation of M∝Etot2/3 . We show that the scaling law derived from a simple physical model explains the solar and stellar observations with a coronal magnetic field strength as a free parameter. We also found that the kinetic energy and velocity of the ejecta correlate with the flare energy. These results suggest a common mechanism driven by magnetic fields to cause cold plasma ejections with flares on the Sun and stars.
Some aspects of role of audio in immersive visualization

We define four different tasks which are common in immersive visualization. Immersive visualization takes place in virtual environments, which provide an integrated system of 3D auditory and 3D visual display. The main objective of our research is to find out the best possible ways to use audio in different tasks. In the long run the goal is more efficient utilization of the spatial audio in immersive visualization application areas. Results of our first experiment have proven that navigation is possible using auditory cues.
On the Complexity of Representation Learning in Contextual Linear Bandits

In contextual linear bandits, the reward function is assumed to be a linear combination of an unknown reward vector and a given embedding of context-arm pairs. In practice, the embedding is often learned at the same time as the reward vector, thus leading to an online representation learning problem. Existing approaches to representation learning in contextual bandits are either very generic (e.g., model-selection techniques or algorithms for learning with arbitrary function classes) or specialized to particular structures (e.g., nested features or representations with certain spectral properties). As a result, the understanding of the cost of representation learning in contextual linear bandit is still limited. In this paper, we take a systematic approach to the problem and provide a comprehensive study through an instance-dependent perspective. We show that representation learning is fundamentally more complex than linear bandits (i.e., learning with a given representation). In particular, learning with a given set of representations is never simpler than learning with the worst realizable representation in the set, while we show cases where it can be arbitrarily harder. We complement this result with an extensive discussion of how it relates to existing literature and we illustrate positive instances where representation learning is as complex as learning with a fixed representation and where sub-logarithmic regret is achievable.
Investigating Stellar Variability in the Open Cluster Region NGC 381

We study variable stars in the field of the open cluster NGC 381 using photometric data observed over 27 nights and identify a total of 57 variable stars, six of which are member stars. The variable stars are classified based on their periods, amplitudes, light-curve shapes, and locations in the H-R diagram. We found a rich variety of variable stars in the cluster. We identified a total of 10 eclipsing binaries, of which two are of Algol type while eight are of W UMa type (EW). The estimated ages of these EW binaries are greater than 0.6 Gyr, which is in agreement with the formation time constraint of ≥0.6 Gyr on short-period eclipsing binaries. The physical parameters of the two EW-type binaries are estimated using PHOEBE model-fitting software. The pulsating variable stars include one each from the δ Scuti and γ Dor variability classes. We determined the pulsation modes of pulsating variables with the help of the FAMIAS package. We obtained 15 rotational variable stars comprising four dwarf stars identified on the basis of their log(g) versus log(T eff) diagram. These dwarf stars are found to have generally longer periods than the remaining rotational variables.
Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPs

Figure skating scoring is challenging because it requires judging players’ technical moves as well as coordination with the background music. Most learning-based methods struggle for two reasons: 1) each move in figure skating changes quickly, hence simply applying traditional frame sampling will lose a lot of valuable information, especially in 3 to 5 minutes lasting videos; 2) prior methods rarely considered the critical audio-visual relationship in their models. Due to these reasons, we introduce a novel architecture, named Skating-Mixer. It extends the MLP framework into a multimodal fashion and effectively learns long-term representations through our designed memory recurrent unit (MRU). Aside from the model, we collected a high-quality audio-visual FS1000 dataset, which contains over 1000 videos on 8 types of programs with 7 different rating metrics, overtaking other datasets in both quantity and diversity. Experiments show the proposed method achieves SOTAs over all major metrics on the public Fis-V and our FS1000 dataset. In addition, we include an analysis applying our method to the recent competitions in Beijing 2022 Winter Olympic Games, proving our method has strong applicability.
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization

The nonconvex formulation of matrix completion problem has received significant attention in recent years due to its affordable complexity compared to the convex formulation. Gradient descent (GD) is the simplest yet efficient baseline algorithm for solving nonconvex optimization problems. The success of GD has been witnessed in many different problems in both theory and practice when it is combined with random initialization. However, previous works on matrix completion require either careful initialization or regularizers to prove the convergence of GD. In this work, we study the rank-1 symmetric matrix completion and prove that GD converges to the ground truth when small random initialization is used. We show that in logarithmic amount of iterations, the trajectory enters the region where local convergence occurs. We provide an upper bound on the initialization size that is sufficient to guarantee the convergence and show that a larger initialization can be used as more samples are available. We observe that implicit regularization effect of GD plays a critical role in the analysis, and for the entire trajectory, it prevents each entry from becoming much larger than the others.
Multilingual Sequence-to-Sequence Models for Hebrew NLP

Recent work attributes progress in NLP to large language models (LMs) with increased model size and large quantities of pretraining data. Despite this, current state-of-the-art LMs for Hebrew are both under-parameterized and under-trained compared to LMs in other languages. Additionally, previous work on pretrained Hebrew LMs focused on encoder-only models. While the encoder-only architecture is beneficial for classification tasks, it does not cater well for sub-word prediction tasks, such as Named Entity Recognition, when considering the morphologically rich nature of Hebrew. In this paper we argue that sequence-to-sequence generative architectures are more suitable for LLMs in the case of morphologically rich languages (MRLs) such as Hebrew. We demonstrate that by casting tasks in the Hebrew NLP pipeline as text-to-text tasks, we can leverage powerful multilingual, pretrained sequence-to-sequence models as mT5, eliminating the need for a specialized, morpheme-based, separately fine-tuned decoder. Using this approach, our experiments show substantial improvements over previously published results on existing Hebrew NLP benchmarks. These results suggest that multilingual sequence-to-sequence models present a promising building block for NLP for MRLs.
Graph Learning: A Comprehensive Survey and Future Directions

—Graph learning aims to learn complex relationships among nodes and the topological structure of graphs, such as social networks, academic networks and e-commerce networks, which are common in the real world. Those relationships make graphs special compared with traditional tabular data in which nodes are dependent on non-Euclidean space and contain rich information to explore. Graph learning developed from graph theory to graph data mining and now is empowered with representation learning, making it achieve great performances in various scenarios, even including text, image, chemistry, and biology. Due to the broad application prospects in the real world, graph learning has become a popular and promising area in machine learning. Thousands of works have been proposed to solve various kinds of problems in graph learning and is appealing more and more attention in academic community, which makes it pivotal to survey previous valuable works. Although some of the researchers have noticed this phenomenon and ﬁnished impressive surveys on graph learning. However, they failed to link related objectives, methods and applications in a more logical way and cover current ample scenarios as well as challenging problems due to the rapid expansion of the graph learning.
Latent Variable Representation for Reinforcement Learning

Deep latent variable models have achieved significant empirical successes in model-based reinforcement learning (RL) due to their expressiveness in modeling complex transition dynamics. On the other hand, it remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of RL. In this paper, we provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle in the face of uncertainty for exploration. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models. Theoretically, we establish the sample complexity of the proposed approach in the online and offline settings. Empirically, we demonstrate superior performance over current state-of-the-art algorithms across various benchmarks.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.
PAL: Persona-Augmented Emotional Support Conversation Generation

Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous studies have demonstrated that seekers' persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support. In this paper, our empirical analysis verifies that persona has an important impact on emotional support. Therefore, we propose a framework for dynamically inferring and modeling seekers' persona. We first train a model for inferring the seeker's persona from the conversation history. Accordingly, we propose PAL, a model that leverages persona information and, in conjunction with our strategy-based controllable generation method, provides personalized emotional support. Automatic and manual evaluations demonstrate that PAL achieves state-of-the-art results, outperforming the baselines on the studied benchmark. Our code and data are publicly available at https://github.com/chengjl19/PAL.
Exact Error Backpropagation Through Spikes for Precise Training of Spiking Neural Networks

—Event-based simulations of Spiking Neural Networks (SNNs) are fast and accurate. However, they are rarely used in the context of event-based gradient descent because their implementations on GPUs are difﬁcult. Discretization with the forward Euler method is instead often used with gradient descent techniques but has the disadvantage of being computationally expensive. Moreover, the lack of precision of discretized simulations can create mismatches between the simulated models and analog neuromorphic hardware. In this work, we propose a new exact error-backpropagation through spikes method for SNNs, extending Fast & Deep to multiple spikes per neuron. We show that our method can be efﬁciently implemented on GPUs in a fully event-based manner, making it fast to compute and precise enough for analog neuromorphic hardware. Compared to the original Fast & Deep and the current state-of-the-art event-based gradient-descent algorithms, we demonstrate increased performance on several benchmark datasets with both feedforward and convolutional SNNs. In particular, we show that multi-spike SNNs can have advantages over single-spike networks in terms of convergence, sparsity, classiﬁcation latency and sensitivity to the dead neuron problem.
Product structure extension of the Alon--Seymour--Thomas theorem

Alon, Seymour and Thomas [1990] proved that every $n$-vertex graph excluding $K_t$ as a minor has treewidth less than $t^{3/2}\sqrt{n}$. Illingworth, Scott and Wood [2022] recently refined this result by showing that every such graph is a subgraph of some graph with treewidth $t-1$, where each vertex is blown up by a complete graph of order $\sqrt{tn}$. Solving an open problem of Illingworth, Scott and Wood [2022], we prove that the treewidth bound can be reduced to $4$ while keeping blowups of size $O_t(\sqrt{n})$. As an extension of the Lipton--Tarjan theorem, in the case of planar graphs, we show that the treewidth can be further reduced to $2$, which is best possible. We generalise this result for $K_{3,t}$-minor-free graphs, with blowups of order $O(t\sqrt{n})$. This setting includes graphs embeddable on any fixed surface.
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation

In recent years several learning approaches to point goal navigation in previously unseen environments have been proposed. They vary in the representations of the environments, problem decomposition, and experimental evaluation. In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem. We adapt the (POMDP) sub-goal framework proposed by [1] and modify the component that estimates frontier properties by using partial semantic maps of indoor scenes built from images' semantic segmentation. In addition to the well-known completeness of the model-based approach, we demonstrate that it is robust and efficient in that it leverages informative, learned properties of the frontiers compared to an optimistic frontier-based planner. We also demonstrate its data efficiency compared to the end-to-end deep reinforcement learning approaches. We compare our results against an optimistic planner, ANS and DD-PPO on Matterport3D dataset using the Habitat Simulator. We show comparable, though slightly worse performance than the SOTA DD-PPO approach, yet with far fewer data.