Miracle-dz/UniFAR-FTUs huggingface dataset:
How many supermassive black hole binaries are detectable through tracking relative motions by sub/millimeter VLBI

The sub/millimeter wavelengths (86-690 GHz) very long baseline interferometry (VLBI) will provide $\sim5-40\ \mu$as angular resolution, $\sim10$ mJy baseline sensitivity, and $\sim 1\ \mu$as/yr proper motion precision, which can directly detect supermassive black hole binary (SMBHB) systems by imaging the two visible sources and tracking their relative motions. Such a way exhibits an advantage compared to indirect detect methods of observing periodic signals in motion and light curves, which are difficult to confirm from competing models. Moreover, tracking relative motion at sub/millimeter wavelengths is more reliable, as there is a negligible offset between the emission region and the black hole center. In this way, it is unnecessary to correct the black hole location by a prior of jet morphology as it would be required at longer wavelengths. We extend the formalism developed in D'Orazio&Loeb (2018) to link the observations with the orbital evolution of SMBHBs from the $\lesssim$10 kpc dynamical friction stages to the $\lesssim 0.01$ pc gravitational radiation stages, and estimate the detectable numbers of SMBHBs. By assuming 5\% of AGNs holding SMBHBs, we find that the number of detectable SMBHBs with redshift $z\le 0.5$ and mass $M\leq 10^{11}M_\odot$ is about 20. Such detection relies heavily on proper motion precision and sensitivity. Furthermore, we propose that the simultaneous multi-frequency technique plays a key role in meeting the observational requirements.
Interpretability in Machine Learning: on the Interplay with Explainability, Predictive Performances and Models

Interpretability has recently gained attention in the field of machine learning, for it is crucial when it comes to high-stakes decisions or troubleshooting. This abstract concept is hard to grasp and has been associated, over time, with many labels and preconceived ideas. In this position paper, in order to clarify some misunderstandings regarding interpretability, we discuss its relationship with significant concepts in machine learning: explainability, predictive performances, and machine learning models. For instance, we challenge the idea that interpretability and explainability are substitutes to one another, or that a fixed degree of interpretability can be associated with a given machine learning model.
Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation

Transformers have been successfully applied in the field of video-based 3D human pose estimation. However, the high computational costs of these video pose transformers (VPTs) make them impractical on resource-constrained devices. In this paper, we present a plug-and-play pruning-and-recovering framework, called Hourglass Tokenizer (HoT), for efficient transformer-based 3D human pose estimation from videos. Our HoT begins with pruning pose tokens of redundant frames and ends with recovering full-length tokens, resulting in a few pose tokens in the intermediate transformer blocks and thus improving the model efficiency. To effectively achieve this, we propose a token pruning cluster (TPC) that dynamically selects a few representative tokens with high semantic diversity while eliminating the redundancy of video frames. In addition, we develop a token recovering attention (TRA) to restore the detailed spatio-temporal information based on the selected tokens, thereby expanding the network output to the original full-length temporal resolution for fast inference. Extensive experiments on two benchmark datasets (i.e., Human3.6M and MPI-INF-3DHP) demonstrate that our method can achieve both high efficiency and estimation accuracy compared to the original VPT models. For instance, applying to MotionBERT and MixSTE on Human3.6M, our HoT can save nearly 50% FLOPs without sacrificing accuracy and nearly 40% FLOPs with only 0.2% accuracy drop, respectively. Code and models are available at https://github.com/NationalGAILab/HoT.
Engage Wider Audience or Facilitate Quality Answers? a Mixed-methods Analysis of Questioning Strategies for Research Sensemaking on a Community Q&A Site

Discussing research-sensemaking questions on Community Question and Answering (CQA) platforms has been an increasingly common practice for the public to participate in science communication. Nonetheless, how users strategically craft research-sensemaking questions to engage public participation and facilitate knowledge construction is a significant yet less understood problem. To fill this gap, we collected 837 science-related questions and 157,684 answers from Zhihu, and conducted a mixed-methods study to explore user-developed strategies in proposing research-sensemaking questions, and their potential effects on public engagement and knowledge construction. Through open coding, we captured a comprehensive taxonomy of question-crafting strategies, such as eyecatching narratives with counter-intuitive claims and rigorous descriptions with data use. Regression analysis indicated that these strategies correlated with user engagement and answer construction in different ways (e.g., emotional questions attracted more views and answers), yet there existed a general divergence between wide participation and quality knowledge establishment, when most questioning strategies could not ensure both. Based on log analysis, we further found that collaborative editing afforded unique values in refining research-sensemaking questions regarding accuracy, rigor, comprehensiveness and attractiveness. We propose design implications to facilitate accessible, accurate and engaging science communication on CQA platforms.
Encoding Performance Data in MEI with the Automatic Music Performance Analysis and Comparison Toolkit (AMPACT)

This paper presents a new method of encoding performance data in MEI using the recently added \texttt{} element. Performance data was extracted using the Automatic Music Performance Analysis and Comparison Toolkit (AMPACT) and encoded as a JSON object within an \texttt{} element linked to a specific musical note. A set of pop music vocals has was encoded to demonstrate both the range of descriptors that can be encoded inand how AMPACT can be used for extracting performance data in the absence of a fully specified musical score.
Cosmic-gamma-ray emission at low-luminosity from CCO 1E 1207.4-5209 and its Host SNR G296.5+10.0

The origin and acceleration mechanisms of energetic particles in the universe remain enigmatic in contemporary astrophysics. Recent efforts have focused on identifying Galactic sources capable of accelerating particles to 1 PeV, known as PeVatrons, and also identifying sources contributing to the spectrum at lower energies. The different morphology of galactic supernova remnants is directly related to the type of stellar explosion and the existence of a possible Compact Central Object (CCO), which possess intense radiativegravitational fields on their surfaces. These CCOs, due to their strong fields and interactions with surrounding magnetic clouds, are potential candidates for cosmic ray production. Through observations of the compact X-ray source 1E 1207.4-5209, located near the remnant G296.5+10.0, and using the enhanced GALPROP code, we analyze the emission of gamma-ray emission resulting from cosmic-ray acceleration and propagation. Additionally, we calculate the contribution of this association to the overall observed Galactic cosmic-ray flux, considering cosmic-ray propagation within the Galaxy, including energy losses and particle interactions. Our findings suggest that this setup offers a fertile environment for producing a wide range of cosmic-ray energies at low energy within the Galaxy.
Analysis of frequent trading effects of various machine learning models

In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN model, and the vector machine. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy.
Rethinking Large Language Models in Mental Health Applications

Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications. This paper offers a perspective on using LLMs in mental health applications. It discusses the instability of generative models for prediction and the potential for generating hallucinatory outputs, underscoring the need for ongoing audits and evaluations to maintain their reliability and dependability. The paper also distinguishes between the often interchangeable terms ``explainability'' and ``interpretability'', advocating for developing inherently interpretable methods instead of relying on potentially hallucinated self-explanations generated by LLMs. Despite the advancements in LLMs, human counselors' empathetic understanding, nuanced interpretation, and contextual awareness remain irreplaceable in the sensitive and complex realm of mental health counseling. The use of LLMs should be approached with a judicious and considerate mindset, viewing them as tools that complement human expertise rather than seeking to replace it.
Bit Cipher - A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models

While Large Language Models (LLMs) become ever more dominant, classic pre-trained word embeddings sustain their relevance through computational efficiency and nuanced linguistic interpretation. Drawing from recent studies demonstrating that the convergence of GloVe and word2vec optimizations all tend towards log-co-occurrence matrix variants, we construct a novel word representation system called Bit-cipher that eliminates the need of backpropagation while leveraging contextual information and hyper-efficient dimensionality reduction techniques based on unigram frequency, providing strong interpretability, alongside efficiency. We use the bit-cipher algorithm to train word vectors via a two-step process that critically relies on a hyperparameter -- bits -- that controls the vector dimension. While the first step trains the bit-cipher, the second utilizes it under two different aggregation modes -- summation or concatenation -- to produce contextually rich representations from word co-occurrences. We extend our investigation into bit-cipher's efficacy, performing probing experiments on part-of-speech (POS) tagging and named entity recognition (NER) to assess its competitiveness with classic embeddings like word2vec and GloVe. Additionally, we explore its applicability in LM training and fine-tuning. By replacing embedding layers with cipher embeddings, our experiments illustrate the notable efficiency of cipher in accelerating the training process and attaining better optima compared to conventional training paradigms. Experiments on the integration of bit-cipher embedding layers with Roberta, T5, and OPT, prior to or as a substitute for fine-tuning, showcase a promising enhancement to transfer learning, allowing rapid model convergence while preserving competitive performance.
Implicit Event-RGBD Neural SLAM

Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting variation, which often leads to issues like convergence failures, localization drifts, and distorted mapping. To address these challenges, we propose EN-SLAM, the first event-RGBD implicit neural SLAM framework, which effectively leverages the high rate and high dynamic range advantages of event data for tracking and mapping. Specifically, EN-SLAM proposes a differentiable CRF (Camera Response Function) rendering technique to generate distinct RGB and event camera data via a shared radiance field, which is optimized by learning a unified implicit representation with the captured event and RGBD supervision. Moreover, based on the temporal difference property of events, we propose a temporal aggregating optimization strategy for the event joint tracking and global bundle adjustment, capitalizing on the consecutive difference constraints of events, significantly enhancing tracking accuracy and robustness. Finally, we construct the simulated dataset DEV-Indoors and real captured dataset DEV-Reals containing 6 scenes, 17 sequences with practical motion blur and lighting changes for evaluations. Experimental results show that our method outperforms the SOTA methods in both tracking ATE and mapping ACC with a real-time 17 FPS in various challenging environments. Project page: https://delinqu.github. io/EN-SLAM.
Modality-invariant and Specific Prompting for Multimodal Human Perception Understanding

Understanding human perceptions presents a formidable multimodal challenge for computers, encompassing aspects such as sentiment tendencies and sense of humor. While various methods have recently been introduced to extract modality-invariant and specific information from diverse modalities, with the goal of enhancing the efficacy of multimodal learning, few works emphasize this aspect in large language models. In this paper, we introduce a novel multimodal prompt strategy tailored for tuning large language models. Our method assesses the correlation among different modalities and isolates the modality-invariant and specific components, which are then utilized for prompt tuning. This approach enables large language models to efficiently and effectively assimilate information from various modalities. Furthermore, our strategy is designed with scalability in mind, allowing the integration of features from any modality into pretrained large language models. Experimental results on public datasets demonstrate that our proposed method significantly improves performance compared to previous methods.
CityScope: Enhanced Localozation and Synchronizing AR for Dynamic Urban Weather Visualization

CityScope uses augmented reality (AR) to change our interaction with weather data. The main goal is to develop real-time 3D weather visualizations, with Taiwan as the model. It displays live weather data from the Central Weather Bureau (CWB), projected onto a physical representation of Taiwan's landscape. A pivotal advancement in our project is the integration of AprilTag with plane detection technology. This innovative combination significantly enhances the precision of the virtual visualizations within the physical world. By accurately aligning AR elements with real-world environments, CityScope achieves a seamless and realistic amalgamation of weather data and the physical terrain of Taiwan. This breakthrough in AR technology not only enhances the accuracy of weather visualizations but also enriches user experience, offering an immersive and interactive way to understand and engage with meteorological information. CityScope stands as a testament to the potential of AR in transforming data visualization and public engagement in meteorology.
Explainable Product Classification for Customs

The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this task follows the doctrine of precedent and can be nontrivial even for experienced officers. Together with the Korea Customs Service (KCS), we propose a first-ever explainable decision supporting model that suggests the most likely subheadings (i.e., the first six digits) of the HS code. The model also provides reasoning for its suggestion in the form of a document that is interpretable by customs officers. We evaluated the model using 5,000 cases that recently received a classification request. The results showed that the top-3 suggestions made by our model had an accuracy of 93.9% when classifying 925 challenging subheadings. A user study with 32 customs experts further confirmed that our algorithmic suggestions accompanied by explainable reasonings, can substantially reduce the time and effort taken by customs officers for classification reviews.
A gamma ray study of Galactic PeVatron candidates LHAASO J1825-1236 and J1839-0545

Recent studies by LHAASO have shown the presence of high-luminosity PeVatrons in our Galaxy. We examine two notable sources, each consisting of two pulsars, detected by LHAASO. We study multimessenger emissions from these pulsars, including gamma rays and particles. We simulated particle propagation throughout the Galaxy using the GALPROP software, accounting for emission from energy losses due to spin-down. As a result, we present the particle spectra generated during this propagation phase along with the corresponding gamma ray emission. Furthermore, we used the Gammapy software to perform gamma ray measurements from these sources in anticipation of future analyses to be carried out with the CTA observatory, which is now under development. The results indicate that CTA may be able to observe these sources and demonstrate the significant influence of gamma rays produced by proton propagation on the high-energy gamma ray spectra.
Compositional Fusion of Signals in Data Embedding

Embeddings in AI convert symbolic structures into fixed-dimensional vectors, effectively fusing multiple signals. However, the nature of this fusion in real-world data is often unclear. To address this, we introduce two methods: (1) Correlation-based Fusion Detection, measuring correlation between known attributes and embeddings, and (2) Additive Fusion Detection, viewing embeddings as sums of individual vectors representing attributes. Applying these methods, word embeddings were found to combine semantic and morphological signals. BERT sentence embeddings were decomposed into individual word vectors of subject, verb and object. In the knowledge graph-based recommender system, user embeddings, even without training on demographic data, exhibited signals of demographics like age and gender. This study highlights that embeddings are fusions of multiple signals, from Word2Vec components to demographic hints in graph embeddings.
Mapping GENERIC Hydrodynamics into Carter's Multifluid Theory

We show that the GENERIC model for relativistic heat conduction is a multifluid of Carter; this allows one to compute the multifluid constitutive relations directly from the GENERIC formalism. As a quick application, we prove that in the limit of infinite heat conductivity, GENERIC heat conduction reduces to the relativistic two-fluid model for superfluidity. This surprising “crossover” is a consequence of relativistic causality: if diffusion happens too fast, all the diffusing charge cumulates on the surface of the light cone, and it eventually travels at the speed of light like a wave. Our analysis is non-perturbative and carried out in a fully non-linear regime.
Assurance for Deployed Continual Learning Systems

The future success of the Navy will depend, in part, on artificial intelligence. In practice, many artificially intelligent algorithms, and in particular deep learning models, rely on continual learning to maintain performance in dynamic environments. The software requires adaptation to maintain its initial level of performance in unseen situations. However, if not monitored properly, continual learning may lead to several issues including catastrophic forgetting in which a trained model forgets previously learned tasks when being retrained on new data. The authors created a new framework for safely performing continual learning with the goal of pairing this safety framework with a deep learning computer vision algorithm to allow for safe and high-performing automatic deck tracking on carriers and amphibious assault ships. The safety framework includes several features, such as an ensemble of convolutional neural networks to perform image classification, a manager to record confidences and determine the best answer from the ensemble, a model of the environment to predict when the system may fail to meet minimum performance metrics, a performance monitor to log system and domain performance and check against requirements, and a retraining component to update the ensemble and manager to maintain performance. The authors validated the proposed method using extensive simulation studies based on dynamic image classification. The authors showed the safety framework could probabilistically detect out of distribution data. The results also show the framework can detect when the system is no longer performing safely and can significantly extend the working envelope of an image classifier.
MULTIWAVELENGTH PROPERTIES OF THE LOW-REDSHIFT ISOLATED GALAXIES WITH ACTIVE NUCLEI MODELLED WITH CIGALE

Using the CIGALE software, we present the preliminary results of a multiwavelength analysis of eighteen low-redshift isolated galaxies with active nuclei (isolated AGNs). This sample was formed by cross-matching the 2MIG isolated AGNs sample with the SDSS DR9 catalog. The host galaxies of this sample have not undergone a merger for at least three billion years, making them a unique laboratory for studying interactions between various astrophysical processes without the complicating factors of merging with other galaxies or the effects of a denser environment. In addition, the study of isolated AGNs can provide valuable information about the evolution and activity of galaxies in the broader context of the distribution of large-scale structures of the Universe. First, we seek to understand how the environment affects the physical processes involved in the accretion of matter onto supermassive black holes in these galaxies. Secondly, to what extent do processes of star formation or degeneration of nuclei activity continue the evolution of these galaxies? Third, how does the localization of isolated AGNs in voids or filaments of a large-scale structure determine the properties of this envi- ronment at the low redshifts? Using observable fluxes from UV to the radio ranges from archival databases of space-born and ground-based observatories (GALEX, SDSS, 2MASS, Spitzer, Hershel, IRAS, WISE, VLA), we estimated the contribution from the emission of an active nucleus to the galaxy’s total emission, the stellar mass, and the star formation rate. The mass of the stellar component falls from 1010 MSun and 1011 MSun. The star formation rate for most galaxies (except UGC 10120) does not exceed 3 MSun per year. The best SED fittings (with χ2 values less than 5) are obtained for the galaxies CGCG248-019 (χ2 = 1.6), CGCG179-005 (χ2 = 1.6), CGCG243-024 (χ2 = 2.6), IC0009 (χ2 = 2.8), MCG+09-25-022 (χ2 = 3.1), UGC10244 (χ2 = 4.1)