Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Extinction on Quasar Luminosity Distances Determined from UV and X-Ray Flux Measurements

In Khadka et al., a sample of X-ray-detected reverberation-mapped quasars was presented and applied for the comparison of cosmological constraints inferred using two well-established relations in active galactic nuclei—the X-ray/UV luminosity (L X –L UV) relation and the broad-line region radius–luminosity (R–L) relation. L X –L UV and R–L luminosity distances to the same quasars exhibit a distribution of their differences that is generally asymmetric and positively shifted for the six cosmological models we consider. We demonstrate that this behavior can be interpreted qualitatively as arising as a result of the dust extinction of UV/X-ray quasar emission. We show that the extinction always contributes to the nonzero difference between L X –L UV-based and R–L-based luminosity distances and we derive a linear relationship between the X-ray/UV color index E X−UV and the luminosity-distance difference, which also depends on the value of the L X –L UV relation slope. Taking into account the median and the peak values of the luminosity-distance difference distributions, the average X-ray/UV color index falls in the range of E ¯ X − UV = 0.03 – 0.28 mag for the current sample of 58 sources. This amount of extinction is typical for the majority of quasars and can be attributed to the circumnuclear and interstellar media of host galaxies. After applying the standard hard X-ray and far-UV extinction cuts, heavily extincted sources are removed but overall the shift toward positive values persists. The effect of extinction on luminosity distances is more pronounced for the L X –L UV relation since the extinction of UV and X-ray emissions both contribute.
A new iterative method for construction of the Kolmogorov-Arnold representation

The Kolmogorov-Arnold representation of a continuous multivariate function is a decomposition of the function into a structure of inner and outer functions of a single variable. It can be a convenient tool for tasks where it is required to obtain a predictive model that maps some vector input of a black box system into a scalar output. However, the construction of such representation based on the recorded input-output data is a challenging task. In the present paper, it is suggested to decompose the underlying functions of the representation into continuous basis functions and parameters. A novel lightweight algorithm for parameter identification is then proposed. The algorithm is based on the Newton-Kaczmarz method for solving non-linear systems of equations and is locally convergent. Numerical examples show that it is more robust with respect to the section of the initial guess for the parameters than the straightforward application of the Gauss-Newton method for parameter identification.
Comparing Variation in Tokenizer Outputs Using a Series of Problematic and Challenging Biomedical Sentences

Background&Objective: Biomedical text data are increasingly available for research. Tokenization is an initial step in many biomedical text mining pipelines. Tokenization is the process of parsing an input biomedical sentence (represented as a digital character sequence) into a discrete set of word/token symbols, which convey focused semantic/syntactic meaning. The objective of this study is to explore variation in tokenizer outputs when applied across a series of challenging biomedical sentences. Method: Diaz [2015] introduce 24 challenging example biomedical sentences for comparing tokenizer performance. In this study, we descriptively explore variation in outputs of eight tokenizers applied to each example biomedical sentence. The tokenizers compared in this study are the NLTK white space tokenizer, the NLTK Penn Tree Bank tokenizer, Spacy and SciSpacy tokenizers, Stanza/Stanza-Craft tokenizers, the UDPipe tokenizer, and R-tokenizers. Results: For many examples, tokenizers performed similarly effectively; however, for certain examples, there were meaningful variation in returned outputs. The white space tokenizer often performed differently than other tokenizers. We observed performance similarities for tokenizers implementing rule-based systems (e.g. pattern matching and regular expressions) and tokenizers implementing neural architectures for token classification. Oftentimes, the challenging tokens resulting in the greatest variation in outputs, are those words which convey substantive and focused biomedical/clinical meaning (e.g. x-ray, IL-10, TCR/CD3, CD4+ CD8+, and (Ca2+)-regulated). Conclusion: When state-of-the-art, open-source tokenizers from Python and R were applied to a series of challenging biomedical example sentences, we observed subtle variation in the returned outputs.
Multilingual Previously Fact-Checked Claim Retrieval

Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper introduces a new multilingual dataset -- MultiClaim -- for previously fact-checked claim retrieval. We collected 28k posts in 27 languages from social media, 206k fact-checks in 39 languages written by professional fact-checkers, as well as 31k connections between these two groups. This is the most extensive and the most linguistically diverse dataset of this kind to date. We evaluated how different unsupervised methods fare on this dataset and its various dimensions. We show that evaluating such a diverse dataset has its complexities and proper care needs to be taken before interpreting the results. We also evaluated a supervised fine-tuning approach, improving upon the unsupervised method significantly.
An Introduction to String Diagrams for Computer Scientists

This document is an elementary introduction to string diagrams. It takes a computer science perspective: rather than using category theory as a starting point, we build on intuitions from formal language theory, treating string diagrams as a syntax with its semantics. After the basic theory, pointers are provided to contemporary applications of string diagrams in various fields of science.
REMAST: Real-time Emotion-based Music Arrangement with Soft Transition

Music as an emotional intervention medium has important applications in scenarios such as music therapy, games, and movies. However, music needs real-time arrangement according to changing emotions, bringing challenges to balance emotion real-time fit and soft emotion transition due to the fine-grained and mutable nature of the target emotion. Existing studies mainly focus on achieving emotion real-time fit, while the issue of smooth transition remains understudied, affecting the overall emotional coherence of the music. In this paper, we propose REMAST to address this trade-off. Specifically, we recognize the last timestep's music emotion and fuse it with the current timestep's input emotion. The fused emotion then guides REMAST to generate the music based on the input melody. To adjust music similarity and emotion real-time fit flexibly, we downsample the original melody and feed it into the generation model. Furthermore, we design four music theory features by domain knowledge to enhance emotion information and employ semi-supervised learning to mitigate the subjective bias introduced by manual dataset annotation. According to the evaluation results, REMAST surpasses the state-of-the-art methods in objective and subjective metrics. These results demonstrate that REMAST achieves real-time fit and smooth transition simultaneously, enhancing the coherence of the generated music.
Efficient Part-Graph Hashes for Object Categorization

In this work we present a part-graph-based classification method for classifying objects using an additive feature (GRSD-). The method allows for easy addition of new training data, without re-creating the complete model, classification time is low due to hashing, and is flexible enough to work with partial occlusions. We compared the proposed classification method on laser point clouds to SVM. We used an additive version of the GRSD feature [1],which is a histogram of neighborhoods of surfaces of different type. We exploited the additive property to efficiently compute the features for different groupings of the parts.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning

Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route. In this work, we propose a novel framework that utilizes context information for improved retrosynthetic planning. We view synthetic routes as reaction graphs and propose to incorporate context through three principled steps: encode molecules into embeddings, aggregate information over routes, and readout to predict reactants. Our approach is the first attempt to utilize in-context learning for retrosynthesis prediction in retrosynthetic planning. The entire framework can be efficiently optimized in an end-to-end fashion and produce more practical and accurate predictions. Comprehensive experiments demonstrate that by fusing in the context information over routes, our model significantly improves the performance of retrosynthetic planning over baselines that are not context-aware, especially for long synthetic routes. Code is available at https://github.com/SongtaoLiu0823/FusionRetro.
StoryWars: A Dataset and Instruction Tuning Baselines for Collaborative Story Understanding and Generation

Collaborative stories, which are texts created through the collaborative efforts of multiple authors with different writing styles and intentions, pose unique challenges for NLP models. Understanding and generating such stories remains an underexplored area due to the lack of open-domain corpora.To address this, we introduce StoryWars, a new dataset of over 40,000 collaborative stories written by 9,400 different authors from an online platform. We design 12 task types, comprising 7 understanding and 5 generation task types, on {pasted macro ‘STORYWARS’}, deriving 101 diverse story-related tasks in total as a multi-task benchmark covering all fully-supervised, few-shot, and zero-shot scenarios.Furthermore, we present our instruction-tuned model, InstructStory, for the story tasks showing that instruction tuning, in addition to achieving superior results in zero-shot and few-shot scenarios, can also obtain the best performance on the fully-supervised tasks in StoryWars, establishing strong multi-task benchmark performances on StoryWars.
Full Velocities and Propagation Directions of Coronal Mass Ejections Inferred from Simultaneous Full-disk Imaging and Sun-as-a-star Spectroscopic Observations

Coronal mass ejections (CMEs) are violent ejections of magnetized plasma from the Sun that can trigger geomagnetic storms, endanger satellite operations, and destroy electrical infrastructures on the Earth. After systematically searching Sun-as-a-star spectra observed by the Extreme Ultraviolet Variability Experiment on board the Solar Dynamics Observatory (SDO) from 2010 May to 2022 May, we identified eight CMEs associated with flares and filament eruptions by analyzing the blue-wing asymmetry of the O iii 52.58 nm line profiles. Combined with images simultaneously taken by the 30.4 nm channel of the Atmospheric Imaging Assembly on board SDO, the full velocity and propagation direction for each of the eight CMEs are derived. We find a strong correlation between geomagnetic indices (Kp and Dst) and the angle between the CME propagation direction and the Sun–Earth line, suggesting that Sun-as-a-star spectroscopic observations at extreme-ultraviolet wavelengths can potentially help to improve the prediction accuracy of the geoeffectiveness of CMEs. Moreover, an analysis of synthesized long-exposure Sun-as-a-star spectra implies that it is possible to detect CMEs from other stars through blue-wing asymmetries or blueshifts of spectral lines.
Reliability Analysis of Monitoring System for Extraterrestrial Habitat using CTMC and Empirical Evaluation

Among the various required resources for this civilization, the habitat is one of the crucial resources to live on Mars. Such an extraterrestrial habitat is designed to provide a safe place to live during the initial missions. It is equipped with monitoring and life support systems to ensure the astronauts' safety. In this work, we present a robust monitoring system with a use case for extraterrestrial habitats. Similar to a typical monitoring system, it consists of sensor nodes and a gateway connected through a wireless communication channel. In our system, we introduce robustness to various failures that can occur after deployment, namely, board, sensor and gateway failure, in the form of redundancy. For each failure, the problem is tackled differently. For the first two types, we use additional hardware as backup, while for the last type, we use neighbouring devices as backups. The backup devices function as replacements for the failed component, which helps the system to collect the data which would otherwise be lost. We evaluate how much the performance of the system improves by using backup devices. We use a Continuous-Time Markov chain for the theoretical evaluation and an experimental setup that includes the hardware prototype for the empirical evaluation. We also analyze the effect of a simple medium access mechanism on the system's performance in the presence of heavy noise on the channel. Based on our requirements, we use a simple custom medium access control (MAC) algorithm called Slotted-ALOHA with Random Back-off (SARB) to make communication reliable. We demonstrate that around $30-34\%$ of the packets are recovered, with the use of backup devices (redundancy), which would otherwise be lost in case of failures. We also demonstrate that the system's performance improves by $3.8-13.2\%$ with the use of a simple medium access technique (SARB).
Electronic band structure change with structural transition of buckled Au2X monolayers induced by strain.

This study investigates the strain-induced structural transitions of η ↔ θ and the changes in electronic band structures of Au2X (X = S, Se, Te, Si, Ge) and Au4SSe. We focus on Au2S monolayers, which can form multiple meta-stable monolayers theoretically, including η-Au2S, a buckled penta-monolayer composed of a square Au lattice and S adatoms. The θ-Au2S is regarded as a distorted structure of η-Au2S. Based on density functional theory (DFT) calculations using a generalized gradient approximation, the conduction and the valence bands of θ-Au2S intersect at the Γ point, leading to linear dispersion, whereas η-Au2S has a band gap of 1.02 eV. The conduction band minimum depends on the specific Au-Au bond distance, while the valence band maximum depends on both Au-S and Au-Au interactions. The band gap undergoes significant changes during the η ↔ θ phase transition of Au2S induced by applying tensile or compressive in-plane biaxial strain to the lattice. Moreover, substituting S atoms with other elements alters the electronic band structures, resulting in a variety of physical properties without disrupting the fundamental Au lattice network. Therefore, the family of Au2X monolayers holds potential as materials for atomic scale network devices.
A Near Time-optimal Population Protocol for Self-stabilizing Leader Election on Rings with a Poly-logarithmic Number of States

We propose a self-stabilizing leader election (SS-LE) protocol on ring networks in the population protocol model. Given a rough knowledge ψ = ⌈log n⌉ + O(1) on the population size n, the proposed protocol lets the population reach a safe configuration within O(n2 log n) steps with high probability starting from any configuration. Thereafter, the population keeps the unique leader forever. Since no protocol solves SS-LE in o(n2) steps with high probability, the convergence time is near-optimal: the gap is only an O(log n) multiplicative factor. This protocol uses only polylog(n) states. There exist two state-of-the-art algorithms in current literature that solve SS-LE on ring networks. The first algorithm uses a polynomial number of states and solves SS-LE in O(n2) steps, whereas the second algorithm requires exponential time but it uses only a constant number of states. Our proposed algorithm provides an excellent middle ground between these two.
A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions

As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact together. This paper discusses the significance of affective computing, as well as its ideas, conceptions, methods, and outcomes. By using approaches of ML and XR, we survey and discuss recent methodologies in affective computing. We survey the state-of-the-art approaches along with current affective data resources. Further, we discuss various applications where affective computing has a significant impact, which will aid future scholars in gaining a better understanding of its significance and practical relevance.
Effects of a finite-sized detector on the OTF of confocal fluorescent microscopy

For a confocal fluorescent microscope with a finite sized circular detector, the 3-D optical transfer function (OTF) for a thick object has been developed without using Stockseth's approximation, and the in-focus (2-D) OTF for a thin object has also been derived. Both OTFs were numerically calculated to reveal the dependence on the radius of the detector. The results show that the OTFs have negative values when the radius of the detector exceeds certain magnitudes and approach those of a conventional incoherent microscope as the radius of the detector becomes infinite
Probing Topological Charge of Discrete Vortices

Discrete vortex, formed by a one-dimensional (1D) ring array of lasers, contains high output power as compared to a conventional continuous vortex, therefore, has attracted considerable interest due to widespread applications in various fields. We present a method for probing the magnitude and sign of the topological charge (TC) of an unknown discrete vortex, by analyzing the interference pattern of a 1D ring array of lasers. The interference pattern of an unknown discrete vortex with TC$\neq 0$ is averaged with the interference pattern of TC= 0, which gives rise to a variation in the fringe visibility as a function of laser number (j) in a 1D ring array. The number of dips observed in the fringe visibility curve is found to be proportional to the magnitude of TC of a discrete vortex. The sign of TC is determined by averaging the interference patterns of unknown discrete vortex (TC$\neq 0$) with known TC= +1. The number of dips in the fringe visibility curve decreases by one for a positive TC, and increases by one for a negative TC. Further, we have verified our method against the phase disorder, and it is found that the phase disorder does not influence an accurate determination of TC of a discrete vortex. The working principle as well as numerical and experimental results are presented for the discrete vortices with TC from small to large values. An excellent agreement between the experimental results and numerical simulations is found. Our method can be useful in the applications of discrete vortices.
Analytical solution for optimal protocols of weak drivings

One of the main objectives of science is the recognition of a general pattern in a particular phenomenon in some particular regime. In this work, this is achieved with the analytical expression for the optimal protocol that minimizes the thermodynamic work and its variance for finite-time, isothermal, and weak processes. The method that solves the Euler-Lagrange integral equation is quite general and depends only on the time-reversal symmetry of the optimal protocol. The solution is composed of a straight line with jumps at the boundaries and impulse-like terms. Already known results are deduced, and many new examples are solved corroborating this pattern. Slowly-varying and sudden cases are deduced in their appropriate asymptotic limits. Comparison with numerical procedures is limited by the nonavailability of the present methods of the literature to produce solutions in the space of distributions.
Mobile-Env: A Universal Platform for Training and Evaluation of Mobile Interaction

The interaction platform plays a crucial role in the recent advancement of the control and decision domains like game playing and embodied intelligence. However, there is still a lack of a satisfactory platform for the information user interface (InfoUI) interaction. The proposed InfoUI comprises not only the plain text information, but the multimodal contents and a few spatial structures with styles as well. To help the research of InfoUI interaction, a novel platform Mobile-Env is presented in this paper. The Mobile-Env platform is designed to be ﬂexible, adaptable, and easily-extended. Based on Mobile-Env, an InfoUI task set is then built for a demonstration and evaluation. An agent based on the large-scale language model (LLM) is tested on the task set. The experiment results demonstrate the great potential of the LLM to do text understanding and matching and, meanwhile, reveal the necessity of a better mechanism of interaction feedback and exploration. Several new discussions are conducted as well. A demo video is available at https://youtu.be/gKV6KZYwxGY . The code repository is available at https: //github.com/X-LANCE/Mobile-Env . The proposed WikiHow task set is made public at https://huggingface.co/datasets/zdy023/WikiHow-taskset .
In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making

The current literature on AI-advised decision making -- involving explainable AI systems advising human decision makers -- presents a series of inconclusive and confounding results. To synthesize these findings, we propose a simple theory that elucidates the frequent failure of AI explanations to engender appropriate reliance and complementary decision making performance. We argue explanations are only useful to the extent that they allow a human decision maker to verify the correctness of an AI's prediction, in contrast to other desiderata, e.g., interpretability or spelling out the AI's reasoning process. Prior studies find in many decision making contexts AI explanations do not facilitate such verification. Moreover, most tasks fundamentally do not allow easy verification, regardless of explanation method, limiting the potential benefit of any type of explanation. We also compare the objective of complementary performance with that of appropriate reliance, decomposing the latter into the notions of outcome-graded and strategy-graded reliance.