Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of ChatGPT on the test of understanding graphs in kinematics

The well-known artificial intelligence-based chatbot ChatGPT-4 has become able to process image data as input in October 2023. We investigated its performance on the Test of Understanding Graphs in Kinematics to inform the physics education community of the current potential of using ChatGPT in the education process, particularly on tasks that involve graphical interpretation. We found that ChatGPT, on average, performed similarly to students taking a high-school level physics course, but with important differences in the distribution of the correctness of its responses, as well as in terms of the displayed"reasoning"and"visual"abilities. While ChatGPT was very successful at proposing productive strategies for solving the tasks on the test and expressed correct"reasoning"in most of its responses, it had difficulties correctly"seeing"graphs. We suggest that, based on its performance, caution and a critical approach are needed if one intends to use it in the role of a tutor, a model of a student, or a tool for assisting vision-impaired persons in the context of kinematics graphs.
CONTRIBUCIÓN HÍDRICA DE LA CAPTACIÓN DE NIEBLA AL BALANCE DE UN BOSQUE DE LAURISILVA EN EL PARQUE NACIONAL DE GARAJONAY

Fog captured by vegetation is often considered an additional source of water in the laurel forest ecosystems of the Canary Islands. However, there are not yet appropriate studies that quantify to what extent such a contribution is relevant to the water budget of the forest. From a two-year time series of micrometeorological measurements in a selected watershed of the Garajonay National Park (La Gomera), a study has been carried out in order to quantify the contribution of fog water captured by vegetation to the vadose zone. Making use of different models which describe the physical processes involved, such as the impaction of fog water drops onto cylindrical elements (needle-like leaves), we obtained estimations which suggest that contribution of fog water captured by vegetation may be relevant to the water status of the vadose zone under certain circumstances. This additional source of water is approximately 20-45% of rainfall and it is distributed along the year, being of importance during the dry summer season, within areas with high density of needle-like trees (optimal for fog water capture), and with favourable conditions for fog precipitation (high altitude and orientation towards the predominant trade winds direction). Finally the contribution of fog water to the water balance of the selected watershed is quantified in relative terms with respect to intercepted rainfall, potential Penman- Monteith evapotranspiration, and transpiration measured with sap flow sensors.
Throughput Maximization in Multi-Band Optical Networks with Column Generation

Multi-band transmission is a promising technical direction for spectrum and capacity expansion of existing optical networks. Due to the increase in the number of usable wavelengths in multi-band optical networks, the complexity of resource allocation problems becomes a major concern. Moreover, the transmission performance, spectrum width, and cost constraint across optical bands may be heterogeneous. Assuming a worst-case transmission margin in U, L, and C-bands, this paper investigates the problem of throughput maximization in multi-band optical networks, including the optimization of route, wavelength, and band assignment. We propose a low-complexity decomposition approach based on Column Generation (CG) to address the scalability issue faced by traditional methodologies. We numerically compare the results obtained by our CG-based approach to an integer linear programming model, confirming the near-optimal network throughput. Our results also demonstrate the scalability of the CG-based approach when the number of wavelengths increases, with the computation time in the magnitude order of 10 s for cases varying from 75 to 1200 wavelength channels per link in a 14-node network. Code of this publication is available at github.com/cchen000/CG-Multi-Band.
Poincare group spin networks

Spin network technique is usually generalized to relativistic case by changing $SO(4)$ group -- Euclidean counterpart of the Lorentz group -- to its universal spin covering $SU(2)\times SU(2)$, or by using the representations of $SO(3,1)$ Lorentz group. We extend this approach by using inhomogeneous Lorentz group $\mathcal{P}=SO(3,1)\rtimes \mathbb{R}^4$, which results in the simplification of the spin network technique. The labels on the network graph corresponding to the subgroup of translations $\mathbb{R}^4$ make the intertwiners into the products of $SU(2)$ parts and the energy-momentum conservation delta functions. This maps relativistic spin networks to usual Feynman diagrams for the matter fields.
Progenitor constraint using line ratios of the CNO elements in supernova remnants

Unveiling the nature of progenitors is crucial for understanding the origin and the mechanism of core-collapse and thermonuclear supernovae (SNe). While several methods have been developed to derive stellar properties so far, many questions remain poorly understood. In this paper we demonstrate an observational approach to constrain progenitors of supernova remnants (SNRs) using abundances of carbon (C), nitrogen (N), and oxygen (O) in shock-heated circumstellar material (CSM). Our calculations with stellar evolution codes indicate that a total amount of these CNO elements will provide a more sensitive determination of the progenitor masses than the conventional method based on ejecta abundances. If the CNO lines (particularly those of C and N) are detected and measured their abundance ratios accurately, they can provide relatively robust constraint on the progenitor mass (and in some cases the rotation velocity) of SNRs. Since our method requires a better energy resolution and larger effective area in the soft X-ray band ($<1$~keV), XRISM launched on September 7, 2023 and next-generation microcalorimeter missions such as Athena, Lynx, LEM, and HUBS will bring a new insight into link between the progenitors and their remnants.
Agnostic Membership Query Learning with Nontrivial Savings: New Results, Techniques

(Abridged) Designing computationally efficient algorithms in the agnostic learning model (Haussler, 1992; Kearns et al., 1994) is notoriously difficult. In this work, we consider agnostic learning with membership queries for touchstone classes at the frontier of agnostic learning, with a focus on how much computation can be saved over the trivial runtime of 2^n$. This approach is inspired by and continues the study of ``learning with nontrivial savings'' (Servedio and Tan, 2017). To this end, we establish multiple agnostic learning algorithms, highlighted by: 1. An agnostic learning algorithm for circuits consisting of a sublinear number of gates, which can each be any function computable by a sublogarithmic degree k polynomial threshold function (the depth of the circuit is bounded only by size). This algorithm runs in time 2^{n -s(n)} for s(n) \approx n/(k+1), and learns over the uniform distribution over unlabelled examples on \{0,1\}^n. 2. An agnostic learning algorithm for circuits consisting of a sublinear number of gates, where each can be any function computable by a \sym^+ circuit of subexponential size and sublogarithmic degree k. This algorithm runs in time 2^{n-s(n)} for s(n) \approx n/(k+1), and learns over distributions of unlabelled examples that are products of k+1 arbitrary and unknown distributions, each over \{0,1\}^{n/(k+1)} (assume without loss of generality that k+1 divides n).
On the dynamics of an epidemic patch model with mass-action transmission mechanism and asymmetric dispersal patterns

This paper examines an epidemic patch model with mass-action transmission mechanism and asymmetric connectivity matrix. Results on the global dynamics of solutions and the spatial structures of endemic solutions are obtained. In particular, we show that when the basic reproduction number $\mathcal{R}_0$ is less than one and the dispersal rate of the susceptible population $d_S$ is large, the population would eventually stabilize at the disease-free equilibrium. However, the disease may persist if $d_S$ is small, even if $\mathcal{R}_0<1$. In such a scenario, explicit conditions on the model parameters that lead to the existence of multiple endemic equilibria are identified. These results provide new insights into the dynamics of infectious diseases in multi-patch environments. Moreover, results in [27], which is for the same model but with symmetric connectivity matrix, are generalized and improved.
Accretion-modified Stars in Accretion Disks of Active Galactic Nuclei: The Low-luminosity Cases and an Application to Sgr A*

In this paper, we investigate the astrophysical processes of stellar-mass black holes (sMBHs) embedded in advection-dominated accretion flows (ADAFs) of supermassive black holes (SMBHs) in low-luminosity active galactic nuclei. The sMBH is undergoing Bondi accretion at a rate lower than the SMBH. Outflows from the sMBH-ADAF dynamically interact with their surroundings and form a cavity inside the SMBH-ADAF, thereby quenching the accretion onto the sMBH. Rejuvenation of the Bondi accretion is rapidly done by turbulence. These processes give rise to quasi-periodic episodes of sMBH activities and create flickerings from relativistic jets developed by the Blandford–Znajek mechanism if the sMBH is maximally rotating. Accumulating successive sMBH-outflows trigger a viscous instability of the SMBH-ADAF, leading to a flare following a series of flickerings. Recently, the similarity of near-infrared flare’s orbits has been found by GRAVITY/VLTI astrometric observations of Sgr A∗: their loci during the last 4 yr consist of a ring in agreement with the well-determined SMBH mass. We apply the present model to Sgr A*, which shows quasi-periodic flickerings. An sMBH of ∼40M ⊙ is preferred orbiting around the central SMBH of Sgr A* from fitting radio to X-ray continuum. Such an extreme mass ratio inspiraling provides an excellent laboratory for LISA/Taiji/Tianqin detection of mHz gravitational waves with strains of ∼10−17, as well as their polarization.
A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases

Enterprise applications of Large Language Models (LLMs) hold promise for question answering on enterprise SQL databases. However, the extent to which LLMs can accurately respond to enterprise questions in such databases remains unclear, given the absence of suitable Text-to-SQL benchmarks tailored to enterprise settings. Additionally, the potential of Knowledge Graphs (KGs) to enhance LLM-based question answering by providing business context is not well understood. This study aims to evaluate the accuracy of LLM-powered question answering systems in the context of enterprise questions and SQL databases, while also exploring the role of knowledge graphs in improving accuracy. To achieve this, we introduce a benchmark comprising an enterprise SQL schema in the insurance domain, a range of enterprise queries encompassing reporting to metrics, and a contextual layer incorporating an ontology and mappings that define a knowledge graph. Our primary finding reveals that question answering using GPT-4, with zero-shot prompts directly on SQL databases, achieves an accuracy of 16%. Notably, this accuracy increases to 54% when questions are posed over a Knowledge Graph representation of the enterprise SQL database. Therefore, investing in Knowledge Graph provides higher accuracy for LLM powered question answering systems.
Coupling haze and cloud microphysics in WASP-39b’s atmosphere based on JWST observations

We present a study on the coupling of haze and clouds in the atmosphere of WASP-39b. We developed a cloud microphysics model simulating the formation of Na2S and MgSiO3 condensates over photochemical hazes in gas giant atmospheres. We apply this model to WASP-39b, recently observed with the JWST to study how these heterogeneous components may affect the transit spectrum. We simulate both morning and evening terminators independently and average their transit spectra. While MgSiO3 formation has negligible impact on the spectrum, Na2S condensates produce gray opacities in the water band, in agreement with HST and JWST observations. Moreover, the formation of Na2S on the morning side depletes the atmosphere of its sodium content, decreasing the strength of the Na line. Combining morning and evening profiles results in a good fit of the Na observations. These nominal results assume a small Na2S/haze contact angle (5.7○). Using a larger value (61○) reduces the cloud density and opacity, but the effect on the Na profile and spectral line remains identical. In addition, the presence of haze in the upper atmosphere reproduces the UV-visible slope observed in the HST and VLT data and contributes to the opacity between the water bands at wavelengths below 2 microns. The averaged spectra are rather insensitive to the variation of eddy diffusion and haze mass flux tested in this study, though the UV-visible slope, probing the haze layer above the clouds, is affected. Finally, our disequilibrium chemistry model, including photochemistry, reproduces the SO2 and CO2 absorption features observed.
Asymmetric Contrastive Multimodal Learning for Advancing Chemical Understanding

The versatility of multimodal deep learning holds tremendous promise for advancing scientific research and practical applications. As this field continues to evolve, the collective power of cross-modal analysis promises to drive transformative innovations, leading us to new frontiers in chemical understanding and discovery. Hence, we introduce Asymmetric Contrastive Multimodal Learning (ACML) as a novel approach tailored for molecules, showcasing its potential to advance the field of chemistry. ACML harnesses the power of effective asymmetric contrastive learning to seamlessly transfer information from various chemical modalities to molecular graph representations. By combining pre-trained chemical unimodal encoders and a shallow-designed graph encoder, ACML facilitates the assimilation of coordinated chemical semantics from different modalities, leading to comprehensive representation learning with efficient training. This innovative framework enhances the interpretability of learned representations and bolsters the expressive power of graph neural networks. Through practical tasks such as isomer discrimination and uncovering crucial chemical properties for drug discovery, ACML exhibits its capability to revolutionize chemical research and applications, providing a deeper understanding of chemical semantics of different modalities.
Multicomponent rendezvous of cofilin, profilin and twinfilin at the actin filament barbed end

Cellular actin dynamics result from collective action of hundreds of regulatory proteins, majority of which target actin filaments at their barbed ends. Three key actin binding proteins - profilin, cofilin and twinfilin individually depolymerize filament barbed ends. Notwithstanding recent leaps in our understanding of their individual action, how they collectively regulate filament dynamics remains an open question. In absence of direct and simultaneous visualization of these proteins at barbed ends, gaining mechanistic insights has been challenging. We have here investigated multicomponent dynamics of profilin, cofilin and twinfilin using a hybrid approach that combines high throughput single filament experiments with theory. We discovered that while twinfilin competes with profilin, it promotes binding of cofilin to filament sides. Interestingly, contrary to previous expectations, we found that profilin and cofilin can simultaneously bind the same filament barbed end resulting in its accelerated depolymerization. Our study reveals that pair-wise interactions can effectively capture depolymerization dynamics in simultaneous presence of all three proteins. We thus believe that our approach of employing a theory-experiment dialog can potentially help decipher multicomponent regulation of actin dynamics.
Efficient Generation of Multimodal Fluid Simulation Data

In this work, we introduce an efficient generation procedure to produce synthetic multi-modal datasets of fluid simulations. The procedure can reproduce the dynamics of fluid flows and allows for exploring and learning various properties of their complex behavior, from distinct perspectives and modalities. We employ our framework to generate a set of thoughtfully designed training datasets, which attempt to span specific fluid simulation scenarios in a meaningful way. The properties of our contributions are demonstrated by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets. Our contribution aims to fulfill the community's need for standardized training data, fostering more reproducibile and robust research.
Sparse Attention-Based Neural Networks for Code Classification

Categorizing source codes accurately and efficiently is a challenging problem in real-world programming education platform management. In recent years, model-based approaches utilizing abstract syntax trees (ASTs) have been widely applied to code classification tasks. Many of these methods start by parsing source code into abstract syntax trees, followed by encoding them as sequences for input into temporal models or Transformer models for subsequent processing. However, existing methods suffer from issues such as loss of structural information and slow training speeds. In response to these challenges, we introduce an approach named the Sparse Attention-based neural network for Code Classification (SACC) in this paper. The approach involves two main steps: In the first step, source code undergoes syntax parsing and preprocessing. The generated abstract syntax tree is split into sequences of subtrees and then encoded using a recursive neural network to obtain a high-dimensional representation. This step simultaneously considers both the logical structure and lexical level information contained within the code. In the second step, the encoded sequences of subtrees are fed into a Transformer model that incorporates sparse attention mechanisms for the purpose of classification. This method efficiently reduces the computational cost of the self-attention mechanisms, thus improving the training speed while preserving effectiveness. Our work introduces a carefully designed sparse attention pattern that is specifically designed to meet the unique needs of code classification tasks. This design helps reduce the influence of redundant information and enhances the overall performance of the model. Finally, we also deal with problems in previous related research, which include issues like incomplete classification labels and a small dataset size. We annotated the CodeNet dataset with algorithm-related labeling categories, which contains a significantly large amount of data. Extensive comparative experimental results demonstrate the effectiveness and efficiency of SACC for the code classification tasks.
Fragmentation of Colliding Liquid Rims

We present direct numerical simulations of the splashing process between two cylindrical liquid rims. This belongs to a class of impact and collision problems with a wide range of applications in science and engineering, and motivated here by splashing processes in breaking ocean waves. Interfacial perturbations with a truncated white noise frequency profile are introduced to the rims before their collision, whose subsequent morphological development is simulated by solving the two-phase incompressible Navier-Stokes equation with the adaptive mesh refinement (AMR) technique, within the Basilisk software environment. We first derive analytical solutions predicting the unsteady interfacial and velocity profiles of the expanding sheet forming between the two rims, and develop scaling laws for the evolution of the lamella rim under capillary deceleration. We then analyse the formation and growth of transverse ligaments ejected from the lamella rims, which we find to originate from the initial corrugated geometry of the perturbed rim surface. Novel scaling models are proposed for predicting the decay of the ligament number density due to the ongoing ligament merging phenomenon, and found to agree well with the numerical results presented here. The role of the mechanism in breaking waves is discussed further and necessary next steps in the problem identified.
The QLBS Model within the presence of feedback loops through the impacts of a large trader

We extend the QLBS model by reformulating via considering a large trader whose transactions leave a permanent impact on the evolution of the exchange rate process and therefore affect the price of contingent claims on such processes. Through a hypothetical limit order book we quantify the exchange rate altered by such transactions. We therefore define the quoted exchange rate process, for which we assume the existence of a postulated hedging strategy. Given the quoted exchange rate and postulated hedging strategy, we find an optimal hedging strategy through batch-mode reinforcement learning given the trader alters the course of the exchange rate process. We assume that the trader has its own concept of fair price and we define our problem as finding the hedging strategy with much lower transaction costs yet delivering a price that well converges to the fair price of the trader. We show our contribution results in an optimal hedging strategy with much lower transaction costs and convergence to the fair price is obtained assuming sensible parameters.
Observational Detection of Higher-Order Secular Perturbations in Tight Hierarchical Triple Stars

In this work, we search for observational evidence of higher-order secular perturbations in three eclipsing binaries. These are slightly eccentric binaries, and they form the inner pairs of tight, compact, hierarchical triple star systems. Simultaneously, we analyze the high-precision satellite (Kepler and TESS) light curves; eclipse timing variations; combined spectral energy distributions (through catalog passband magnitudes); and, where available, radial velocities of KICs 9714358, 5771589, and TIC 219885468. Besides the determination of the robust astrophysical and dynamical properties of the three systems, we find evidence that the observed unusual eclipse timing variations of KIC 9714358 are a direct consequence of the octupole-order secular eccentricity perturbations forced by unusual, resonant behavior between the lines of the apsides of the inner and outer orbital ellipses. We also show that, despite its evident cyclic eclipse depth variations, KIC 5771589 is an almost perfectly coplanar system (to within 0.3∘), and we explain the rapid eclipse depth variations with the grazing nature of the eclipses. Finally, we find that the inner pair of TIC 219885468 consists of two twin stars; hence, in this triple there are no octupole-order three-body perturbations. Moreover, we show that this triple is also coplanar on the same level as the former one, but due to its deep eclipses, it does not exhibit eclipse depth variations. We intend to follow this work up with further analyses and a quantitative comparison of the theoretical and the observed perturbations.