Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Structure-Preserving Relaxation Method for Charged Systems on Unstructured Meshes

This work considers charged systems described by the modified Poisson--Nernst--Planck (PNP) equations, which incorporate ionic steric effects and the Born solvation energy for dielectric inhomogeneity. Solving the steady-state modified PNP equations poses numerical challenges due to the emergence of sharp boundary layers caused by small Debye lengths, particularly when local ionic concentrations reach saturation. To address this, we first reformulate the steady-state problem as a constraint optimization, where the ionic concentrations on unstructured Delaunay nodes are treated as fractional particles moving along edges between nodes. The electric fields are then updated to minimize the objective free energy while satisfying the discrete Gauss's law. We develop a local relaxation method on unstructured meshes that inherently respects the discrete Gauss's law, ensuring curl-free electric fields. Numerical analysis demonstrates that the optimal mass of the moving fractional particles guarantees the positivity of both ionic and solvent concentrations. Additionally, the free energy of the charged system consistently decreases during successive updates of ionic concentrations and electric fields. We conduct numerical tests to validate the expected numerical accuracy, positivity, free-energy dissipation, and robustness of our method in simulating charged systems with sharp boundary layers.
On Solving Close Enough Orienteering Problem with Overlapped Neighborhoods

Close Enough Traveling Salesman Problem (CETSP) is a well-known variant of TSP whereby the agent may complete its mission at any point within a target neighborhood. Heuristics based on overlapped neighborhoods, known as Steiner Zones (SZ), have gained attention in addressing CETSP. While SZs offer effective approximations to the original graph, their inherent overlap imposes constraints on search space, potentially conflicting with global optimization objectives. Here we show how such limitations can be converted into advantages in a Close Enough Orienteering Problem (CEOP) by aggregating prizes across overlapped neighborhoods. We further extend classic CEOP with Non-uniform Neighborhoods (CEOP-N) by introducing non-uniform costs for prize collection. To tackle CEOP and CEOP-N, we develop a new approach featuring a Randomized Steiner Zone Discretization (RSZD) scheme coupled with a hybrid algorithm based on Particle Swarm Optimization (PSO) and Ant Colony System (ACS), CRaSZe-AntS. The RSZD scheme identifies sub-regions for PSO exploration, and ACS determines the discrete visiting sequence. We evaluate the RSZD's discretization performance on CEOP instances derived from established CETSP instances and compare CRaSZe-AntS against the most relevant state-of-the-art heuristic focused on single-neighborhood optimization for CEOP instances. We also compare the performance of the interior search within SZs and the boundary search on individual neighborhoods in the context of CEOP-N. Our experimental results show that CRaSZe-AntS can yield comparable solution quality with significantly reduced computation time compared to the single neighborhood strategy, where we observe an average 140.44% increase in prize collection and a 55.18% reduction in algorithm execution time. CRaSZe-AntS is thus highly effective in solving emerging CEOP-N, examples of which include truck-and-drone delivery scenarios.
A Holistic Evaluation of Piano Sound Quality

This paper aims to develop a holistic evaluation method for piano sound quality to assist in purchasing decisions. Unlike previous studies that focused on the effect of piano performance techniques on sound quality, this study evaluates the inherent sound quality of different pianos. To derive quality evaluation systems, the study uses subjective questionnaires based on a piano sound quality dataset. The method selects the optimal piano classification models by comparing the fine-tuning results of different pre-training models of Convolutional Neural Networks (CNN). To improve the interpretability of the models, the study applies Equivalent Rectangular Bandwidth (ERB) analysis. The results reveal that musically trained individuals are better able to distinguish between the sound quality differences of different pianos. The best fine-tuned CNN pre-trained backbone achieves a high accuracy of 98.3\% as the piano classifier. However, the dataset is limited, and the audio is sliced to increase its quantity, resulting in a lack of diversity and balance, so we use focal loss to reduce the impact of data imbalance. To optimize the method, the dataset will be expanded, or few-shot learning techniques will be employed in future research.
Differential Game Strategies for Social Networks with Self-Interested Individuals

A social network population engages in collective actions as a direct result of forming a particular opinion. The strategic interactions among the individuals acting independently and selfishly naturally portray a noncooperative game. Nash equilibrium allows for self-enforcing strategic interactions between selfish and self-interested individuals. This paper presents a differential game approach to the opinion formation problem in social networks to investigate the evolution of opinions as a result of a Nash equilibrium. The opinion of each individual is described by a differential equation, which is the continuous-time Hegselmann-Krause model for opinion dynamics with a time delay in input. The objective of each individual is to seek optimal strategies for her own opinion evolution by minimizing an individual cost function. Two differential game problems emerge, one for a population that is not stubborn and another for a population that is stubborn. The open-loop Nash equilibrium actions and their associated opinion trajectories are derived for both differential games using Pontryagin's principle. Additionally, the receding horizon control scheme is used to practice feedback strategies where the information flow is restricted by fixed and complete social graphs as well as the second neighborhood concept. The game strategies were executed on the well-known Zachary's Karate Club social network. The resulting opinion trajectories associated with the game strategies showed consensus, polarization, and disagreement in final opinions.
Accelerated Affine-Invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes

Recent papers have shown that the Frank-Wolfe algorithm (FW) with open-loop step-sizes exhibits rates of convergence faster than the iconic $\mathcal{O}(t^{-1})$ rate. In particular, when the minimizer of a strongly convex function over a polytope lies in the relative interior of a feasible region face, the FW with open-loop step-sizes $\eta_t = \frac{\ell}{t+\ell}$ for $\ell \in \mathbb{N}_{\geq 2}$ has accelerated convergence $\mathcal{O}(t^{-2})$ in contrast to the rate $\Omega(t^{-1-\epsilon})$ attainable with more complex line-search or short-step step-sizes. Given the relevance of this scenario in data science problems, research has grown to explore the settings enabling acceleration in open-loop FW. However, despite FW's well-known affine invariance, existing acceleration results for open-loop FW are affine-dependent. This paper remedies this gap in the literature by merging two recent research trajectories: affine invariance (Wirth et al., 2023b) and open-loop step-sizes (Pena, 2021). In particular, we extend all known non-affine-invariant convergence rates for FW with open-loop step-sizes to affine-invariant results.
Measurement of ultrashort bi-photon correlation times with an integrated two-colour broadband SU(1,1)-interferometer

The bi-photon correlation time, a measure for the conditional uncertainty in the temporal arrival of two photons from a photon pair source, is a key performance identifier for many quantum spectroscopy applications, with shorter correlation times typically yielding better performance. Furthermore, it provides fundamental insight into the effects of dispersion on the bi-photon state. Here, we retrieve ultrashort bi-photon correlation times of around $100\,\mathrm{fs}$ by measuring simultaneously spectral and temporal interferograms at the output of an SU(1,1) interferometer based on an integrated broadband parametric down-conversion source in a $\mathrm{Ti:LiNbO}_3$ waveguide
Text Embeddings Reveal (Almost) As Much As Text

How much private information do text embeddings reveal about the original text? We investigate the problem of embedding \textit{inversion}, reconstructing the full text represented in dense text embeddings. We frame the problem as controlled generation: generating text that, when reembedded, is close to a fixed point in latent space. We find that although a na\"ive model conditioned on the embedding performs poorly, a multi-step method that iteratively corrects and re-embeds text is able to recover $92\%$ of $32\text{-token}$ text inputs exactly. We train our model to decode text embeddings from two state-of-the-art embedding models, and also show that our model can recover important personal information (full names) from a dataset of clinical notes. Our code is available on Github: \href{https://github.com/jxmorris12/vec2text}{github.com/jxmorris12/vec2text}.
River rehabilitation and fish: the challenge of initiating ecological recovery

The failure of river rehabilitation projects is often reported in the literature. One possible reason for this failure is the insufficient consideration of factors degrading riverine ecosystems at large spatio-temporal scales. A precedent analysis of the evolution and significance of these factors at the watershed level is proposed as a prerequisite for a successful rehabilitation project. Based on a watershed-scale approach, we investigated the current and historical states of the fish assemblage and of relevant abiotic factors in the river Rhone, a seventh-order stream in Switzerland scheduled for large-scale rehabilitation. Recent field data gathered by electrofishing and habitat mapping were analysed by means of a mixed model approach and were qualitatively compared to historical information derived from topographic maps and documentary sources. The length of the entire active channel has been reduced by 45 % (102 km) since 1850, representing a significant diminution in lateral connectivity. Our recent fish survey revealed a depleted species set, with only two of 19 historically documented species found. The density of brown trout was generally low, but positively correlated with the presence of cover. Thus, morphological improvements, e.g. through local river widening, offer extensive potential for the restoration of native fish assemblages, but will probably only be successful in combination with a more natural hydrological regime.
Auto-survey Challenge

We present a novel platform for evaluating the capability of Large Language Models (LLMs) to autonomously compose and critique survey papers spanning a vast array of disciplines including sciences, humanities, education, and law. Within this framework, AI systems undertake a simulated peer-review mechanism akin to traditional scholarly journals, with human organizers serving in an editorial oversight capacity. Within this framework, we organized a competition for the AutoML conference 2023. Entrants are tasked with presenting stand-alone models adept at authoring articles from designated prompts and subsequently appraising them. Assessment criteria include clarity, reference appropriateness, accountability, and the substantive value of the content. This paper presents the design of the competition, including the implementation baseline submissions and methods of evaluation.
Balancing utility and cognitive cost in social representation

To successfully navigate its environment, an agent must construct and maintain representations of the other agents that it encounters. Such representations are useful for many tasks, but they are not without cost. As a result, agents must make decisions regarding how much information they choose to store about the agents in their environment. Using selective social learning as an example task, we motivate the problem of finding agent representations that optimally trade off between downstream utility and information cost, and illustrate two example approaches to resource-constrained social representation.
Can LLMs Demystify Bug Reports?

Bugs are notoriously challenging: they slow down software users and result in time-consuming investigations for developers. These challenges are exacerbated when bugs must be reported in natural language by users. Indeed, we lack reliable tools to automatically address reported bugs (i.e., enabling their analysis, reproduction, and bug fixing). With the recent promises created by LLMs such as ChatGPT for various tasks, including in software engineering, we ask ourselves: What if ChatGPT could understand bug reports and reproduce them? This question will be the main focus of this study. To evaluate whether ChatGPT is capable of catching the semantics of bug reports, we used the popular Defects4J benchmark with its bug reports. Our study has shown that ChatGPT was able to demystify and reproduce 50% of the reported bugs. ChatGPT being able to automatically address half of the reported bugs shows promising potential in the direction of applying machine learning to address bugs with only a human-in-the-loop to report the bug.
Kerr black hole shadows from the axion-photon coupling

We have investigated the motion for photons in the Kerr black hole spacetime under the axion-photon coupling. The birefringence phenomena arising from the axion-photon coupling can be negligible in the weak coupling approximation because the leading-order contributions to the equations of motion come from the square term of the coupling parameter. We find that the coupling parameter makes the size of shadows slightly increase for arbitrary spin parameter. For the rapid rotating black hole case with a larger coupling, we find that there exist a pedicel-like structure appeared in the left of the D-type like shadows.Comparing the shadow size of the Kerr black hole with the shadow size of the Sgr A* and M87* black holes, we find that there is room for such a theoretical model of the axion-photon coupling.
Edge-Locating Coloring of Graphs

An edge-locating coloring of a simple connected graph $G$ is a partition of its edge set into matchings such that the vertices of $G$ are distinguished by the distance to the matchings. The minimum number of the matchings of $G$ that admits an edge-locating coloring is the edge-locating chromatic number of $G$, and denoted by $\chi'_L(G)$. In this paper we initiate to introduce the concept of edge-locating coloring and determine the exact values $\chi'_L(G)$ of some custom graphs. The graphs $G$ with $\chi'_L(G)\in \{2,m\}$ are characterized, where $m$ is the size of $G$. We investigate the relationship between order, diameter, and edge-locating chromatic number of $G$. For a complete graph $K_n$, we obtain the exact values of $\chi'_L(K_n)$ and $\chi'_L(K_n-M)$, where $M$ is a maximum matching; indeed this result is also extended for any graph. We will determine the edge-locating chromatic number of join graph $G+H$, where $G$ and $H$ are some well-known graphs. In particular, for any graph $G$, we show a relationship between $\chi'_L(G+K_1)$ and $\Delta(G)$. We investigate the edge-locating chromatic number of trees and present a characterization bound for any tree in terms of maximum degree, number of leaves, and the support vertices of trees. Finally, we prove that any edge-locating coloring of a graph is an edge distinguishing coloring.
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix

Fitness applications are commonly used to monitor activities within the gym, but they often fail to automatically track indoor activities inside the gym. This study proposes a model that utilizes pose estimation combined with a novel data augmentation method, i.e., rotation matrix. We aim to enhance the classification accuracy of activity recognition based on pose estimation data. Through our experiments, we experiment with different classification algorithms along with image augmentation approaches. Our findings demonstrate that the SVM with SGD optimization, using data augmentation with the Rotation Matrix, yields the most accurate results, achieving a 96% accuracy rate in classifying five physical activities. Conversely, without implementing the data augmentation techniques, the baseline accuracy remains at a modest 64%.
Detecting Semantic Conflicts using Static Analysis

Version control system tools empower developers to independently work on their development tasks. These tools also facilitate the integration of changes through merging operations, and report textual conflicts. However, when developers integrate their changes, they might encounter other types of conflicts that are not detected by current merge tools. In this paper, we focus on dynamic semantic conflicts, which occur when merging reports no textual conflicts but results in undesired interference - causing unexpected program behavior at runtime. To address this issue, we propose a technique that explores the use of static analysis to detect interference when merging contributions from two developers. We evaluate our technique using a dataset of 99 experimental units extracted from merge scenarios. The results provide evidence that our technique presents significant interference detection capability. It outperforms, in terms of F1 score and recall, previous methods that rely on dynamic analysis for detecting semantic conflicts, but these show better precision. Our technique precision is comparable to the ones observed in other studies that also leverage static analysis or use theorem proving techniques to detect semantic conflicts, albeit with significantly improved overall performance.
On Temporal References in Emergent Communication

As humans, we use linguistic elements referencing time, such as before or tomorrow, to easily share past experiences and future predictions. While temporal aspects of the language have been considered in computational linguistics, no such exploration has been done within the field of emergent communication. We research this gap, providing the first reported temporal vocabulary within emergent communication literature. Our experimental analysis shows that a different agent architecture is sufficient for the natural emergence of temporal references, and that no additional losses are necessary. Our readily transferable architectural insights provide the basis for the incorporation of temporal referencing into other emergent communication environments.
2+1D symmetry-topological-order from local symmetric operators in 1+1D

A generalized symmetry (defined by the algebra of local symmetric operators) can go beyond group or higher group description. A theory of generalized symmetry (up to holo-equivalence) was developed in terms of symmetry-TO -- a bosonic topological order (TO) with gappable boundary in one higher dimension. We propose a general method to compute the 2+1D symmetry-TO from the local symmetric operators in 1+1D systems. Our theory is based on the commutant patch operators, which are extended operators constructed as products and sums of local symmetric operators. A commutant patch operator commutes with all local symmetric operators away from its boundary. We argue that topological invariants associated with anyon diagrams in 2+1D can be computed as contracted products of commutant patch operators in 1+1D. In particular, we give concrete formulae for several topological invariants in terms of commutant patch operators. Topological invariants computed from patch operators include those beyond modular data, such as the link invariants associated with the Borromean rings and the Whitehead link. These results suggest that the algebra of commutant patch operators is described by 2+1D symmetry-TO. Based on our analysis, we also argue briefly that the commutant patch operators would serve as order parameters for gapped phases with finite symmetries.
Twisted Coupled Wire Model for moir\'e Sliding Luttinger Liquid

Recent experiments in twisted bilayer WTe$_2$ revealed the existence of anisotropic Luttinger liquid behavior. To generically characterize such anisotropic twisted bilayer systems, we study a model of twisted bilayer of 2D arrays of coupled wires, which effectively form an array of coupled moir\'e wires. We solve the model by transfer matrix method, and identify quasi-1D electron bands in the system at small twist angles. With electron interactions added, we show that the moir\'e wires have an effective Luttinger parameter $g_\text{eff}$ much lower than that of the microscopic wires. This leads to a sliding Luttinger liquid (SLL) temperature regime, in which power-law current voltage relations arise. For parameters partly estimated from WTe$_2$, a microscopic interaction $U\sim0.7$eV yields a temperature regime of SLL similar to that in the WTe$_2$ experiments.
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images

Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue to be serious global health concerns that affect millions of people worldwide. In medical practice, chest X-ray examinations have emerged as the norm for diagnosing diseases, particularly chest infections such as COVID-19. Paramedics and scientists are working intensively to create a reliable and precise approach for early-stage COVID-19 diagnosis in order to save lives. But with a variety of symptoms, medical diagnosis of these disorders poses special difficulties. It is essential to address their identification and timely diagnosis in order to successfully treat and prevent these illnesses. In this research, a multiclass classification approach using state-of-the-art methods for deep learning and image processing is proposed. This method takes into account the robustness and efficiency of the system in order to increase diagnostic precision of chest diseases. A comparison between a brand-new convolution neural network (CNN) and several transfer learning pre-trained models including VGG19, ResNet, DenseNet, EfficientNet, and InceptionNet is recommended. Publicly available and widely used research datasets like Shenzen, Montogomery, the multiclass Kaggle dataset and the NIH dataset were used to rigorously test the model. Recall, precision, F1-score, and Area Under Curve (AUC) score are used to evaluate and compare the performance of the proposed model. An AUC value of 0.95 for COVID-19, 0.99 for TB, and 0.98 for pneumonia is obtained using the proposed network. Recall and precision ratings of 0.95, 0.98, and 0.97, respectively, likewise met high standards.