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Cultural evolution theory suggests that prestige bias (whereby individuals preferentially learn from prestigious figures) has played a key role in human ecological success. However, its impact within online environments remains unclear, particularly regarding whether reposts by prestigious individuals amplify diffusion more effectively than reposts by non-influential users. Here, we analyzed over 55 million posts and 520 million reposts on Twitter (currently X) to examine whether users with high influence scores (hg-index) more effectively amplified the reach of others' content. Our findings indicate that posts shared by influencers were more likely to be further shared compared to those shared by non-influencers. This effect persisted over time, especially in viral posts. Moreover, a small group of highly influential users accounted for approximately half of the information flow within repost cascades. These findings demonstrate a prestige bias in information diffusion within digital society, suggesting that cognitive biases shape content spread through reposting., Comment: 4pages, 4Figure (+ Supplementary)

As global warming increases the complexity of weather patterns; the precision of weather forecasting becomes increasingly important. Our study proposes a novel preprocessing method and convolutional autoencoder model developed to improve the interpretation of synoptic weather maps. These are critical for meteorologists seeking a thorough understanding of weather conditions. This model could recognize historical synoptic weather maps that nearly match current atmospheric conditions, marking a significant step forward in modern technology in meteorological forecasting. This comprises unsupervised learning models like VQ-VQE, as well as supervised learning models like VGG16, VGG19, Xception, InceptionV3, and ResNet50 trained on the ImageNet dataset, as well as research into newer models like EfficientNet and ConvNeXt. Our findings proved that, while these models perform well in various settings, their ability to identify comparable synoptic weather maps has certain limits. Our research, motivated by the primary goal of significantly increasing meteorologists' efficiency in labor-intensive tasks, discovered that cosine similarity is the most effective metric, as determined by a combination of quantitative and qualitative assessments to accurately identify relevant historical weather patterns. This study broadens our understanding by shifting the emphasis from numerical precision to practical application, ensuring that our model is effective in theory practical, and accessible in the complex and dynamic field of meteorology.

This study implements and evaluates passive interaction using an autostereoscopic display and ultrasound haptics technology, simulating Garra rufa ("doctor fish") nibbling. When the virtual doctor fish touches the user's hand, ultrasound tactile sensations are presented by spatio-temporal modulation (STM) as an ultrasound focal point orbits around the contact points. A user study evaluated parameters affecting realism, including STM frequency, the number of focal points, ultrasound amplitude, and hand moistening. Comparing several combinations of parameters revealed that representing contact with fewer representative points and setting the frequency of the STM to 10 Hz produced the most realistic experience., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

We report the re-measurement of the $a$-axis spin susceptibility component in an early-stage sample of the spin-triplet superconductor UTe$_2$ with the transition temperature of $T_{\rm SC}$ = 1.6 K. Using Knight-shift measurements along the $b$ axis and at a 10-degree tilt from the $b$ axis towards the $a$ axis, we accurately determined the $a$-axis component without directly measuring the $a$-axis Knight shift. Our results reveal a decrease of approximately 3\% in the $a$-axis spin susceptibility in the superconducting state under $a$-axis magnetic field $\mu_0 H_a \sim 0.1$ T, indicating that the spin susceptibility decreases similarly in both early-stage and ultraclean samples with $T_{\rm SC}$ = 2.1 K. The previously reported absence of the reduction in Knight shift is attributed to the missing of signal from the superconducting region and to the detection of residual signals from the non-superconducting region instead. We also found that the decrease in the $a$-axis spin susceptibility is immediately suppressed with increasing the $a$-axis magnetic field and is estimated to be completely suppressed at around 1.5 T due to superconducting spin rotation., Comment: 5 pages, 3 figures

In this article, we address aquatic environmental monitoring using a fleet of unmanned surface vehicles (USVs). Specifically, we develop an online path generator that provides either of circular or elliptic paths based on the real-time feedback so that the USVs efficiently sample the sensor data over given aquatic environment. To this end, we begin by formulating a novel online path generation problem for a group of Dubins vehicles in the form of cost minimization based on the formulation of persistent coverage control. We then transform the cost minimization into a constraint-based specification so that a prescribed performance level is certified. An online coverage path generator is then designed based on the so-called constraint-based control in order to meet the performance certificate together with additional constraints inherent in the parameters that specify the paths. It is also shown there that the present constraint-based approach allows one to drastically reduce the computational complexity stemming from combinations of binary variables corresponding to the turning directions of the USVs. The present coverage path generator is finally demonstrated through simulations and experiments on an original testbed of multiple USVs.

We discuss the neutrino mass and Hubble tension solutions and examine their effects on the Redshift-Space Distortion (RSD) observations. An analysis with RSD data indicates smaller amplitude of perturbation. Including RSD data results in a slightly weaker upper limit on the neutrino mass than that derived for data without RSD, which is common in other extended models too. We have evaluated the impacts of RSD observations on some extended models, including the varying electron mass model, a time-dependent dark energy model with two parameter equations of state (EOS), and a model where the number of neutrino species is free. When we estimate the cosmological parameters for data including RSD, we found that the EOS parameter for dark energy is larger than that of the cosmological constant, and the effective number of neutrino species is smaller than the standard value, which infers a smaller present Hubble parameter $H_0$. From the viewpoint of cosmological tensions, the varying electron mass model with non-zero neutrino mass option looks promising to relax the Hubble tension and the $S_8$ tension simultaneously., Comment: 22 pages, 6 figures

Mouse and human brains have different functions that depend on their neuronal networks. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. These structural features allow mouse neurons to be integrated in the limited space of the brain, though thin neurites should suppress distal connections according to cable theory. We implemented this mouse-mimetic constraint in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, and birds. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the four datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks., Comment: 41 pages, 4 figures

Spin dynamics in the van der Waals antiferromagnet FeGa$_2$S$_4$ with triangular lattices are investigated using magnetometry, neutron scattering, and muon spin relaxation measurements. The characteristic spin relaxation time is thoroughly clarified over thirteen orders of magnitude. Although the temperature dependence of DC and AC susceptibilities recalls a conventional spin-glass transition, nonlinear susceptibilities showing no divergences at the anomalous temperature, $T^{\ast}=16.87(7)$~K, deny that and instead hint at other mechanisms. Elastic neutron scattering together with previously measured muon results depict a slowly fluctuated ($\sim 10^{-5}$~sec) spin state above $T^{\ast}$. In juxtaposing the underlying simplest structure among frustrated magnets with an intricate hierarchy of time scales, FeGa$_2$S$_4$ can be a playground for studying temporal spin correlations in the two-dimensional limit., Comment: 10 pages, 5 figures

We present a new method for the verification of quantum circuits based on a novel symbolic representation of sets of quantum states using level-synchronized tree automata (LSTAs). LSTAs extend classical tree automata by labeling each transition with a set of choices, which are then used to synchronize subtrees of an accepted tree. Compared to the traditional tree automata, LSTAs have an incomparable expressive power while maintaining important properties, such as closure under union and intersection, and decidable language emptiness and inclusion. We have developed an efficient and fully automated symbolic verification algorithm for quantum circuits based on LSTAs. The complexity of supported gate operations is at most quadratic, dramatically improving the exponential worst-case complexity of an earlier tree automata-based approach. Furthermore, we show that LSTAs are a promising model for parameterized verification, i.e., verifying the correctness of families of circuits with the same structure for any number of qubits involved, which principally lies beyond the capabilities of previous automated approaches. We implemented this method as a C++ tool and compared it with three symbolic quantum circuit verifiers and two simulators on several benchmark examples. The results show that our approach can solve problems with sizes orders of magnitude larger than the state of the art.

Current cosmological observations allow for deviations from the standard growth of large-scale structures in the universe. These deviations could indicate modifications to General Relativity on cosmological scales or suggest the dynamical nature of dark energy. It is important to characterize these departures in a model-independent manner to understand their significance objectively and explore their fundamental causes more generically across a wider spectrum of theories and models. In this paper, we compress the information from redshift-space distortion data into 2-3 parameters $\mu_i$, which control the ratio between the effective gravitational coupling in Poisson's equation and Newton's constant in several redshift bins in the late universe. We test the efficiency of this compression using mock final-year data from the Dark Energy Spectroscopic Instrument (DESI) and considering three different models within the class of effective field theories of dark energy. The constraints on the parameters of these models, obtained from both the direct fit to the data and the projection of the compressed parameters onto the parameters of the models, are fully consistent, demonstrating the method's good performance. Then, we apply it to current data and find hints of a suppressed matter growth in the universe at $\sim 2.7\sigma$ C.L., in full accordance with previous works in the literature. Finally, we perform a forecast with DESI data and show that the uncertainties on the parameters $\mu_1$ at $z<1$ and $\mu_2$ at $1

To study the ejecta property dependence of the gamma-ray burst (GRB) afterglow, we carry out spherically symmetrical one-dimensional special relativistic magneto-hydrodynamic simulations of magnetized outflows with an adaptive mesh refinement method. The Lorentz factor evolutions of forward and reverse shocks induced by the interaction between magnetized ejecta and an ambient medium are investigated for a wide range of magnetization and width of the ejecta. The forward shock evolution is described by the magnetic acceleration, coasting, transition, and self-similar deceleration phases. According to our simulation results, we numerically calculate the corresponding radiation. Based on our numerical results, to model afterglow light curves in general cases, we construct semi-analytical formulae for the Lorentz factor evolutions. The magnetization and ejecta width dependence are clearly seen in the reverse shock light curves. The transition phase with a reasonable ejecta width can reproduce the shallow decay phase in the observed GRB afterglow. The inverse Compton emission in the magnetic acceleration phase can be responsible for the very steep rise of the early TeV emission in GRB221009A., Comment: 15 pages, 13 figures, 1 table, submitted to MNRAS

Identifying the point of error is imperative in software debugging. Traditional fault localization (FL) techniques rely on executing the program and using the code coverage matrix in tandem with test case results to calculate a suspiciousness score for each function or line. Recently, learning-based FL techniques have harnessed machine learning models to extract meaningful features from the code coverage matrix and improve FL performance. These techniques, however, require compilable source code, existing test cases, and specialized tools for generating the code coverage matrix for each programming language of interest. In this paper, we propose, for the first time, a simple but effective sequence generation approach for fine-tuning large language models of code (LLMCs) for FL tasks. LLMCs have recently received much attention for various software engineering problems. In line with these, we leverage the innate understanding of code that LLMCs have acquired through pre-training on large code corpora. Specifically, we fine-tune representative encoder, encoder-decoder, and decoder-based 13 LLMCs for FL tasks. Unlike previous approaches, LLMCs can analyze code sequences even with syntactic errors, since they do not rely on compiled input. Still, they have a limitation on the length of the input data. Therefore, for a fair comparison with existing FL techniques, we extract methods with errors from the project-level benchmark, Defects4J, and analyze them at the line level. Experimental results show that LLMCs fine-tuned with our approach successfully pinpoint error positions in 50.6\%, 64.2\%, and 72.3\% of 1,291 methods in Defects4J for Top-1/3/5 prediction, outperforming the best learning-based state-of-the-art technique by up to 1.35, 1.12, and 1.08 times, respectively. Our findings suggest promising research directions for FL and automated program repair tasks using LLMCs.

When an ideal insulator is cooled, four regimes of thermal conductivity are expected to emerge one after another. Two of these, the Ziman and the Poiseuille, are hydrodynamic regimes in which collision among phonons are mostly Normal. It has been difficult to observe them, save for a few insulators with high levels of isotopic and chemical purity. Our thermal transport measurements, covering four decades of temperatures between 0.1 K and 900 K, reveal that sapphire displays all four regimes, despite its isotopic impurity. In the Ziman regime, the thermal conductivity exponentially increases attaining an amplitude as large as 35,000 W/Km. We show that the peak thermal conductivity of ultra-pure, simple insulators, including diamond, silicon and solid helium, is set by a universal scaling depending on isotropic purity. The thermal conductivity of sapphire is an order of magnitude higher than what is expected by this scaling. We argue that this may be caused by the proximity of optical and acoustic phonon modes, as a consequence of the large number of atoms in the primitive cell.

Open-source LLMs have shown great potential as fine-tuned chatbots, and demonstrate robust abilities in reasoning and surpass many existing benchmarks. Retrieval-Augmented Generation (RAG) is a technique for improving the performance of LLMs on tasks that the models weren't explicitly trained on, by leveraging external knowledge databases. Numerous studies have demonstrated the effectiveness of RAG to more successfully accomplish downstream tasks when using vector datasets that consist of relevant background information. It has been implicitly assumed by those in the field that if adversarial background information is utilized in this context, that the success of using a RAG-based approach would be nonexistent or even negatively impact the results. To address this assumption, we tested several open-source LLMs on the ability of RAG to improve their success in answering multiple-choice questions (MCQ) in the medical subspecialty field of Nephrology. Unlike previous studies, we examined the effect of RAG in utilizing both relevant and adversarial background databases. We set up several open-source LLMs, including Llama 3, Phi-3, Mixtral 8x7b, Zephyr$\beta$, and Gemma 7B Instruct, in a zero-shot RAG pipeline. As adversarial sources of information, text from the Bible and a Random Words generated database were used for comparison. Our data show that most of the open-source LLMs improve their multiple-choice test-taking success as expected when incorporating relevant information vector databases. Surprisingly however, adversarial Bible text significantly improved the success of many LLMs and even random word text improved test taking ability of some of the models. In summary, our results demonstrate for the first time the countertintuitive ability of adversarial information datasets to improve the RAG-based LLM success., Comment: 24 pages, 3 figures, 11 tables

A quantum finite-state automaton (QFA) is a theoretical model designed to simulate the evolution of a quantum system with finite memory in response to sequential input strings. We define the language of a QFA as the set of strings that lead the QFA to an accepting state when processed from its initial state. QFAs exemplify how quantum computing can achieve greater efficiency compared to classical computing. While being one of the simplest quantum models, QFAs are still notably challenging to construct from scratch due to the preliminary knowledge of quantum mechanics required for superimposing unitary constraints on the automata. Furthermore, even when QFAs are correctly assembled, the limitations of a current quantum computer may cause fluctuations in the simulation results depending on how an assembled QFA is translated into a quantum circuit. We present a framework that provides a simple and intuitive way to build QFAs and maximize the simulation accuracy. Our framework relies on two methods: First, it offers a predefined construction for foundational types of QFAs that recognize special languages MOD and EQU. They play a role of basic building blocks for more complex QFAs. In other words, one can obtain more complex QFAs from these foundational automata using standard language operations. Second, we improve the simulation accuracy by converting these QFAs into quantum circuits such that the resulting circuits perform well on noisy quantum computers. Our framework is available at https://github.com/sybaik1/qfa-toolkit., Comment: 14 pages, 5 figures

It has recently been argued that the Hubble tension may call for a combination of both pre- and post-recombination new physics. Motivated by these considerations, we provide one of the first concrete case studies aimed at constructing such a viable combination. We consider models that have individually worked best on either end of recombination so far: a spatially uniform time-varying electron mass leading to earlier recombination (also adding non-zero spatial curvature), and a sign-switching cosmological constant inducing an AdS-to-dS transition within the $\Lambda_{\rm s}$CDM model. When confronted against Cosmic Microwave Background (CMB), Baryon Acoustic Oscillations, and Type Ia Supernovae data, we show that no combination of these ingredients can successfully solve the Hubble tension. We find that the matter density parameter $\Omega_m$ plays a critical role, driving important physical scales in opposite directions: the AdS-to-dS transition requires a larger $\Omega_m$ to maintain the CMB acoustic scale fixed, whereas the varying electron mass requires a smaller $\Omega_m$ to maintain the redshift of matter-radiation equality fixed. Despite the overall failure, we use our results to draw general model-building lessons, highlighting the importance of assessing tension-solving directions in the parameter space of new physics parameters and how these correlate with shifts in other standard parameters, while underscoring the crucial role of $\Omega_m$ in this sense., Comment: 22 pages, 7 figures, 4 tables. Fig. 7 is the summary Figure. The reason for the failure of this particular combination of models and the consequent general lessons we draw are discussed between Pages 10 and 14. v2: several additional references added, minor change to title, added Bayesian evidence computation. Version accepted for publication in Phys. Dark Univ

Expressive music synthesis (EMS) for violin performance is a challenging task due to the disagreement among music performers in the interpretation of expressive musical terms (EMTs), scarcity of labeled recordings, and limited generalization ability of the synthesis model. These challenges create trade-offs between model effectiveness, diversity of generated results, and controllability of the synthesis system, making it essential to conduct a comparative study on EMS model design. This paper explores two violin EMS approaches. The end-to-end approach is a modification of a state-of-the-art text-to-speech generator. The parameter-controlled approach is based on a simple parameter sampling process that can render note lengths and other parameters compatible with MIDI-DDSP. We study these two approaches (in total, three model variants) through objective and subjective experiments and discuss several key issues of EMS based on the results., Comment: 15 pages, 2 figures, 3 tables

The linear representation hypothesis is the informal idea that semantic concepts are encoded as linear directions in the representation spaces of large language models (LLMs). Previous work has shown how to make this notion precise for representing binary concepts that have natural contrasts (e.g., {male, female}) as directions in representation space. However, many natural concepts do not have natural contrasts (e.g., whether the output is about an animal). In this work, we show how to extend the formalization of the linear representation hypothesis to represent features (e.g., is_animal) as vectors. This allows us to immediately formalize the representation of categorical concepts as polytopes in the representation space. Further, we use the formalization to prove a relationship between the hierarchical structure of concepts and the geometry of their representations. We validate these theoretical results on the Gemma and LLaMA-3 large language models, estimating representations for 900+ hierarchically related concepts using data from WordNet., Comment: Best Paper Award at the ICML 2024 Workshop on Mechanistic Interpretability. Code is available at https://github.com/KihoPark/LLM_Categorical_Hierarchical_Representations

Baryon acoustic oscillation (BAO) is one of the important standard rulers in cosmology. The results of the latest BAO measurements by Dark Energy Spectroscopic Instrument (DESI) survey have been reported. Cosmology with the varying electron mass model and the early dark energy models are regarded as interesting models to resolve the Hubble tension. We present constraints on the varying electron mass model and early dark energy models in new DESI data as well as cosmic microwave background by Planck and the conventional BAO data from 6dF, MGS, and DR12 and supernovae light curve data into analysis. Since new DESI BAO data indicate a slightly longer sound horizon $r_dh$ than the other BAO observations, for the varying electron mass model, the larger $H_0 =69.44\pm 0.84 $ km$/$s$/$Mpc is indicated., Comment: 18 pages, 6 figures

Deep learning-based, data-driven models are gaining prevalence in climate research, particularly for global weather prediction. However, training the global weather data at high resolution requires massive computational resources. Therefore, we present a new model named KARINA to overcome the substantial computational demands typical of this field. This model achieves forecasting accuracy comparable to higher-resolution counterparts with significantly less computational resources, requiring only 4 NVIDIA A100 GPUs and less than 12 hours of training. KARINA combines ConvNext, SENet, and Geocyclic Padding to enhance weather forecasting at a 2.5{\deg} resolution, which could filter out high-frequency noise. Geocyclic Padding preserves pixels at the lateral boundary of the input image, thereby maintaining atmospheric flow continuity in the spherical Earth. SENet dynamically improves feature response, advancing atmospheric process modeling, particularly in the vertical column process as numerous channels. In this vein, KARINA sets new benchmarks in weather forecasting accuracy, surpassing existing models like the ECMWF S2S reforecasts at a lead time of up to 7 days. Remarkably, KARINA achieved competitive performance even when compared to the recently developed models (Pangu-Weather, GraphCast, ClimaX, and FourCastNet) trained with high-resolution data having 100 times larger pixels. Conclusively, KARINA significantly advances global weather forecasting by efficiently modeling Earth's atmosphere with improved accuracy and resource efficiency.

This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework while reducing its computational complexity based on shape-gain vector quantization. In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized using a trainable Grassmannian codebook. A multi-rate codebook design strategy is also developed by introducing a codeword selection rule for a nested codebook along with the design of a loss function. Simulation results demonstrate that the proposed method reduces the computational complexity associated with VQ-VAE while improving CSI reconstruction performance under a given feedback overhead.

In this paper, we present a novel approach for joint activity detection (AD), channel estimation (CE), and data detection (DD) in uplink grant-free non-orthogonal multiple access (NOMA) systems. Our approach employs an iterative and parallel interference removal strategy inspired by parallel interference cancellation (PIC), enhanced with deep learning to jointly tackle the AD, CE, and DD problems. Based on this approach, we develop three PIC frameworks, each of which is designed for either coherent or non-coherence schemes. The first framework performs joint AD and CE using received pilot signals in the coherent scheme. Building upon this framework, the second framework utilizes both the received pilot and data signals for CE, further enhancing the performances of AD, CE, and DD in the coherent scheme. The third framework is designed to accommodate the non-coherent scheme involving a small number of data bits, which simultaneously performs AD and DD. Through joint loss functions and interference cancellation modules, our approach supports end-to-end training, contributing to enhanced performances of AD, CE, and DD for both coherent and non-coherent schemes. Simulation results demonstrate the superiority of our approach over traditional techniques, exhibiting enhanced performances of AD, CE, and DD while maintaining lower computational complexity.