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We investigate isoscalar axial-vector mesons using a coupled-channel formalism. The kernel amplitudes are constructed from meson-exchange diagrams in the $t$- and $u$-channels, which are derived from effective Lagrangians based on hidden local symmetry. We incorporate six channels: $\pi\rho$, $\eta\omega$, $K\bar{K}^*$, $\eta\phi$, $\eta'\omega$, and $\eta'\phi$, and solve the off-shell coupled integral equations. We first discuss the dynamical generation of the $h_1(1170)$. The pole diagram for $h_1(1595)$ has a certain effect on the generation of $h_1(1170)$. We observe two poles at $(1387-i6)$ MeV and $(1452-i51)$ MeV, which exhibit a two-pole structure of the $h_1(1415)$ meson. This two-pole structure may resolve the discrepancy in the experimental data on the mass of $h_1(1415)$. The results show that the lower pole couples strongly to the $K\bar{K}^*$ channel, while the higher pole couples predominantly to the $\eta\phi$ channel. This provides insights into the nature of $h_1$ mesons and explains ossible discrepancies in the mass of $h_1(1415)$., Comment: 12 pages, 7 figure

We investigate hidden-charm pentaquark states using an off-shell coupled-channel formalism involving heavy meson and singly heavy baryon scattering. Our approach utilizes an effective Lagrangian to construct the kernel amplitudes, which respect both heavy quark symmetry and hidden local symmetry. After solving the coupled integral equations, we obtain the transition amplitudes for $J/\psi N$ scattering and various heavy meson and singly heavy baryon scattering processes. We identify seven distinct peaks related to molecular states of heavy mesons $\bar{D}$ ($\bar{D}^*$) and singly heavy baryons $\Sigma_c$ ($\Sigma_c^*$). Four of these peaks can be associated with the known $P_{c\bar{c}}$ states: $P_{c\bar{c}}(4312)$, $P_{c\bar{c}}(4380)$, $P_{c\bar{c}}(4440)$, and $P_{c\bar{c}}(4457)$. We predict two additional resonances with masses around 4.5 GeV, which we interpret as $\overline{D}^* \Sigma_c^*$ molecular states, and identify one cusp structure. Additionally, we predict two $P$-wave pentaquark states with positive parity, which may be candidates for genuine pentaquark configurations. Notably, these pentaquark states undergo significant modifications in the $J/\psi N$ elastic channel, with some even disappearing due to interference from the positive parity channel. The present investigation may provide insight into the absence of pentaquark states in $J/\psi$ photoproduction observed by the GlueX collaboration., Comment: 17 pages, 9 figures

The ${\rm SL}_3$-skein algebra $\mathscr{S}_{\bar{q}}(\mathfrak{S})$ of a punctured oriented surface $\mathfrak{S}$ is a quantum deformation of the coordinate algebra of the ${\rm SL}_3$-character variety of $\mathfrak{S}$. When $\bar{q}$ is a root of unity, we prove the Unicity Theorem for representations of $\mathscr{S}_{\bar{q}}(\mathfrak{S})$, in particular the existence and uniqueness of a generic irreducible representation. Furthermore, we show that the center of $\mathscr{S}_{\bar{q}}(\frak{S})$ is generated by the peripheral skeins around punctures and the central elements contained in the image of the Frobenius homomorphism for $\mathscr{S}_{\bar{q}}(\frak{S})$, a surface generalization of Frobenius homomorphisms of quantum groups related to ${\rm SL}_3$. We compute the rank of $\mathscr{S}_{\bar{q}}(\mathfrak{S})$ over its center, hence the dimension of the generic irreducible representation., Comment: v2: 56 pages, reorganized, streamlined, added some details

This report describes the submission of HYU ASML team to the IEEE Signal Processing Cup 2024 (SP Cup 2024). This challenge, titled "ROBOVOX: Far-Field Speaker Recognition by a Mobile Robot," focuses on speaker recognition using a mobile robot in noisy and reverberant conditions. Our solution combines the result of deep residual neural networks and time-delay neural network-based speaker embedding models. These models were trained on a diverse dataset that includes French speech. To account for the challenging evaluation environment characterized by high noise, reverberation, and short speech conditions, we focused on data augmentation and training speech duration for the speaker embedding model. Our submission achieved second place on the SP Cup 2024 public leaderboard, with a detection cost function of 0.5245 and an equal error rate of 6.46%., Comment: Technical report for IEEE Signal Processing Cup 2024, 9 pages

We investigate the properties of the static neutron stars and strangeness-mixed stars, based on the equations of state derived from a pion mean-field approach. Using the empirical data on the pion-nucleus scattering and bulk properties of nuclear matter, we have already fixed all the parameters in a previous work, where the nucleons and hyperons were shown to be modified in various nuclear medium. In the current work, we first examine the energy and pressure inside a neutron star. We show that the central densities in various neutron stars vary within the range of $(3-6)\rho_0$, where $\rho_0$ is the normal nuclear matter density. The mass-radius relations are obtained and discussed. As the slope parameter for neutron matter increases, the radii of the neutron stars increase with their masses fixed. We also study the strangeness-mixed stars or the hyperon stars using the same sets of the parameters. As the strangeness content of strange matter increases, the binding energy per nucleon is saturated and the corresponding equation of state becomes softened. Consequently, the central densities of the strangeness-mixed stars increase. Assuming that recently observed neutron stars are the strangeness-mixed ones, we find that the central densities increase. In the case of the pure strange stars, the central densities reach almost $(5-6)\rho_0$., Comment: 15 pages, 8 figures

As speech synthesis systems continue to make remarkable advances in recent years, the importance of robust deepfake detection systems that perform well in unseen systems has grown. In this paper, we propose a novel adaptive centroid shift (ACS) method that updates the centroid representation by continually shifting as the weighted average of bonafide representations. Our approach uses only bonafide samples to define their centroid, which can yield a specialized centroid for one-class learning. Integrating our ACS with one-class learning gathers bonafide representations into a single cluster, forming well-separated embeddings robust to unseen spoofing attacks. Our proposed method achieves an equal error rate (EER) of 2.19% on the ASVspoof 2021 deepfake dataset, outperforming all existing systems. Furthermore, the t-SNE visualization illustrates that our method effectively maps the bonafide embeddings into a single cluster and successfully disentangles the bonafide and spoof classes., Comment: Accepted by Interspeech 2024

Voxel hierarchy on dynamic brain graphs is produced by k core percolation on functional dynamic amplitude correlation of resting-state fMRI. Directed graphs and their afferent/efferent capacities are produced by Markov modeling of the universal cover of undirected graphs simultaneously with the calculation of volume entropy. Positive and unsigned negative brain graphs were analyzed separately on sliding-window representation to underpin the visualization and quantitation of mental dynamic states with their transitions. Voxel hierarchy animation maps of positive graphs revealed abrupt changes in coreness k and kmaxcore, which we called mental state transitions. Afferent voxel capacities of the positive graphs also revealed transient modules composed of dominating voxels/independent components and their exchanges representing mental state transitions. Animation and quantification plots of voxel hierarchy and afferent capacity corroborated each other in underpinning mental state transitions and afferent module exchange on the positive directed functional connectivity graphs. We propose the use of spatiotemporal trajectories of voxels on positive dynamic graphs to construct hierarchical structures by k core percolation and quantified in- and out-flows of information of voxels by volume entropy/directed graphs to subserve diverse resting mental state transitions on resting-state fMRI graphs in normal human individuals.

The capability of the global, electromagnetic gyrokinetic GENE code interfaced with the transport Tango solver is exploited to address the impact of fusion alpha particles (in their dual role of fast particles and heating source) on plasma profiles and performance at JET in the discharges with the highest quasi-stationary peak fusion power during the DTE2 experimental campaigns. Employing radially global nonlinear electromagnetic GENE-Tango simulations, we compare results with/without alpha particles and alpha heating. Our findings reveal that alpha particles have a negligible impact on turbulent transport, with GENE-Tango converging to similar plasma profiles regardless of their inclusion as a kinetic species in GENE. On the other hand, alpha heating is found to contribute to the peaking of the electron temperature profiles, leading to a 1keV drop on the on-axis electron temperature when alpha heating is neglected in Tango. The minimal impact of alpha particles on turbulent transport in this JET discharge - despite this being the shot with the highest fusion output - is attributed to the low content of fusion alpha in this discharge. To assess the potential impact of alpha particles on turbulent transport in regimes with higher alpha particle density, as expected in ITER and fusion reactors, we artificially increased the alpha particle concentration to levels expected for ITER. By performing global nonlinear GENE standalone simulations, we found that increasing the alpha particle density beyond five times the nominal value lead to significant overall turbulence destabilization.

Neuro-symbolic neural networks have been extensively studied to integrate symbolic operations with neural networks, thereby improving systematic generalization. Specifically, Tensor Product Representation (TPR) framework enables neural networks to perform differentiable symbolic operations by encoding the symbolic structure of data within vector spaces. However, TPR-based neural networks often struggle to decompose unseen data into structured TPR representations, undermining their symbolic operations. To address this decomposition problem, we propose a Discrete Dictionary-based Decomposition (D3) layer designed to enhance the decomposition capabilities of TPR-based models. D3 employs discrete, learnable key-value dictionaries trained to capture symbolic features essential for decomposition operations. It leverages the prior knowledge acquired during training to generate structured TPR representations by mapping input data to pre-learned symbolic features within these dictionaries. D3 is a straightforward drop-in layer that can be seamlessly integrated into any TPR-based model without modifications. Our experimental results demonstrate that D3 significantly improves the systematic generalization of various TPR-based models while requiring fewer additional parameters. Notably, D3 outperforms baseline models on the synthetic task that demands the systematic decomposition of unseen combinatorial data.

In 1980's H Verlinde suggested to construct and use a quantization of Teichm\"uller spaces to construct spaces of conformal blocks for the Liouville conformal field theory. This suggestion led to a mathematical formulation by Fock in 1990's, called the modular functor conjecture, based on the Chekhov-Fock quantum Teichm\"uller theory. In 2000's Teschner combined the Chekhov-Fock version and the Kashaev version of quantum Teichm\"uller theory to construct a solution to a modified form of the conjecture. We embark on a direct approach to the conjecture based on the Chekhov-Fock(-Goncharov) theory. We construct quantized trace-of-monodromy along simple loops via Bonahon and Wong's quantum trace maps developed in 2010's, and investigate algebraic structures of them, which will eventually lead to construction and properties of quantized geodesic length operators. We show that a special recursion relation used by Teschner is satisfied by the quantized trace-of-monodromy, and that the quantized trace-of-monodromy for disjoint loops commute in a certain strong sense., Comment: 74 pages

The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to the scarcity of labeled datasets. Despite the integration of advanced Inertial Measurement Units (IMUs) in ubiquitous wearable devices like smartwatches and fitness trackers, which offer self-labeled activity data from users, the volume of labeled data remains insufficient compared to domains where deep learning has achieved remarkable success. Addressing this gap, in this paper, we propose a novel approach to improve wearable sensor-based HAR by introducing a pose-to-sensor network model that generates sensor data directly from 3D skeleton pose sequences. our method simultaneously trains the pose-to-sensor network and a human activity classifier, optimizing both data reconstruction and activity recognition. Our contributions include the integration of simultaneous training, direct pose-to-sensor generation, and a comprehensive evaluation on the MM-Fit dataset. Experimental results demonstrate the superiority of our framework with significant performance improvements over baseline methods., Comment: Accepted in IEEE 6th International Conference on Activity and Behavior Computing (ABC 2024)

Electrically tunable optical devices present diverse functionalities for manipulating electromagnetic waves by leveraging elements capable of reversibly switching between different optical states. This adaptability in adjusting their responses to electromagnetic waves after fabrication is crucial for developing more efficient and compact optical systems for a broad range of applications including sensing, imaging, telecommunications, and data storage. Chalcogenide-based phase change materials (PCMs) have shown great promise due to their stable, non-volatile phase transition between amorphous and crystalline states. Nonetheless, optimizing the switching parameters of PCM devices and maintaining their stable operation over thousands of cycles with minimal variation can be challenging. In this paper, we report on the critical role of PCM pattern as well as electrical pulse form in achieving reliable and stable switching, extending the operational lifetime of the device beyond 13,000 switching events. To achieve this, we have developed a computer-aided algorithm that monitors optical changes in the device and adjusts the applied voltage in accordance with the phase transformation process, thereby significantly enhancing the lifetime of these reconfigurable devices. Our findings reveal that patterned PCM structures show significantly higher endurance compared to blanket PCM thin films.

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names

Measuring the complex permittivity of material is essential in many scenarios such as quality check and component analysis. Generally, measurement methods for characterizing the material are based on the usage of vector network analyzer, which is large and not easy for on-site measurement, especially in high frequency range such as millimeter wave (mmWave). In addition, some measurement methods require the destruction of samples, which is not suitable for non-destructive inspection. In this work, a small distance increment (SDI) method is proposed to non-destructively measure the complex permittivity of material. In SDI, the transmitter and receiver are formed as the monostatic radar, which is facing towards the material under test (MUT). During the measurement, the distance between radar and MUT changes with small increments and the signals are recorded at each position. A mathematical model is formulated to depict the relationship among the complex permittivity, distance increment, and measured signals. By fitting the model, the complex permittivity of MUT is estimated. To implement and evaluate the proposed SDI method, a commercial off-the-shelf mmWave radar is utilized and the measurement system is developed. Then, the evaluation was carried out on the acrylic plate. With the proposed method, the estimated complex permittivity of acrylic plate shows good agreement with the literature values, demonstrating the efficacy of SDI method for characterizing the complex permittivity of material.

Diffusion-based generative models have emerged as powerful tools in the realm of generative modeling. Despite extensive research on denoising across various timesteps and noise levels, a conflict persists regarding the relative difficulties of the denoising tasks. While various studies argue that lower timesteps present more challenging tasks, others contend that higher timesteps are more difficult. To address this conflict, our study undertakes a comprehensive examination of task difficulties, focusing on convergence behavior and changes in relative entropy between consecutive probability distributions across timesteps. Our observational study reveals that denoising at earlier timesteps poses challenges characterized by slower convergence and higher relative entropy, indicating increased task difficulty at these lower timesteps. Building on these observations, we introduce an easy-to-hard learning scheme, drawing from curriculum learning, to enhance the training process of diffusion models. By organizing timesteps or noise levels into clusters and training models with ascending orders of difficulty, we facilitate an order-aware training regime, progressing from easier to harder denoising tasks, thereby deviating from the conventional approach of training diffusion models simultaneously across all timesteps. Our approach leads to improved performance and faster convergence by leveraging benefits of curriculum learning, while maintaining orthogonality with existing improvements in diffusion training techniques. We validate these advantages through comprehensive experiments in image generation tasks, including unconditional, class-conditional, and text-to-image generation.