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We present the multiwavelength flaring activity of the blazar AO 0235+164 during its recent active period from 2013 to 2019. From a discrete correlation function (DCF) analysis, we find a significant (>95%) correlation between radio and $\gamma$-ray light curves with flares at longer wavelengths following flares at shorter wavelengths. We identify a new jet component in 43 GHz VLBA data that was ejected from the radio core on MJD $57246^{+26}_{-30}$ (2015 August 12), during the peak of the 2015 radio flare. From the analysis of the jet component, we derived a Doppler factor of $\delta_{\rm var}=28.5\pm8.4$, a bulk Lorentz factor of $\Gamma=16.8^{+3.6}_{-3.1}$, and an intrinsic viewing angle of $\theta_{\rm v}=1.42^{+1.07}_{-0.52}\textrm{ degrees}$. Investigation of the quasi-simultaneous radio data revealed a partially absorbed spectrum with the turnover frequency varying in the range of $10-70$ GHz and the peak flux density varying in the range of $0.7-4$ Jy. We find the synchrotron self-absorption magnetic field strength to be $B_{\rm SSA}=15.3^{+12.6}_{-14.0}$ mG at the peak of the 2015 radio flare, which is comparable to the equipartition magnetic field strength of $B_{\rm EQ}=43.6^{+10.6}_{-10.4}$ mG calculated for the same epoch. Additional analysis of the radio emission region in the relativistic jet of AO 0235+164 suggests that it did not significantly deviate from equipartition during its recent flaring activity., Comment: 13 pages, 11 figures, 4 tables; Accepted for publication in MNRAS

The basal ganglia (BG) in the brain exhibit diverse functions for motor, cognition, and emotion. Such BG functions could be made via competitive harmony between the two competing pathways, direct pathway (DP) (facilitating movement) and indirect pathway (IP) (suppressing movement). As a result of break-up of harmony between DP and IP, there appear pathological states with disorder for movement, cognition, and psychiatry. In this paper, we are concerned about the Huntington's disease (HD), which is a genetic neurodegenerative disorder causing involuntary movement and severe cognitive and psychiatric symptoms. For the HD, the number of D2 SPNs ($N_{\rm D2}$) is decreased due to degenerative loss, and hence, by decreasing $x_{\rm D2}$ (fraction of $N_{\rm D2}$), we investigate break-up of harmony between DP and IP in terms of their competition degree ${\cal C}_d$, given by the ratio of strength of DP (${\cal S}_{DP}$) to strength of IP (${\cal S}_{IP}$) (i.e., ${\cal C}_d = {\cal S}_{DP} / {\cal S}_{IP}$). In the case of HD, the IP is under-active, in contrast to the case of Parkinson's disease with over-active IP, which results in increase in ${\cal C}_d$ (from the normal value). Thus, hyperkinetic dyskinesia such as chorea (involuntary jerky movement) occurs. We also investigate treatment of HD, based on optogenetics and GP ablation, by increasing strength of IP, resulting in recovery of harmony between DP and IP. Finally, we study effect of loss of healthy synapses of all the BG cells on HD. Due to loss of healthy synapses, disharmony between DP and IP increases, leading to worsen symptoms of the HD., Comment: arXiv admin note: substantial text overlap with arXiv:2309.12540

Personalized news recommendation aims to assist users in finding news articles that align with their interests, which plays a pivotal role in mitigating users' information overload problem. Although many recent works have been studied for better personalized news recommendation, the following challenges should be explored more: (C1) Comprehending manifold intents coupled within a news article, (C2) Differentiating varying post-read preferences of news articles, and (C3) Addressing the cold-start user problem. To tackle the aforementioned challenges together, in this paper, we propose a novel personalized news recommendation framework (CROWN) that employs (1) category-guided intent disentanglement for (C1), (2) consistency-based news representation for (C2), and (3) GNN-enhanced hybrid user representation for (C3). Furthermore, we incorporate a category prediction into the training process of CROWN as an auxiliary task, which provides supplementary supervisory signals to enhance intent disentanglement. Extensive experiments on two real-world datasets reveal that (1) CROWN provides consistent performance improvements over ten state-of-the-art news recommendation methods and (2) the proposed strategies significantly improve the accuracy of CROWN., Comment: 10 pages, 6 figures, 8 tables

Low-density parity-check (LDPC) codes have been successfully commercialized in communication systems due to their strong error correction capabilities and simple decoding process. However, the error-floor phenomenon of LDPC codes, in which the error rate stops decreasing rapidly at a certain level, presents challenges for achieving extremely low error rates and deploying LDPC codes in scenarios demanding ultra-high reliability. In this work, we propose training methods for neural min-sum (NMS) decoders to eliminate the error-floor effect. First, by leveraging the boosting learning technique of ensemble networks, we divide the decoding network into two neural decoders and train the post decoder to be specialized for uncorrected words that the first decoder fails to correct. Secondly, to address the vanishing gradient issue in training, we introduce a block-wise training schedule that locally trains a block of weights while retraining the preceding block. Lastly, we show that assigning different weights to unsatisfied check nodes effectively lowers the error-floor with a minimal number of weights. By applying these training methods to standard LDPC codes, we achieve the best error-floor performance compared to other decoding methods. The proposed NMS decoder, optimized solely through novel training methods without additional modules, can be integrated into existing LDPC decoders without incurring extra hardware costs. The source code is available at https://github.com/ghy1228/LDPC_Error_Floor ., Comment: 17 pages, 10 figures

We conducted a polarimetry campaign from radio to X-ray wavelengths of the high-synchrotron-peak (HSP) blazar Mrk 421, including Imaging X-ray Polarimetry Explorer (IXPE) measurements on 2022 December 6-8. We detected X-ray polarization of Mrk 421 with a degree of $\Pi_{\rm X}$=14$\pm$1$\%$ and an electric-vector position angle $\psi_{\rm X}$=107$\pm$3$^{\circ}$ in the 2-8 keV band. From the time variability analysis, we find a significant episodic variation in $\psi_{\rm X}$. During 7 months from the first IXPE pointing of Mrk 421 in 2022 May, $\psi_{\rm X}$ varied across the range of 0$^{\circ}$ to 180$^{\circ}$, while $\Pi_{\rm X}$ maintained similar values within $\sim$10-15$\%$. Furthermore, a swing in $\psi_{\rm X}$ in 2022 June was accompanied by simultaneous spectral variations. The results of the multiwavelength polarimetry show that the X-ray polarization degree was generally $\sim$2-3 times greater than that at longer wavelengths, while the polarization angle fluctuated. Additionally, based on radio, infrared, and optical polarimetry, we find that rotation of $\psi$ occurred in the opposite direction with respect to the rotation of $\psi_{\rm X}$ over longer timescales at similar epochs. The polarization behavior observed across multiple wavelengths is consistent with previous IXPE findings for HSP blazars. This result favors the energy-stratified shock model developed to explain variable emission in relativistic jets. The accompanying spectral variation during the $\psi_{\rm X}$ rotation can be explained by a fluctuation in the physical conditions, e.g., in the energy distribution of relativistic electrons. The opposite rotation direction of $\psi$ between the X-ray and longer-wavelength polarization accentuates the conclusion that the X-ray emitting region is spatially separated from that at longer wavelengths., Comment: 17 pages, 13 figures, 4 tables; Accepted for publication in A&A

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.

The basal ganglia (BG) show a variety of functions for motor and cognition. There are two competitive pathways in the BG; direct pathway (DP) which facilitates movement and indirect pathway (IP) which suppresses movement. It is well known that diverse functions of the BG may be made through "balance" between DP and IP. But, to the best of our knowledge, so far no quantitative analysis for such balance was done. In this paper, as a first time, we introduce the competition degree ${\cal C}_d$ between DP and IP. Then, by employing ${\cal C}_d$, we quantify their competitive harmony (i.e., competition and cooperative interplay), which could lead to improving our understanding of the traditional "balance" so clearly and quantitatively. We first consider the case of normal dopamine (DA) level of $\phi^*=0.3$. In the case of phasic cortical input (10 Hz), a healthy state with ${\cal C}_d^* = 2.82$ (i.e., DP is 2.82 times stronger than IP) appears. In this case, normal movement occurs via harmony between DP and IP. Next, we consider the case of decreased DA level, $\phi = \phi^*(=0.3)~x_{DA}$ ($1 > x_{DA} \geq 0$). With decreasing $x_{DA}$ from 1, the competition degree ${\cal C}_d$ between DP and IP decreases monotonically from ${\cal C}_d^*$, which results in appearance of a pathological Parkinsonian state with reduced ${\cal C}_d$. In this Parkinsonian state, strength of IP is much increased than that in the case of normal healthy state, leading to disharmony between DP and IP. Due to such break-up of harmony between DP and IP, impaired movement occurs. Finally, we also study treatment of the pathological Parkinsonian state via recovery of harmony between DP and IP.

We experimentally explored novel behaviors of non-reciprocal absorption and almost zero reflection in a dual photon resonator system, which is physically separated and composed of two inverted split ring resonators (ISRRs) with varying inter-distances. We also found that an electromagnetically-induced-transparency (EIT)-like peak at a specific inter-distance of d = 18 mm through traveling waves flowing along a shared microstrip line to which the dual ISRRs are dissipatively coupled. With the aid of CST-simulations and analytical modeling, we found that destructive and/or constructive interferences in traveling waves, indirectly coupled to each ISRR, result in a traveling-wave-induced transparency peak within a narrow window. Furthermore, we observed not only strong non-reciprocal responses of reflectivity and absorptivity at individual inter-distances exactly at the corresponding EIT-like peak positions, but also nearly zero reflection and almost perfect absorption for a specific case of d = 20 mm. Finally, the unidirectional absorptions with zero reflection at d = 20 mm are found to be ascribed to a non-Hermitian origin. This work not only provides a better understanding of traveling-wave-induced indirect coupling between two photonic resonators without magnetic coupling, but also suggests potential implications for the resulting non-reciprocal behaviors of absorption and reflection in microwave circuits and quantum information devices.

Hypergraphs can naturally model group-wise relations (e.g., a group of users who co-purchase an item) as hyperedges. Hyperedge prediction is to predict future or unobserved hyperedges, which is a fundamental task in many real-world applications (e.g., group recommendation). Despite the recent breakthrough of hyperedge prediction methods, the following challenges have been rarely studied: (C1) How to aggregate the nodes in each hyperedge candidate for accurate hyperedge prediction? and (C2) How to mitigate the inherent data sparsity problem in hyperedge prediction? To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework (CASH) that employs (1) context-aware node aggregation to precisely capture complex relations among nodes in each hyperedge for (C1) and (2) self-supervised contrastive learning in the context of hyperedge prediction to enhance hypergraph representations for (C2). Furthermore, as for (C2), we propose a hyperedge-aware augmentation method to fully exploit the latent semantics behind the original hypergraph and consider both node-level and group-level contrasts (i.e., dual contrasts) for better node and hyperedge representations. Extensive experiments on six real-world hypergraphs reveal that CASH consistently outperforms all competing methods in terms of the accuracy in hyperedge prediction and each of the proposed strategies is effective in improving the model accuracy of CASH. For the detailed information of CASH, we provide the code and datasets at: https://github.com/yy-ko/cash., Comment: 12 pages, 11 figures

We explore magnetic-field-driven chaos in magnetic skyrmions. Oscillating magnetic fields induce nonlinear dynamics in skyrmions, arising from the coupling of the secondary gyrotropic mode with a non-uniform, breathing-like mode. Through micromagnetic simulations, we observe complex patterns of hypotrochoidal motion in the orbital trajectories of the skyrmions, which are interpreted using bifurcation diagrams and local Lyapunov exponents. Our findings demonstrate that different nonlinear behaviors of skyrmions emerge at distinct temporal stages, depending on the nonlinear dynamic parameters. Investigating the abundant dynamic patterns of skyrmions during the emergence of chaos not only enhances device reliability but also provides useful guidelines for establishing chaos computing based on skyrmion dynamics.

The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCN) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known societal theories, i.e., balance and status. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN., Comment: 12 pages, 8 figures, 9 tables

Using both the in-out formalism and the monodromy method, we study the emission of charges from near-extremal charged Nariai black holes with the black hole event and cosmological horizons close to each other, whose near-horizon geometry is $\mathrm{dS}_2 \times \mathrm{S}^2$. The emission becomes catastrophic for a charge with energy greater than its chemical potential, whose leading exponential factor increases inversely proportional to the separation of two horizons. This effect may prevent near-extremal Nariai black holes with large charges that evaporate dominantly through the charge emission from evolving to black holes with a naked singularity, in analog to near-extremal RN-dS black holes that have the Breitenlohner-Friedman bound, below which they become stable against Hawking radiation and Schwinger effect of charge emission. The near-extremal Nariai black holes with small charges, which are close to near-extremal Schwarzschild-dS black holes, emit dominantly charge-neutral particles and evolve to black holes with increasing charge to mass ratio. We illuminate the origin of the catastrophic emission in the phase-integral formulation and monodromy method by comparing near-extremal charged Nariai black holes with near-extremal RN-dS black holes., Comment: v2: revised version including corrections of mistakes and more physical discussions, v3: with a new diagram and more discussions

Highly magnetized neutron stars have magnetic fields of order of the critical field and can lead to measurable QED effects. We consider the Goldreich-Julian pulsar model with supercritical magnetic fields, induced subcritical electric fields, and a period of milliseconds. We then study the strong field physics, such as Schwinger pair production and the vacuum birefringence including the wrench effect, whose X-ray polarimetry will be observed in future space missions., Comment: 13 pages, 6 figures; Talk at the Fifth Zeldovich meeting, an international conference in honor of Ya. B. Zeldovich held in Yerevan, Armenia on June 12--16, 2023

Sparse generalized matrix-matrix multiplication (SpGEMM) is a fundamental operation for real-world network analysis. With the increasing size of real-world networks, the single-machine-based SpGEMM approach cannot perform SpGEMM on large-scale networks, exceeding the size of main memory (i.e., not scalable). Although the distributed-system-based approach could handle large-scale SpGEMM based on multiple machines, it suffers from severe inter-machine communication overhead to aggregate results of multiple machines (i.e., not efficient). To address this dilemma, in this paper, we propose a novel storage-based SpGEMM approach (SAGE) that stores given networks in storage (e.g., SSD) and loads only the necessary parts of the networks into main memory when they are required for processing via a 3-layer architecture. Furthermore, we point out three challenges that could degrade the overall performance of SAGE and propose three effective strategies to address them: (1) block-based workload allocation for balancing workloads across threads, (2) in-memory partial aggregation for reducing the amount of unnecessarily generated storage-memory I/Os, and (3) distribution-aware memory allocation for preventing unexpected buffer overflows in main memory. Via extensive evaluation, we verify the superiority of SAGE over existing SpGEMM methods in terms of scalability and efficiency., Comment: 11 pages, 9 figures, 5 tables, CIKM

In communication and storage systems, error correction codes (ECCs) are pivotal in ensuring data reliability. As deep learning's applicability has broadened across diverse domains, there is a growing research focus on neural network-based decoders that outperform traditional decoding algorithms. Among these neural decoders, Error Correction Code Transformer (ECCT) has achieved the state-of-the-art performance, outperforming other methods by large margins. To further enhance the performance of ECCT, we propose two novel methods. First, leveraging the systematic encoding technique of ECCs, we introduce a new masking matrix for ECCT, aiming to improve the performance and reduce the computational complexity. Second, we propose a novel transformer architecture of ECCT called a double-masked ECCT. This architecture employs two different mask matrices in a parallel manner to learn more diverse features of the relationship between codeword bits in the masked self-attention blocks. Extensive simulation results show that the proposed double-masked ECCT outperforms the conventional ECCT, achieving the state-of-the-art decoding performance with significant margins., Comment: 8 pages, 5 figures

Introducing quasiparticle anisotropy in graphene via uniaxial strain has a profound effect on the polarization charge density induced by external impurities, both Coulomb and short-range. In particular, the charge distribution induced by a Coulomb impurity exhibits a power law tail modulated by a strain-dependent admixture of angular harmonics. The appearance of distributed charge is in sharp contrast to the response in pristine/isotropic graphene, where for subcritical impurities the polarization charge is fully localized at the impurity position. It is also interesting to note that our results are obtained strictly at zero chemical potential, and the behavior is distinct from the familiar Friedel oscillations observed at finite chemical potential. We find that over a wide range of strain, the $d$-wave symmetry is dominant. The presence of Dirac cone tilt, relevant to some 2D materials beyond graphene, can also substantially affect the induced charge distribution. Finally we consider impurities with short range potentials, and study the effect of strain on the charge response. Our results were obtained in the continuum via perturbation theory valid for weak (subcritical) potentials, and supported by numerical lattice simulations based on density functional theory., Comment: 13 pages, 13 figures. Added new Section VI with new figures, and updated old figures