Search

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally, data-driven approaches are utilized for this task, yet the high dimensional nature of genotype data makes the analysis and prediction difficult. Motivated by the extensive knowledge encoded in pre-trained LLMs and their success in processing complex biomedical concepts, we set to examine the ability of LLMs in feature selection and engineering for tabular genotype data, with a novel knowledge-driven framework. We develop FREEFORM, Free-flow Reasoning and Ensembling for Enhanced Feature Output and Robust Modeling, designed with chain-of-thought and ensembling principles, to select and engineer features with the intrinsic knowledge of LLMs. Evaluated on two distinct genotype-phenotype datasets, genetic ancestry and hereditary hearing loss, we find this framework outperforms several data-driven methods, particularly on low-shot regimes. FREEFORM is available as open-source framework at GitHub: https://github.com/PennShenLab/FREEFORM.

Resistive random-access memory (ReRAM) crossbar arrays are suitable for efficient inference computations in neural networks due to their analog general matrix-matrix multiplication (GEMM) capabilities. However, traditional ReRAM-based accelerators suffer from spatial and temporal underutilization. We present HURRY, a reconfigurable and multifunctional ReRAM-based in-situ accelerator. HURRY uses a block activation scheme for concurrent activation of dynamically sized ReRAM portions, enhancing spatial utilization. Additionally, it incorporates functional blocks for convolution, ReLU, max pooling, and softmax computations to improve temporal utilization. System-level scheduling and data mapping strategies further optimize performance. Consequently, HURRY achieves up to 3.35x speedup, 5.72x higher energy efficiency, and 7.91x greater area efficiency compared to current ReRAM-based accelerators.

Faraday rotation is an important probe of the magnetic fields and magnetized plasma around active galactic nuclei (AGN) jets. We present a Faraday rotation measure image of the M87 jet between 85.2 GHz and 101.3 GHz with a resolution of ~2" with the Atacama Large Millimeter/submillimeter Array (ALMA). We found that the rotation measure (RM) of the M87 core is $\rm (4.5\pm 0.4)\times10^{4}\ rad\ m^{-2}$ with a low linear polarization fraction of $\rm (0.88\pm 0.08)\%$. The spatial RM gradient in the M87 jet spans a wide range from $\sim -2\times10^4\rm~rad\ m^{-2}$ to $\sim 3\times10^4\rm~rad\ m^{-2}$ with a typical uncertainty of $0.3\times10^4\rm~rad\ m^{-2}$. A comparison with previous RM measurements of the core suggests that the Faraday rotation of the core may originate very close to the super massive black hole (SMBH). Both an internal origin and an external screen with a rapidly varying emitting source could be possible. As for the jet, the RM gradient indicates a helical configuration of the magnetic field that persists up to kpc scale. Combined with the kpc-scale RM measurements at lower frequencies, we found that RM is frequency-dependent in the jet. One possible scenario to explain this dependence is that the kpc-scale jet has a trumpet-like shape and the jet coil unwinds near its end., Comment: 11pages, 5 figures. Accepted for publication in ApJ. Comments are welcome

Person search is the task to localize a query person in gallery datasets of scene images. Existing methods have been mainly developed to handle a single target dataset only, however diverse datasets are continuously given in practical applications of person search. In such cases, they suffer from the catastrophic knowledge forgetting in the old datasets when trained on new datasets. In this paper, we first introduce a novel problem of lifelong person search (LPS) where the model is incrementally trained on the new datasets while preserving the knowledge learned in the old datasets. We propose an end-to-end LPS framework that facilitates the knowledge distillation to enforce the consistency learning between the old and new models by utilizing the prototype features of the foreground persons as well as the hard background proposals in the old domains. Moreover, we also devise the rehearsal-based instance matching to further improve the discrimination ability in the old domains by using the unlabeled person instances additionally. Experimental results demonstrate that the proposed method achieves significantly superior performance of both the detection and re-identification to preserve the knowledge learned in the old domains compared with the existing methods., Comment: 10 pages, 6 figure

Event cameras are dynamic vision sensors inspired by the biological retina, characterized by their high dynamic range, high temporal resolution, and low power consumption. These features make them capable of perceiving 3D environments even in extreme conditions. Event data is continuous across the time dimension, which allows a detailed description of each pixel's movements. To fully utilize the temporally dense and continuous nature of event cameras, we propose a novel temporal event stereo, a framework that continuously uses information from previous time steps. This is accomplished through the simultaneous training of an event stereo matching network alongside stereoscopic flow, a new concept that captures all pixel movements from stereo cameras. Since obtaining ground truth for optical flow during training is challenging, we propose a method that uses only disparity maps to train the stereoscopic flow. The performance of event-based stereo matching is enhanced by temporally aggregating information using the flows. We have achieved state-of-the-art performance on the MVSEC and the DSEC datasets. The method is computationally efficient, as it stacks previous information in a cascading manner. The code is available at https://github.com/mickeykang16/TemporalEventStereo., Comment: Accepted by ECCV2024

Recent advancements in large language models (LLMs) have achieved promising performances across various applications. Nonetheless, the ongoing challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. In this work, we introduce DALK, a.k.a. Dynamic Co-Augmentation of LLMs and KG, to address this limitation and demonstrate its ability on studying Alzheimer's Disease (AD), a specialized sub-field in biomedicine and a global health priority. With a synergized framework of LLM and KG mutually enhancing each other, we first leverage LLM to construct an evolving AD-specific knowledge graph (KG) sourced from AD-related scientific literature, and then we utilize a coarse-to-fine sampling method with a novel self-aware knowledge retrieval approach to select appropriate knowledge from the KG to augment LLM inference capabilities. The experimental results, conducted on our constructed AD question answering (ADQA) benchmark, underscore the efficacy of DALK. Additionally, we perform a series of detailed analyses that can offer valuable insights and guidelines for the emerging topic of mutually enhancing KG and LLM. We will release the code and data at https://github.com/David-Li0406/DALK., Comment: Accepted by EMNLP 2024 Findings; revise format problem

Collecting and labeling real datasets to train the person search networks not only requires a lot of time and effort, but also accompanies privacy issues. The weakly-supervised and unsupervised domain adaptation methods have been proposed to alleviate the labeling burden for target datasets, however, their generalization capability is limited. We introduce a novel person search method based on the domain generalization framework, that uses an automatically labeled unreal dataset only for training but is applicable to arbitrary unseen real datasets. To alleviate the domain gaps when transferring the knowledge from the unreal source dataset to the real target datasets, we estimate the fidelity of person instances which is then used to train the end-to-end network adaptively. Moreover, we devise a domain-invariant feature learning scheme to encourage the network to suppress the domain-related features. Experimental results demonstrate that the proposed method provides the competitive performance to existing person search methods even though it is applicable to arbitrary unseen datasets without any prior knowledge and re-training burdens., Comment: AAAI2024 accepted

Lifelong person re-identification (LReID) assumes a practical scenario where the model is sequentially trained on continuously incoming datasets while alleviating the catastrophic forgetting in the old datasets. However, not only the training datasets but also the gallery images are incrementally accumulated, that requires a huge amount of computational complexity and storage space to extract the features at the inference phase. In this paper, we address the above mentioned problem by incorporating the backward-compatibility to LReID for the first time. We train the model using the continuously incoming datasets while maintaining the model's compatibility toward the previously trained old models without re-computing the features of the old gallery images. To this end, we devise the cross-model compatibility loss based on the contrastive learning with respect to the replay features across all the old datasets. Moreover, we also develop the knowledge consolidation method based on the part classification to learn the shared representation across different datasets for the backward-compatibility. We suggest a more practical methodology for performance evaluation as well where all the gallery and query images are considered together. Experimental results demonstrate that the proposed method achieves a significantly higher performance of the backward-compatibility compared with the existing methods. It is a promising tool for more practical scenarios of LReID., Comment: 17 pages, 5 figures, 7 tables

This study addresses the challenge of forming effective groups in collaborative problem-solving environments. Recognizing the complexity of human interactions and the necessity for efficient collaboration, we propose a novel approach leveraging graph theory and reinforcement learning. Our methodology involves constructing a graph from a dataset where nodes represent participants, and edges signify the interactions between them. We conceptualize each participant as an agent within a reinforcement learning framework, aiming to learn an optimal graph structure that reflects effective group dynamics. Clustering techniques are employed to delineate clear group structures based on the learned graph. Our approach provides theoretical solutions based on evaluation metrics and graph measurements, offering insights into potential improvements in group effectiveness and reductions in conflict incidences. This research contributes to the fields of collaborative work and educational psychology by presenting a data-driven, analytical approach to group formation. It has practical implications for organizational team building, classroom settings, and any collaborative scenario where group dynamics are crucial. The study opens new avenues for exploring the application of graph theory and reinforcement learning in social and behavioral sciences, highlighting the potential for empirical validation in future work.

In this paper, we are going to share a draft of the development of a conversational agent created to disseminate information about historical sites located in the Seoul. The primary objective of the agent is to increase awareness among visitors who are not familiar with Seoul, about the presence and precise locations of valuable cultural heritage sites. It aims to promote a basic understanding of Korea's rich and diverse cultural history. The agent is thoughtfully designed for accessibility in English and utilizes data generously provided by the Seoul Metropolitan Government. Despite the limited data volume, it consistently delivers reliable and accurate responses, seamlessly aligning with the available information. We have meticulously detailed the methodologies employed in creating this agent and provided a comprehensive overview of its underlying structure within the paper. Additionally, we delve into potential improvements to enhance this initial version of the system, with a primary emphasis on expanding the available data through our prompting. In conclusion, we provide an in-depth discussion of our expectations regarding the future impact of this agent in promoting and facilitating the sharing of historical sites., Comment: 4 pages, 4 figures, draft

In stereo-matching knowledge distillation methods of the self-supervised monocular depth estimation, the stereo-matching network's knowledge is distilled into a monocular depth network through pseudo-depth maps. In these methods, the learning-based stereo-confidence network is generally utilized to identify errors in the pseudo-depth maps to prevent transferring the errors. However, the learning-based stereo-confidence networks should be trained with ground truth (GT), which is not feasible in a self-supervised setting. In this paper, we propose a method to identify and filter errors in the pseudo-depth map using multiple disparity maps by checking their consistency without the need for GT and a training process. Experimental results show that the proposed method outperforms the previous methods and works well on various configurations by filtering out erroneous areas where the stereo-matching is vulnerable, especially such as textureless regions, occlusion boundaries, and reflective surfaces., Comment: ICASSP 2024. The first two authors are equally contributed

We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems. Grounded in the foundational concepts of disparity definition and the disparity plane sweep, the proposed stereo-confidence method is built upon the idea that any shift in a stereo-image pair should be updated in a corresponding amount shift in the disparity map. Based on this idea, the proposed stereo-confidence method can be summarized in three folds. 1) Using the disparity plane sweep, multiple disparity maps can be obtained and treated as a 3-D volume (predicted disparity volume), like the cost volume is constructed. 2) One of these disparity maps serves as an anchor, allowing us to define a desirable (or ideal) disparity profile at every spatial point. 3) By comparing the desirable and predicted disparity profiles, we can quantify the level of matching ambiguity between left and right images for confidence measurement. Extensive experimental results using various stereo-matching networks and datasets demonstrate that the proposed stereo-confidence method not only shows competitive performance on its own but also consistent performance improvements when it is used as an input modality for learning-based stereo-confidence methods., Comment: AAAI 2024. The first two authors contributed equally

One of the most well-known extragalactic sources in the sky, quasar 3C 454.3, shows a curved parsec-scale jet that has been exhaustively monitored with very-long-baseline interferometry (VLBI) over the recent years. In this work, we present a comprehensive analysis of four years of high-frequency VLBI observations at 43 GHz and 86 GHz, between 2013-2017, in total intensity and linear polarization. The images obtained from these observations enabled us to study the jet structure and the magnetic field topology of the source on spatial scales down to 4.6 parsec in projected distance. The kinematic analysis reveals the abrupt vanishing of at least four new superluminal jet features in a characteristic jet region (i.e., region C), which is located at an approximate distance of 0.6 milliarcseconds from the VLBI core. Our results support a model in which the jet bends, directing the relativistic plasma flow almost perfectly toward our line of sight, co-spatially with the region where components appear to stop., Comment: 15 pages, 7 figures

We present total intensity and linear polarization images of OJ287 at 1.68GHz, obtained through space-based VLBI observations with RadioAstron on April 16, 2016. The observations were conducted using a ground array consisting of the VLBA and the EVN. Ground-space fringes were detected with a maximum projected baseline length of 5.6 Earth's diameter, resulting in an angular resolution of 530 uas. With this unprecedented resolution at such a low frequency, the progressively bending jet structure of OJ287 has been resolved up to 10 pc of the projected distance from the radio core. In comparison with close-in-time VLBI observations at 15, 43, 86 GHz from MOJAVE and VLBA-BU-BLAZAR monitoring projects, we obtain the spectral index map showing the opaque core and optically thin jet components. The optically thick core has a brightness temperature of 10$^{13}$ K, and is further resolved into two sub-components at higher frequencies labeled C1 and C2. These sub-components exhibit a transition from optically thick to thin, with a SSA turnover frequency estimated to be 33 and 11.5 GHz, and a turnover flux density 4 and 0.7 Jy, respectively. Assuming a Doppler boosting factor of 10, the SSA values provide the estimate of the magnetic field strengths from SSA of 3.4 G for C1 and 1.0 G for C2. The magnetic field strengths assuming equipartition arguments are also estimated as 2.6 G and 1.6 G, respectively. The integrated degree of linear polarization is found to be approximately 2.5 %, with the electric vector position angle being well aligned with the local jet direction at the core region. This alignment suggests a predominant toroidal magnetic field, which is in agreement with the jet formation model that requires a helical magnetic field anchored to either the black hole ergosphere or the accretion disk. Further downstream, the jet seems to be predominantly threaded by a poloidal magnetic field., Comment: 15 pages, 11 figures, 3 tables

Anomaly detection is a critical and challenging task that aims to identify data points deviating from normal patterns and distributions within a dataset. Various methods have been proposed using a one-class-one-model approach, but these techniques often face practical problems such as memory inefficiency and the requirement of sufficient data for training. In particular, few-shot anomaly detection presents significant challenges in industrial applications, where limited samples are available before mass production. In this paper, we propose a few-shot anomaly detection method that integrates adversarial training loss to obtain more robust and generalized feature representations. We utilize the adversarial loss previously employed in domain adaptation to align feature distributions between source and target domains, to enhance feature robustness and generalization in few-shot anomaly detection tasks. We hypothesize that adversarial loss is effective when applied to features that should have similar characteristics, such as those from the same layer in a Siamese network's parallel branches or input-output pairs of reconstruction-based methods. Experimental results demonstrate that the proposed method generally achieves better performance when utilizing the adversarial loss., Comment: BMVC 2023

Supermassive black holes at the centre of active galactic nuclei power some of the most luminous objects in the Universe. Typically, very long baseline interferometric (VLBI) observations of blazars have revealed only funnel-like morphologies with little information of the ejected plasma internal structure, or lacked the sufficient dynamic range to reconstruct the extended jet emission. Here we show microarcsecond-scale angular resolution images of the blazar 3C 279 obtained at 22 GHz with the space VLBI mission RadioAstron, which allowed us to resolve the jet transversely and reveal several filaments produced by plasma instabilities in a kinetically dominated flow. Our high angular resolution and dynamic range image suggests that emission features traveling down the jet may manifest as a result of differential Doppler-boosting within the filaments, as opposed to the standard shock-in-jet model invoked to explain blazar jet radio variability. Moreover, we infer that the filaments in 3C 279 are possibly threaded by a helical magnetic field rotating clockwise, as seen in the direction of the flow motion, with an intrinsic helix pitch angle of ~45 degrees in a jet with a Lorentz factor of ~13 at the time of observation., Comment: 15 pages, 7 figures. Initial version of an article published in Nature Astronomy

Context. Blazar AO 0235+164, located at redshift z = 0.94, has undergone several sharp multi-spectral-range flaring episodes during the last decades. In particular, the episodes peaking in 2008 and 2015, that received extensive multi-wavelength coverage, exhibited interesting behavior. Aims. We study the actual origin of these two observed flares by constraining the properties of the observed photo-polarimetric variability, those of the broad-band spectral energy-distribution and the observed time-evolution behavior of the source as seen by ultra-high resolution total-flux and polarimetric Very-long-baseline interferometry (VLBI) imaging. Methods. The analysis of VLBI images allows us to constrain kinematic and geometrical parameters of the 7 mm jet. We use the Discrete Correlation Function to compute the statistical correlation and the delays between emission at different spectral ranges. Multi-epoch modeling of the spectral energy distributions allows us to propose specific models of emission; in particular for the unusual spectral features observed in this source in the X-ray region of the spectrum during strong multi spectral-range flares. Results. We find that these X-ray spectral features can be explained by an emission component originating in a separate particle distribution than the one responsible for the two standard blazar bumps. This is in agreement with the results of our correlation analysis that do not find a strong correlation between the X-rays and the remaining spectral ranges. We find that both external Compton dominated and synchrotron self-Compton dominated models can explain the observed spectral energy distributions. However, synchrotron self-Compton models are strongly favored by the delays and geometrical parameters inferred from the observations.

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