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In the past decade, although single-robot perception has made significant advancements, the exploration of multi-robot collaborative perception remains largely unexplored. This involves fusing compressed, intermittent, limited, heterogeneous, and asynchronous environmental information across multiple robots to enhance overall perception, despite challenges like sensor noise, occlusions, and sensor failures. One major hurdle has been the lack of real-world datasets. This paper presents a pioneering and comprehensive real-world multi-robot collaborative perception dataset to boost research in this area. Our dataset leverages the untapped potential of air-ground robot collaboration featuring distinct spatial viewpoints, complementary robot mobilities, coverage ranges, and sensor modalities. It features raw sensor inputs, pose estimation, and optional high-level perception annotation, thus accommodating diverse research interests. Compared to existing datasets predominantly designed for Simultaneous Localization and Mapping (SLAM), our setup ensures a diverse range and adequate overlap of sensor views to facilitate the study of multi-robot collaborative perception algorithms. We demonstrate the value of this dataset qualitatively through multiple collaborative perception tasks. We believe this work will unlock the potential research of high-level scene understanding through multi-modal collaborative perception in multi-robot settings., Comment: 8 pages, 8 figures, 4 tables, Accepted at the IEEE Robotics Automation Letter (RA-L) 2024

We classify all graphs for which the Rees algebras of their edge ideals are normal and have regularity equal to their matching numbers.

We report on the development and commissioning of a new Q-band receiver for the Nobeyama 45-m telescope, covering 30--50 GHz with a receiver noise temperature of about 15 K. We name it eQ (extended Q-band) receiver. The system noise temperatures for observations are measured to be $\sim$ 30 K at 33 GHz and $\sim$ 75 K at 45 GHz. The Half-Power-Beam-Width (HPBW) is around 38\arcsec at 43 GHz. To enhance the observation capability, we tested the smoothed bandpass calibration technique and demonstrated the observation time can be significantly reduced compared to the standard position switch technique. The wide-bandwidth capability of this receiver provides precise determination of rest frequencies for molecular transitions with an accuracy of a few kHz through simultaneous observations of multiple transitions. Particularly, we determined the rest frequency of SO ($J_N$ = $1_0$--$0_1$) to be 30.001542 GHz, along with the rest frequency of CCS ($J_N$ = $4_3$--$3_2$) being 45.379033 GHz, adopting CCS ($J_N$ = $3_2$--$2_1$) at 33.751370 GHz as a reference line. The SO profile shows a double peak shape at the Cyanopolyyne Peak (CP) position of the Taurus Molecular Cloud-1 (TMC-1). The SO peaks coincide well with the CCS sub-components located near the outer parts of the TMC-1 filament. We interpret that the gravitational infall of TMC-1 generates shocks which enhance the SO abundance. The TMC-1 map shows that carbon-chain molecules are more abundant in the southern part of the filament, whereas SO is more abundant in the northern part. The eQ's excellent sensitivity allowed us to detect faint CO ($J$ = 1--0) spectra from the high-redshift object at a redshift of 2.442. Our receiver is expected to open new avenues for high-sensitivity molecular line observations in the Q-band., Comment: 20 pages, 18 figures, PASJ in press

Colonoscopy is a common and practical method for detecting and treating polyps. Segmenting polyps from colonoscopy image is useful for diagnosis and surgery progress. Nevertheless, achieving excellent segmentation performance is still difficult because of polyp characteristics like shape, color, condition, and obvious non-distinction from the surrounding context. This work presents a new novel architecture namely Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation (ADSNet), which modifies misclassified details and recovers weak features having the ability to vanish and not be detected at the final stage. The architecture consists of a complementary trilateral decoder to produce an early global map. A continuous attention module modifies semantics of high-level features to analyze two separate semantics of the early global map. The suggested method is experienced on polyp benchmarks in learning ability and generalization ability, experimental results demonstrate the great correction and recovery ability leading to better segmentation performance compared to the other state of the art in the polyp image segmentation task. Especially, the proposed architecture could be experimented flexibly for other CNN-based encoders, Transformer-based encoders, and decoder backbones., Comment: 13 pages with 7 figures, British Machine Vision Conference 2023

In this paper we study the well-posedness of the evolution equation of the form $u'(t)=Au(t)+Cu(t)$, $t\ge 0$, where $A$ is the generator of a $C_0$- semigroup and $C$ is a (possibly unbounded) linear operator in a Banach space $\mathbb{X}$. We prove that if $A$ generates a $C_0$-semigroup $\left (T_A(t)\right )_{t \geq 0}$ with $\|T(t)\| \le Me^{\omega t}$ in a Banach space $\mathbb{X}$ and $C$ is a linear operator in $\mathbb{X}$ such that $D(A)\subset D(C)$ and $\| CR(\mu ,A)\| \le K/(\mu -\omega)$ for each $\mu>\omega$, then, the above-mentioned evolution equation is well-posed, that is, $A+C$ generates a $C_0$-semigroup $\left (T_{A+C}(t)\right )_{t \geq 0}$ satisfying $\| T_{A+C}(t)\| \le Me^{(\omega +MK)t}$. Our approach is to use the Hille-Yosida Theorem. Discussions on the persistence of asymptotic behavior of the perturbed equations such as the roughness of exponential dichotomy are also given. The obtained results seem to be new., Comment: 12 pages

In this paper, we study the connectedness of the commuting graph of a general Lie algebra and provide a process to determine whether the commuting graph is connected or not, as well as to compute an upper bound for its diameter. In addition, we will examine the connectedness and diameter of the commuting graphs of some remarkable classes of Lie algebras, including: (1) a class of Lie algebras with one- or two-dimensional derived algebras; and (2) a class of solvable Lie algebras over the real field of dimension up to $4$., Comment: 21 pages

In this paper, we study the uniqueness problem for linearly nondegenerate meromorphic mappings from a K\"{a}hler manifold into $\mathbb P^n(\mathbb C)$ satisfying a condition $(C_\rho)$ and sharing hyperplanes in general position, where the condition that two meromorphic mappings $f,g$ have the same inverse image for some hyperplanes $H$ is replaced by a weaker one that $f^{-1}(H)\subset g^{-1}(H)$. Moreover, we also give some improvements on the uniqueness problem and algebraic dependence problem of meromorphic mappings which share hyperplanes and satisfy $(C_\rho)$ conditions for different non-negative numbers $\rho$., Comment: The paper consists of 19 pages

A new variant of Newton's method - named Backtracking New Q-Newton's method (BNQN) - was recently introduced by the second author. This method has good global convergence guarantees, specially concerning finding roots of meromorphic functions. This paper explores using BNQN for the Riemann xi function. We show in particular that the Riemann hypothesis is equivalent to that all attractors of BNQN lie on the critical line. We also explain how an apparent relation between the basins of attraction of BNQN and Voronoi's diagram can be helpful for verifying the Riemann hypothesis or finding a counterexample to it. Some illustrating experimental results are included, which convey some interesting phenomena. The experiments show that BNQN works very stably with highly transcendental functions like the Riemann xi function and its derivatives. Based on insights from the experiments, we discuss some concrete steps on using BNQN towards the Riemann hypothesis, by combining with de Bruijn -Newman's constant. Ideas and results from this paper can be extended to other zeta functions., Comment: 20 pages. Some typos are fixed, references are updated. Exposition is improved. Main change: A further idea is added to Section 4, where we combine with de Bruijn -Newman's constant. Comments are welcome!

Traffic predictions play a crucial role in intelligent transportation systems. The rapid development of IoT devices allows us to collect different kinds of data with high correlations to traffic predictions, fostering the development of efficient multi-modal traffic prediction models. Until now, there are few studies focusing on utilizing advantages of multi-modal data for traffic predictions. In this paper, we introduce a novel temporal attentive cross-modality transformer model for long-term traffic predictions, namely xMTrans, with capability of exploring the temporal correlations between the data of two modalities: one target modality (for prediction, e.g., traffic congestion) and one support modality (e.g., people flow). We conducted extensive experiments to evaluate our proposed model on traffic congestion and taxi demand predictions using real-world datasets. The results showed the superiority of xMTrans against recent state-of-the-art methods on long-term traffic predictions. In addition, we also conducted a comprehensive ablation study to further analyze the effectiveness of each module in xMTrans., Comment: Accepted at MDM 2024

The quest for robust Person re-identification (Re-ID) systems capable of accurately identifying subjects across diverse scenarios remains a formidable challenge in surveillance and security applications. This study presents a novel methodology that significantly enhances Person Re-Identification (Re-ID) by integrating Uncertainty Feature Fusion (UFFM) with Wise Distance Aggregation (WDA). Tested on benchmark datasets - Market-1501, DukeMTMC-ReID, and MSMT17 - our approach demonstrates substantial improvements in Rank-1 accuracy and mean Average Precision (mAP). Specifically, UFFM capitalizes on the power of feature synthesis from multiple images to overcome the limitations imposed by the variability of subject appearances across different views. WDA further refines the process by intelligently aggregating similarity metrics, thereby enhancing the system's ability to discern subtle but critical differences between subjects. The empirical results affirm the superiority of our method over existing approaches, achieving new performance benchmarks across all evaluated datasets.

In this paper, we establish a second main theorem for holomorphic maps with finite growth index on complex discs intersecting arbitrary families of hypersurfaces (fixed and moving) in projective varieties, which gives an above bound of the sum of truncated defects. Our result also is generalizes and improves many previous second main theorems for holomorphic maps from $\mathbb C$ intersecting hypersurfaces (moving and fixed) in projective varieties., Comment: This paper consists of 16 pages

We would like to enable a collaborative multiagent team to navigate at long length scales and under uncertainty in real-world environments. In practice, planning complexity scales with the number of agents in the team, with the length scale of the environment, and with environmental uncertainty. Enabling tractable planning requires developing abstract models that can represent complex, high-quality plans. However, such models often abstract away information needed to generate directly-executable plans for real-world agents in real-world environments, as planning in such detail, especially in the presence of real-world uncertainty, would be computationally intractable. In this paper, we describe the deployment of a planning system that used a hierarchy of planners to execute collaborative multiagent navigation tasks in real-world, unknown environments. By developing a planning system that was robust to failures at every level of the planning hierarchy, we enabled the team to complete collaborative navigation tasks, even in the presence of imperfect planning abstractions and real-world uncertainty. We deployed our approach on a Clearpath Husky-Jackal team navigating in a structured outdoor environment, and demonstrated that the system enabled the agents to successfully execute collaborative plans., Comment: Accepted to the IEEE ICRA Workshop on Field Robotics 2024

Multi-phase computed tomography (CT) scans use contrast agents to highlight different anatomical structures within the body to improve the probability of identifying and detecting anatomical structures of interest and abnormalities such as liver lesions. Yet, detecting these lesions remains a challenging task as these lesions vary significantly in their size, shape, texture, and contrast with respect to surrounding tissue. Therefore, radiologists need to have an extensive experience to be able to identify and detect these lesions. Segmentation-based neural networks can assist radiologists with this task. Current state-of-the-art lesion segmentation networks use the encoder-decoder design paradigm based on the UNet architecture where the multi-phase CT scan volume is fed to the network as a multi-channel input. Although this approach utilizes information from all the phases and outperform single-phase segmentation networks, we demonstrate that their performance is not optimal and can be further improved by incorporating the learning from models trained on each single-phase individually. Our approach comprises three stages. The first stage identifies the regions within the liver where there might be lesions at three different scales (4, 8, and 16 mm). The second stage includes the main segmentation model trained using all the phases as well as a segmentation model trained on each of the phases individually. The third stage uses the multi-phase CT volumes together with the predictions from each of the segmentation models to generate the final segmentation map. Overall, our approach improves relative liver lesion segmentation performance by 1.6% while reducing performance variability across subjects by 8% when compared to the current state-of-the-art models.

Traffic video description and analysis have received much attention recently due to the growing demand for efficient and reliable urban surveillance systems. Most existing methods only focus on locating traffic event segments, which severely lack descriptive details related to the behaviour and context of all the subjects of interest in the events. In this paper, we present TrafficVLM, a novel multi-modal dense video captioning model for vehicle ego camera view. TrafficVLM models traffic video events at different levels of analysis, both spatially and temporally, and generates long fine-grained descriptions for the vehicle and pedestrian at different phases of the event. We also propose a conditional component for TrafficVLM to control the generation outputs and a multi-task fine-tuning paradigm to enhance TrafficVLM's learning capability. Experiments show that TrafficVLM performs well on both vehicle and overhead camera views. Our solution achieved outstanding results in Track 2 of the AI City Challenge 2024, ranking us third in the challenge standings. Our code is publicly available at https://github.com/quangminhdinh/TrafficVLM.

The emergence of deep learning techniques has advanced the image segmentation task, especially for medical images. Many neural network models have been introduced in the last decade bringing the automated segmentation accuracy close to manual segmentation. However, cutting-edge models like Transformer-based architectures rely on large scale annotated training data, and are generally designed with densely consecutive layers in the encoder, decoder, and skip connections resulting in large number of parameters. Additionally, for better performance, they often be pretrained on a larger data, thus requiring large memory size and increasing resource expenses. In this study, we propose a new lightweight but efficient model, namely LiteNeXt, based on convolutions and mixing modules with simplified decoder, for medical image segmentation. The model is trained from scratch with small amount of parameters (0.71M) and Giga Floating Point Operations Per Second (0.42). To handle boundary fuzzy as well as occlusion or clutter in objects especially in medical image regions, we propose the Marginal Weight Loss that can help effectively determine the marginal boundary between object and background. Furthermore, we propose the Self-embedding Representation Parallel technique, that can help augment the data in a self-learning manner. Experiments on public datasets including Data Science Bowls, GlaS, ISIC2018, PH2, and Sunnybrook data show promising results compared to other state-of-the-art CNN-based and Transformer-based architectures. Our code will be published at: https://github.com/tranngocduvnvp/LiteNeXt., Comment: 35 pages, 9 figures, 10 tables

We establish a transcendental generalization of Nakamaye's theorem to compact complex manifolds when the form is not assumed to be closed. We apply the recent analytic technique developed by Collins--Tosatti to show that the non-Hermitian locus of a nef and big $(1,1)$-form, which is not necessarily closed, on a compact complex manifold equals the union of all positive-dimensional analytic subvarieties where the restriction of the form is not big (null locus). As an application, we can give an alternative proof of the Nakai--Moishezon criterion of Buchdahl and Lamari for complex surfaces and generalize this result in higher dimensions. This is also used for studying degenerate complex Monge--Amp\`ere equations on compact Hermitian manifolds. Finally, we investigate finite time non-collapsing singularities of the Chern--Ricci flow, partially answering a question raised by Tosatti and Weinkove., Comment: 34 pages, comments are welcome

The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at the cost of sacrificing model performance for robustness. We hypothesize that adversarial malware exploits the low-confidence regions of models and can be identified using epistemic uncertainty of ML approaches -- epistemic uncertainty in a machine learning-based malware detector is a result of a lack of similar training samples in regions of the problem space. In particular, a Bayesian formulation can capture the model parameters' distribution and quantify epistemic uncertainty without sacrificing model performance. To verify our hypothesis, we consider Bayesian learning approaches with a mutual information-based formulation to quantify uncertainty and detect adversarial malware in Android, Windows domains and PDF malware. We found, quantifying uncertainty through Bayesian learning methods can defend against adversarial malware. In particular, Bayesian models: (1) are generally capable of identifying adversarial malware in both feature and problem space, (2) can detect concept drift by measuring uncertainty, and (3) with a diversity-promoting approach (or better posterior approximations) lead to parameter instances from the posterior to significantly enhance a detectors' ability., Comment: Accepted to the 29th European Symposium on Research in Computer Security (ESORICS) 2024 Conference

The rise in additive manufacturing comes with unique opportunities and challenges. Massive part customization and rapid design changes are made possible with additive manufacturing, however, manufacturing industries that desire the implementation of robotics automation to improve production efficiency could face challenges in the gripper design and grasp planning due to highly complex geometrical shapes resulting from massive part customization. Yet, current gripper design for such objects are often manual and rely on ad-hoc design intuition. This would be limiting as such grippers would lack the ability to grasp different objects or grasp points, which is important for practical implementations. Hence, we introduce a fast, end-to-end approach to customize rigid gripper fingerpads that could achieve precise and stable grasping for different objects at multiple grasp points. Our approach relies on two key components: (i) a method based on set Boolean operations, e.g. intersections, subtractions, and unions to extract object features and synthesize gripper surfaces that conform to different local shapes to form caging grasps; (ii) a method to evaluate the grasp quality of synthesized grippers. We experimentally demonstrate the validity of our approach by synthesizing fingerpads that, once mounted on a physical robot gripper, are able to grasp different objects at multiple grasp points, all with tightly constrained grasps.

Navigating multi-robot systems in complex terrains has always been a challenging task. This is due to the inherent limitations of traditional robots in collision avoidance, adaptation to unknown environments, and sustained energy efficiency. In order to overcome these limitations, this research proposes a solution by integrating living insects with miniature electronic controllers to enable robotic-like programmable control, and proposing a novel control algorithm for swarming. Although these creatures, called cyborg insects, have the ability to instinctively avoid collisions with neighbors and obstacles while adapting to complex terrains, there is a lack of literature on the control of multi-cyborg systems. This research gap is due to the difficulty in coordinating the movements of a cyborg system under the presence of insects' inherent individual variability in their reactions to control input. In response to this issue, we propose a novel swarm navigation algorithm addressing these challenges. The effectiveness of the algorithm is demonstrated through an experimental validation in which a cyborg swarm was successfully navigated through an unknown sandy field with obstacles and hills. This research contributes to the domain of swarm robotics and showcases the potential of integrating biological organisms with robotics and control theory to create more intelligent autonomous systems with real-world applications.

Semantic segmentation is crucial for autonomous driving, particularly for Drivable Area and Lane Segmentation, ensuring safety and navigation. To address the high computational costs of current state-of-the-art (SOTA) models, this paper introduces TwinLiteNetPlus (TwinLiteNet$^+$), a model adept at balancing efficiency and accuracy. TwinLiteNet$^+$ incorporates standard and depth-wise separable dilated convolutions, reducing complexity while maintaining high accuracy. It is available in four configurations, from the robust 1.94 million-parameter TwinLiteNet$^+_{\text{Large}}$ to the ultra-compact 34K-parameter TwinLiteNet$^+_{\text{Nano}}$. Notably, TwinLiteNet$^+_{\text{Large}}$ attains a 92.9\% mIoU for Drivable Area Segmentation and a 34.2\% IoU for Lane Segmentation. These results notably outperform those of current SOTA models while requiring a computational cost that is approximately 11 times lower in terms of Floating Point Operations (FLOPs) compared to the existing SOTA model. Extensively tested on various embedded devices, TwinLiteNet$^+$ demonstrates promising latency and power efficiency, underscoring its suitability for real-world autonomous vehicle applications.

While intrusion detection systems (IDSs) benefit from the diversity and generalization of IoT data features, the data diversity (e.g., the heterogeneity and high dimensions of data) also makes it difficult to train effective machine learning models in IoT IDSs. This also leads to potentially redundant/noisy features that may decrease the accuracy of the detection engine in IDSs. This paper first introduces a novel neural network architecture called Multiple-Input Auto-Encoder (MIAE). MIAE consists of multiple sub-encoders that can process inputs from different sources with different characteristics. The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks. To distil and retain more relevant features but remove less important/redundant ones during the training process, we further design and embed a feature selection layer right after the representation layer of MIAE resulting in a new model called MIAEFS. This layer learns the importance of features in the representation vector, facilitating the selection of informative features from the representation vector. The results on three IDS datasets, i.e., NSLKDD, UNSW-NB15, and IDS2017, show the superior performance of MIAE and MIAEFS compared to other methods, e.g., conventional classifiers, dimensionality reduction models, unsupervised representation learning methods with different input dimensions, and unsupervised feature selection models. Moreover, MIAE and MIAEFS combined with the Random Forest (RF) classifier achieve accuracy of 96.5% in detecting sophisticated attacks, e.g., Slowloris. The average running time for detecting an attack sample using RF with the representation of MIAE and MIAEFS is approximate 1.7E-6 seconds, whilst the model size is lower than 1 MB.

Representation Learning (RL) plays a pivotal role in the success of many problems including cyberattack detection. Most of the RL methods for cyberattack detection are based on the latent vector of Auto-Encoder (AE) models. An AE transforms raw data into a new latent representation that better exposes the underlying characteristics of the input data. Thus, it is very useful for identifying cyberattacks. However, due to the heterogeneity and sophistication of cyberattacks, the representation of AEs is often entangled/mixed resulting in the difficulty for downstream attack detection models. To tackle this problem, we propose a novel mod called Twin Auto-Encoder (TAE). TAE deterministically transforms the latent representation into a more distinguishable representation namely the \textit{separable representation} and the reconstructsuct the separable representation at the output. The output of TAE called the \textit{reconstruction representation} is input to downstream models to detect cyberattacks. We extensively evaluate the effectiveness of TAE using a wide range of bench-marking datasets. Experiment results show the superior accuracy of TAE over state-of-the-art RL models and well-known machine learning algorithms. Moreover, TAE also outperforms state-of-the-art models on some sophisticated and challenging attacks. We then investigate various characteristics of TAE to further demonstrate its superiority.

Tackling is a fundamental defensive move in American football, with the main purpose of stopping the forward motion of the ball-carrier. However, current tackling metrics are manually recorded outcomes that are inherently flawed due to their discrete and subjective nature. Using player tracking data, we present a novel framework for assessing tackling contribution in a continuous and objective manner. Our approach first identifies when a defender is in a ``contact window'' of the ball-carrier during a play, before assigning value to each window and the players involved. This enables us to devise a new metric called fractional tackles, which credits defenders for halting the ball-carrier's forward motion toward the end zone. We demonstrate that fractional tackles overcome the shortcomings of traditional metrics such as tackles and assists, by providing greater variation and measurable information for players lacking recorded statistics like defensive linemen. We view our contribution as a significant step forward in measuring defensive performance in American football and a clear demonstration of the capabilities of player tracking data., Comment: 16 pages, 6 figures, 2 tables

The combinatorial pricing problem (CPP) is a bilevel problem in which the leader maximizes their revenue by imposing tolls on certain items that they can control. Based on the tolls set by the leader, the follower selects a subset of items corresponding to an optimal solution of a combinatorial optimization problem. To accomplish the leader's goal, the tolls need to be sufficiently low to discourage the follower from choosing the items offered by the competitors. In this paper, we derive a single-level reformulation for the CPP by rewriting the follower's problem as a longest path problem using a dynamic programming model, and then taking its dual and applying strong duality. We proceed to solve the reformulation in a dynamic fashion with a cutting plane method. We apply this methodology to 2 distinct dynamic programming models, namely, a novel formulation designated as selection diagram and the well-known decision diagram. We also produce numerical results to evaluate their performances across 3 different specializations of the CPP and a closely related problem that is the knapsack interdiction problem. Our results showcase the potential of the 2 proposed reformulations over the natural value function approach, expanding the set of tools to solve combinatorial bilevel programs.

Ethereum 2.0 is a major upgrade to improve its scalability, throughput, and security. In this version, RANDAO is the scheme to randomly select the users who propose, confirm blocks, and get rewards. However, a vulnerability, referred to as the `Last Revealer Attack' (LRA), compromises the randomness of this scheme by introducing bias to the Random Number Generator (RNG) process. This vulnerability is first clarified again in this study. After that, we propose a Shamir's Secret Sharing (SSS)-based RANDAO scheme to mitigate the LRA. Through our analysis, the proposed method can prevent the LRA under favorable network conditions.

Drawing is an art that enables people to express their imagination and emotions. However, individuals usually face challenges in drawing, especially when translating conceptual ideas into visually coherent representations and bridging the gap between mental visualization and practical execution. In response, we propose ARtVista - a novel system integrating AR and generative AI technologies. ARtVista not only recommends reference images aligned with users' abstract ideas and generates sketches for users to draw but also goes beyond, crafting vibrant paintings in various painting styles. ARtVista also offers users an alternative approach to create striking paintings by simulating the paint-by-number concept on reference images, empowering users to create visually stunning artwork devoid of the necessity for advanced drawing skills. We perform a pilot study and reveal positive feedback on its usability, emphasizing its effectiveness in visualizing user ideas and aiding the painting process to achieve stunning pictures without requiring advanced drawing skills. The source code will be available at https://github.com/htrvu/ARtVista., Comment: CHI 2024

We report the discovery of a hot Jupiter candidate orbiting HS Psc, a K7 ($\approx$0.7 $M_\odot$) member of the $\approx$130 Myr AB Doradus moving group. Using radial velocities over 4 years from the Habitable-zone Planet Finder spectrograph at the Hobby-Eberly Telescope, we find a periodic signal at $P_b = 3.986_{-0.003}^{+0.044}$ d. A joint Keplerian and Gaussian process stellar activity model fit to the RVs yields a minimum mass of $m_p \sin i = 1.5_{-0.4}^{+0.6}$ $M_\mathrm{Jup}$. The stellar rotation period is well constrained by the Transiting Exoplanet Survey Satellite light curve ($P_\mathrm{rot} = 1.086 \pm 0.003$ d) and is not an integer harmonic nor alias of the orbital period, supporting the planetary nature of the observed periodicity. HS Psc b joins a small population of young, close-in giant giant planet candidates with robust age and mass constraints and demonstrates that giant planets can either migrate to their close-in orbital separations by 130 Myr or form $in \; situ$. Given its membership in a young moving group, HS Psc represents an excellent target for follow-up observations to further characterize this young hot Jupiter, refine its orbital properties, and search for additional planets in the system., Comment: Accepted for publication to The Astronomical Journal, 27 pages, 6 figures

This paper presents our contributions towards advancing the state of Vietnamese language understanding and generation through the development and dissemination of open datasets and pre-trained models for Vietnamese Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).