Miracle-dz/UniFAR-FTUs huggingface dataset:
MUSE–ALMA Haloes – IX. Morphologies and stellar properties of gas-rich galaxies

Understanding how galaxies interact with the circumgalactic medium (CGM) requires determining how galaxies’ morphological and stellar properties correlate with their CGM properties. We report an analysis of 66 well-imaged galaxies detected in Hubble Space Telescope and Very Large Telescope MUSE observations and determined to be within ±500 km s−1 of the redshifts of strong intervening quasar absorbers at 0.2 ≲ z ≲ 1.4 with H i column densities $N_{\rm H I} \gt 10^{18}\, \rm cm^{-2}$. We present the geometrical properties (Sérsic indices, effective radii, axis ratios, and position angles) of these galaxies determined using galfit. Using these properties along with star formation rates (SFRs, estimated using the H α or [O ii] luminosity) and stellar masses (M* estimated from spectral energy distribution fits), we examine correlations among various stellar and CGM properties. Our main findings are as follows: (1) SFR correlates well with M*, and most absorption-selected galaxies are consistent with the star formation main sequence of the global population. (2) More massive absorber counterparts are more centrally concentrated and are larger in size. (3) Galaxy sizes and normalized impact parameters correlate negatively with NHI, consistent with higher NHI absorption arising in smaller galaxies, and closer to galaxy centres. (4) Absorption and emission metallicities correlate with M* and specific SFR, implying metal-poor absorbers arise in galaxies with low past star formation and faster current gas consumption rates. (5) SFR surface densities of absorption-selected galaxies are higher than predicted by the Kennicutt–Schmidt relation for local galaxies, suggesting a higher star formation efficiency in the absorption-selected galaxies.
Simultaneous Planning of Liner Ship Speed Optimization, Fleet Deployment, Scheduling and Cargo Allocation with Container Transshipment

Due to a substantial growth in the world waterborne trade volumes and drastic changes in the global climate accounted for CO2 emissions, the shipping companies need to escalate their operational and energy efficiency. Therefore, a multi-objective mixed-integer non-linear programming (MINLP) model is proposed in this study to simultaneously determine the optimal service schedule, number of vessels in a fleet serving each route, vessel speed between two ports of call, and flow of cargo considering transshipment operations for each pair of origin-destination. This MINLP model presents a trade-off between economic and environmental aspects considering total shipping time and overall shipping cost as the two conflicting objectives. The shipping cost comprises of CO2 emission, fuel consumption and several operational costs where fuel consumption is determined using speed and load. Two efficient evolutionary algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Online Clustering-based Evolutionary Algorithm (OCEA) are applied to attain the near-optimal solution of the proposed problem. Furthermore, six problem instances of different sizes are solved using these algorithms to validate the proposed model.
Dissecting Code Vulnerabilities: Insights from C++ and Java Vulnerability Analysis with ReVeal Model

This study presents an analysis conducted on a real-world dataset of Java vulnerability-fixing commits. The dataset consists of commits with varying numbers of modified methods, leading to a natural partitioning based on the number of changed functions. The research aims to address several key questions. Firstly, the study investigates the optimal parameter selection for ReVeal, a state-of-the-art model, in order to achieve its best performance. Secondly, it explores the contributions of different parts of the Java dataset towards vulnerability detection. Lastly, the study evaluates the model's performance in separating close-to-vulnerable methods (vulnerable methods and their fixed versions) from randomly selected safe code, as well as the finer separation of vulnerable methods from their fixed versions within the set of close-to-vulnerable methods. The research employs a series of experiments to answer these questions and derive meaningful insights.
Tuning Pre-trained Model via Moment Probing

Recently, efficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in exploiting the final representations for task-dependent classification. However, most of the existing methods focus on how to effectively introduce a few of learnable parameters, and little work pays attention to the commonly used LP module. In this paper, we propose a novel Moment Probing (MP) method to further explore the potential of LP. Distinguished from LP which builds a linear classification head based on the mean of final features (e.g., word tokens for ViT) or classification tokens, our MP performs a linear classifier on feature distribution, which provides the stronger representation ability by exploiting richer statistical information inherent in features. Specifically, we represent feature distribution by its characteristic function, which is efficiently approximated by using first- and second-order moments of features. Furthermore, we propose a multi-head convolutional cross-covariance (MHC3) to compute second-order moments in an efficient and effective manner. By considering that MP could affect feature learning, we introduce a partially shared module to learn two recalibrating parameters (PSRP) for backbones based on MP, namely MP+. Extensive experiments on ten benchmarks using various models show that our MP significantly outperforms LP and is competitive with counterparts at lower training cost, while our MP+ achieves state-of-the-art performance.
Mixbiotic society measures: Assessment of community well-going as living system

Social isolation is caused by the impoverishment of community (atomism) and fragmentation is caused by the enlargement of in-group (mobism), both of which can be viewed as social problems related to communication. To solve these problems, the philosophical world has proposed the concept of"mixbiotic society,"in which individuals with freedom and diverse values mix and mingle to recognize their respective"fundamental incapability"each other and sublimate into solidarity. Based on this concept, this study proposes new mixbiotic society measures to evaluate dynamic communication patterns with reference to classification in cellular automata and particle reaction diffusion that simulate living phenomena. Specifically, the hypothesis of measures corresponding to the four classes was formulated, and the hypothesis was validated by simulating the generation and disappearance of communication. As a result, considering communication patterns as multidimensional vectors, it found that the mean of Euclidean distance for"mobism,"the variance of the relative change in distance for"atomism,"the composite measure that multiplies the mean and variance of cosine similarity for"mixism,"which corresponds to the well-going of mixbiotic society, and the almost zero measures for"nihilism,"are suitable. Then, evaluating seven real-society datasets using these measures, we showed that the mixism measure is useful for assessing the livingness of communication, and that it is possible to typify communities based on plural measures. The measures established in this study are superior to conventional analysis in that they can evaluate dynamic patterns, they are simple to calculate, and their meanings are easy to interpret. As a future development, the mixbiotic society measures will be used in the fields of digital democracy and platform cooperativism toward a desirable society.
Hybrid deliberation: Citizen dialogues in a post-pandemic era

This report first provides a brief review of various forms of dialogue-based participation, e.g., Citizen Assembly, Citizen Lottery, Citizen Jury, Deliberative Polling, and Participatory Budgeting. Challenges associated with these long-lasting practices are identified and hybrid deliberation is proposed as a concept to address the challenges. The report then analyzes six leading examples of digital or hybrid formats of citizen dialogues. Through the comparison of the cases, the report concludes about the hurdles/risks, success factors/opportunities, and best practices for a complementary use of digital and analogue participation formats. Hybrid deliberation is proposed to be the future direction for dialogue-based participation that involves masses and generates high-quality outcomes.
Rethinking Mesh Watermark: Towards Highly Robust and Adaptable Deep 3D Mesh Watermarking

The goal of 3D mesh watermarking is to embed the message in 3D meshes that can withstand various attacks imperceptibly and reconstruct the message accurately from watermarked meshes. The watermarking algorithm is supposed to withstand multiple attacks, and the complexity should not grow significantly with the mesh size. Unfortunately, previous methods are less robust against attacks and lack of adaptability. In this paper, we propose a robust and adaptable deep 3D mesh watermarking Deep3DMark that leverages attention-based convolutions in watermarking tasks to embed binary messages in vertex distributions without texture assistance. Furthermore, our Deep3DMark exploits the property that simplified meshes inherit similar relations from the original ones, where the relation is the offset vector directed from one vertex to its neighbor. By doing so, our method can be trained on simplified meshes but remains effective on large size meshes (size adaptable) and unseen categories of meshes (geometry adaptable). Extensive experiments demonstrate our method remains efficient and effective even if the mesh size is 190× increased. Under mesh attacks, Deep3DMark achieves 10%∼50% higher accuracy than traditional methods, and 2× higher SNR and 8% higher accuracy than previous DNN-based methods.
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication

Handwriting authentication is a valuable tool used in various fields, such as fraud prevention and cultural heritage protection. However, it remains a challenging task due to the complex features, severe damage, and lack of supervision. In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address these issues. It can dynamically learn complex yet important features and accurately predict writer identities. Specifically, to remove the negative effects of imperfections and redundancy, we design an information-theoretic filter for pre-processing and propose a novel adaptive matching scheme to represent images as patches of local regions dominated by more important features. Through online optimization at inference time, the most informative patch embeddings are identified as the"most important"elements. Furthermore, we employ contrastive self-supervised training with a momentum-based paradigm to learn more general statistical structures of handwritten data without supervision. We conduct extensive experiments on five benchmark datasets and our manually annotated dataset EN-HA, which demonstrate the superiority of our CSSL-RHA compared to baselines. Additionally, we show that our proposed model can still effectively achieve authentication even under abnormal circumstances, such as data falsification and corruption.
ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection

Object detection is the key technique to a number of Computer Vision applications, but it often requires large amounts of annotated data to achieve decent results. Moreover, for pedestrian detection specifically, the collected data might contain some personally identifiable information (PII), which is highly restricted in many countries. This label intensive and privacy concerning task has recently led to an increasing interest in training the detection models using synthetically generated pedestrian datasets collected with a photo-realistic video game engine. The engine is able to generate unlimited amounts of data with precise and consistent annotations, which gives potential for significant gains in the real-world applications. However, the use of synthetic data for training introduces a synthetic-to-real domain shift aggravating the final performance. To close the gap between the real and synthetic data, we propose to use a Generative Adversarial Network (GAN), which performsparameterized unpaired image-to-image translation to generate more realistic images. The key benefit of using the GAN is its intrinsic preference of low-level changes to geometric ones, which means annotations of a given synthetic image remain accurate even after domain translation is performed thus eliminating the need for labeling real data. We extensively experimented with the proposed method using MOTSynth dataset to train and MOT17 and MOT20 detection datasets to test, with experimental results demonstrating the effectiveness of this method. Our approach not only produces visually plausible samples but also does not require any labels of the real domain thus making it applicable to the variety of downstream tasks.
Online Monocular Lane Mapping Using Catmull-Rom Spline

In this study, we introduce an online monocular lane mapping approach that solely relies on a single camera and odometry for generating spline-based maps. Our proposed technique models the lane association process as an assignment issue utilizing a bipartite graph, and assigns weights to the edges by incorporating Chamfer distance, pose uncertainty, and lateral sequence consistency. Furthermore, we meticulously design control point initialization, spline parameterization, and optimization to progressively create, expand, and refine splines. In contrast to prior research that assessed performance using self-constructed datasets, our experiments are conducted on the openly accessible OpenLane dataset. The experimental outcomes reveal that our suggested approach enhances lane association and odometry precision, as well as overall lane map quality. We have open-sourced out code11https://github.com/HKUST-Aerial-Robotics/MonoLaneMapping for this project.
Approximating a continuously stratified hydrostatic system by the multi-layer shallow water system

In this article we consider the multi-layer shallow water system for the propagation of gravity waves in density-stratified flows, with additional terms introduced by the oceanographers Gent and McWilliams in order to take into account large-scale isopycnal diffusivity induced by small-scale unresolved eddies. We establish a bridge between the multi-layer shallow water system and the corresponding system for continuously stratified flows, that is the incompressible Euler equations with eddy-induced diffusivity under the hydrostatic approximation. Specifically we prove that, under an assumption of stable stratification, sufficiently regular solutions to the incompressible Euler equations can be approximated by solutions to multi-layer shallow water systems as the number of layers, $N$, increases. Moreover, we provide a convergence rate of order $1/N^2$. A key ingredient in the proof is a stability estimate for the multi-layer system which relies on suitable energy estimates mimicking the ones recently established by Bianchini and Duch\^ene on the continuously stratified system. This requires to compile a dictionary that translates continuous operations (differentiation, integration, etc.) into corresponding discrete operations.
On the Complexity of the Bipartite Polarization Problem: from Neutral to Highly Polarized Discussions

The Bipartite Polarization Problem is an optimization problem where the goal is to find the highest polarized bipartition on a weighted and labelled graph that represents a debate developed through some social network, where nodes represent user's opinions and edges agreement or disagreement between users. This problem can be seen as a generalization of the maxcut problem, and in previous work approximate solutions and exact solutions have been obtained for real instances obtained from Reddit discussions, showing that such real instances seem to be very easy to solve. In this paper, we investigate further the complexity of this problem, by introducing an instance generation model where a single parameter controls the polarization of the instances in such a way that this correlates with the average complexity to solve those instances. The average complexity results we obtain are consistent with our hypothesis: the higher the polarization of the instance, the easier is to find the corresponding polarized bipartition.
BLISS: Interplanetary Exploration with Swarms of Low-Cost Spacecraft

Leveraging advancements in micro-scale technology, we propose a fleet of autonomous, low-cost, small solar sails for interplanetary exploration. The Berkeley Low-cost Interplanetary Solar Sail (BLISS) project aims to utilize small-scale technologies to create a fleet of tiny interplanetary femto-spacecraft for rapid, low-cost exploration of the inner solar system. This paper describes the hardware required to build a nearly 10 g spacecraft using a 1 m$^2$ solar sail steered by micro-electromechanical systems (MEMS) inchworm actuators. The trajectory control to a NEO, here 101955 Bennu, is detailed along with the low-level actuation control of the solar sail and the specifications of proposed onboard communication and computation. Two other applications are also shortly considered: sample return from dozens of Jupiter-family comets and interstellar comet rendezvous and imaging. The paper concludes by discussing the fundamental scaling limits and future directions for steerable autonomous miniature solar sails with onboard custom computers and sensors.
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications

The challenging deployment of compute-intensive applications from domains such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance. This radical paradigm shift has attracted interest from both academia and industry, resulting in significant research on approximation techniques and methodologies at different design layers (from system down to integrated circuits). Motivated by the wide appeal of Approximate Computing over the last 10 years, we conduct a two-part survey to cover key aspects (e.g., terminology and applications) and review the state-of-the art approximation techniques from all layers of the traditional computing stack. In Part II of our survey, we classify and present the technical details of application-specific and architectural approximation techniques, which both target the design of resource-efficient processors/accelerators&systems. Moreover, we present a detailed analysis of the application spectrum of Approximate Computing and discuss open challenges and future directions.
3D Skeletonization of Complex Grapevines for Robotic Pruning

Robotic pruning of dormant grapevines is an area of active research in order to promote vine balance and grape quality, but so far robotic efforts have largely focused on planar, simplified vines not representative of commercial vineyards. This paper aims to advance the robotic perception capabilities necessary for pruning in denser and more complex vine structures by extending plant skeletonization techniques. The proposed pipeline generates skeletal grapevine models that have lower reprojection error and higher connectivity than baseline algorithms. We also show how 3D and skeletal information enables prediction accuracy of pruning weight for dense vines surpassing prior work, where pruning weight is an important vine metric influencing pruning site selection.
Effect of self invertible matrix on cipher tetragraphic trifunction

In this paper, we give some solutions L2×2 for L2×23≡A2×2(modN) when A2×2 is zero, identity, [ ef00 ] and [ ef0h ] matrices. As a result, the use of a secret key L4×4=[ L2×2I−L2×2I+L2×2−L2×2 ] should be avoided in order to enhance the security of Cipher Tetragraphic Trifunction transformations, C4×J(t)≡L4×4P4×j(modN) where t∈ { 1,2,3 }, C4×j(t) is ciphertext and P4×j is plaintext.
Internet of Things-based real-time production logistics synchronization mechanism and method toward customer order dynamics

The reluctance or incapability of further increasing production resources has made many enterprises suffering from high resource-workload situation. Production dynamics thus cannot be resolved by single resource independently, yet have to make an integral use of the adjustable capability of multiple resources of the whole system in a synchronized way. This paper considers a dynamic production logistics (PL) process comprising multiple independently operated PL stages, which adopts Internet of Things to capture real-time execution dynamics and rely on plan (re)scheduling and cloud-based resources re-configuration to cope with dynamics. A generic dynamic production logistics synchronization (PLS) solution is proposed. Qualitatively, a multi-phase multi-stage multi-degree synchronization control mechanism is put forward toward a PL system with generic structure and typical execution dynamics. Quantitatively, collaborative optimization is applied to assist the PLS to obtain the synchronization results. With a real-life case study, the effectiveness of the proposed mechanism and method has been verified. A set of sensitivity analysis is also conducted toward dynamics of different degree and different time, which provides significant managerial implication to PL managers to better deal with dynamics.
Brillouin optical time-domain reflectometer

The utility model provides a Brillouin optical time domain reflectometer, which comprises a light source assembly, a first optical path assembly, a second optical path assembly, a first coupling assembly, a second coupling assembly, and a detection and processing assembly. The light source assembly generates two beams of laser light. One beam of laser light is transmitted to a sensing optical fiber to form Brillouin scattered light by the first optical path assembly, and the other beam of laser light passes through the second optical path assembly to form a local oscillator. The second coupling assembly is located between the first optical path assembly and the first coupling assembly. The Brillouin scattered light transmitted from the first optical path assembly is divided into two beams, wherein one beam is transmitted to the first coupling assembly, and Rayleigh scattered light is fetched from the other beam and received by the detection and processing assembly. The Brillouin scattered light and the local-oscillator are coupled to the detection and processing assembly by the first coupling assembly. The detection and processing assembly calculates temperature and stress of the sensing optical fiber according to the Brillouin scattered light signal and the Rayleigh scattered light. Measurement errors on stress and temperature can be reduced, and spatial resolution is capable of being improved by utilizing the utility model.