Multimodal large language models (MLLMs), built on large-scale pre-trained vision towers and language models, have shown great capabilities in multimodal understanding. However, most existing MLLMs are trained on single-turn vision question-answering tasks, which do not accurately reflect real-world human conversations. In this paper, we introduce MMDiag, a multi-turn multimodal dialogue dataset. This dataset is collaboratively generated through deliberately designed rules and GPT assistance, featuring strong correlations between questions, between questions and images, and among different image regions; thus aligning more closely with real-world scenarios. MMDiag serves as a strong benchmark for multi-turn multimodal dialogue learning and brings more challenges to the grounding and reasoning capabilities of MLLMs. Further, inspired by human vision processing, we present DiagNote, an MLLM equipped with multimodal grounding and reasoning capabilities. DiagNote consists of two modules (Deliberate and Gaze) interacting with each other to perform Chain-of-Thought and annotations respectively, throughout multi-turn dialogues. We empirically demonstrate the advantages of DiagNote in both
The Tor network offers network anonymity to its users by routing their traffic through a sequence of relays. A group of nine directory authorities maintains information about all available relay nodes using a distributed directory protocol. We observe that the current protocol makes a strong synchrony assumption, which makes it vulnerable to natural as well as adversarial non-synchronous communication scenarios over the Internet. In this paper, we show that it is possible to cause a failure in the Tor directory protocol by targeting a majority of the authorities for only five minutes using a well-executed distributed denial-of-service (DDoS) attack. We demonstrate this attack in a controlled environment and show that it is cost-effective for as little as \$53.28 per month to disrupt the protocol and to effectively bring down the entire Tor network. To mitigate this problem, we consider the popular partial synchrony assumption that ensures protocol security even when the network delays are large and unknown initially. We design a new Tor directory protocol that leverages a standard partial-synchronous consensus protocol to solve this problem, while also proving its security. We have
Bringing AI to vehicles and enabling them as sensing platforms is key to transforming maintenance from reactive to proactive. Now is the time to integrate AI copilots that speak both languages: machine and driver. This article offers a conceptual and technical perspective intended to spark interdisciplinary dialogue and guide future research and development in intelligent vehicle systems, predictive maintenance, and AI-powered user interaction.
The predicted and observed space density of cataclysmic variables (CVs) have been for a long time discrepant by at least an order of magnitude. The standard model of CV evolution predicts that the vast majority of CVs should be period bouncers, whose space density has been recently measured to be $ρ\lesssim 2 \times 10^{-5}$ pc$^{-3}$. We performed population synthesis of CVs using an updated version of the Binary Stellar Evolution (BSE) code for single and binary star evolution. We find that the recently suggested empirical prescription of consequential angular momentum loss (CAML) brings into agreement predicted and observed space densities of CVs and period bouncers. To progress with our understanding of CV evolution it is crucial to understand the physical mechanism behind empirical CAML. Our changes to the BSE code are also provided in details, which will allow the community to accurately model mass transfer in interacting binaries in which degenerate objects accrete from low-mass main-sequence donor stars.
Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of bright-field incoherent microscopy.
There are numerous types of programming languages developed in the last decades, and most of them provide interface to call C++ or C for high efficiency implementation. The motivation of Svar is to design an efficient, light-weighted and general middle-ware for multiple languages, meanwhile, brings the dynamism features from script language to C++ in a straightforward way. Firstly, a Svar class with JSON like data structure is designed to hold everything exists in C++, including basic values, functions or user defined classes and objects. Secondly, arguments are auto cast to and from Svar efficiently with compile time pointers, references and shared\_ptr detection. Thirdly, classes and functions are binded with string names to support reflection, this means all functions and classes in a shared library can be exported to a Svar object, which also calls a Svar module. The Svar modules can be accessed by different languages and this paper demonstrates how to import and use a Svar module in Python and Node.js. Moreover, the Svar modules or even a python module can also be imported by C++ at runtime, which makes C++ easier to compile and use since headers are not required anymore. We c
The low degradability of common polymers composed of light elements, results in a serious impact on the environment, which has become an urgent problem to be solved. As the reverse process of monomer polymerization, what deviates degradation from the idealized sequential depolymerization process, thereby bringing strange degradation products or even hindering further degradation? This is a key issue at the atomic level that must be addressed. Herein, we reveal that hydrogen atom transfer (HAT) during degradation, which is usually attributed to the thermal effect, unexpectedly exhibits a strong high-temperature tunnelling effect. This gives a possible answer to the above question. High-precision first-principles calculations show that, in various possible HAT pathways, lower energy barrier and stronger tunnelling effect make the HAT reaction related to the active end of the polymer occur more easily. In particular, although the energy barrier of the HAT reaction is only of 0.01 magnitude different from depolymerization, the tunnelling probability of the former can be 14~32 orders of magnitude greater than that of the latter. Furthermore, chain scission following HAT will lead to a v
Affective visualization is increasingly studied in visualization research, yet how designers bring emotions into their visualization work remains unexplored. This paper addresses this gap through semi-structured interviews with 15 visualization practitioners. Using hybrid thematic analysis, we identify: (1) three functions that emotions can serve for viewers (entry, engagement, outcome); (2) three facets of how designers work with emotion (data, design, audience), along with design strategies; and (3) ethical considerations in the design process. We also observe that affective intent often emerges during the design process rather than being planned from the outset, and that emotional impact arises from accumulated design choices rather than isolated visual elements. Finally, we highlight evaluation as a key challenge identified by our participants.
Deep Neural Networks (DNNs) have achieved remarkable success across various intelligent tasks but encounter performance and energy challenges in inference execution due to data movement bottlenecks. We introduce DataMaestro, a versatile and efficient data streaming unit that brings the decoupled access/execute architecture to DNN dataflow accelerators to address this issue. DataMaestro supports flexible and programmable access patterns to accommodate diverse workload types and dataflows, incorporates fine-grained prefetch and addressing mode switching to mitigate bank conflicts, and enables customizable on-the-fly data manipulation to reduce memory footprints and access counts. We integrate five DataMaestros with a Tensor Core-like GeMM accelerator and a Quantization accelerator into a RISC-V host system for evaluation. The FPGA prototype and VLSI synthesis results demonstrate that DataMaestro helps the GeMM core achieve nearly 100% utilization, which is 1.05-21.39x better than state-of-the-art solutions, while minimizing area and energy consumption to merely 6.43% and 15.06% of the total system.
Shallow node embeddings like node2vec (N2V) can be used for nodes without features or to supplement existing features with structure-based information. Embedding methods like N2V are limited in their application on new nodes, which restricts them to the transductive setting where the entire graph, including the test nodes, is available during training. We propose inductive node2vec (iN2V), which combines a post-hoc procedure to compute embeddings for nodes unseen during training and modifications to the original N2V training procedure to prepare the embeddings for this post-hoc procedure. We conduct experiments on several benchmark datasets and demonstrate that iN2V is an effective approach to bringing transductive embeddings to an inductive setting. Using iN2V embeddings improves node classification by 1 point on average, with up to 6 points of improvement depending on the dataset and the number of unseen nodes. Our iN2V is a plug-in approach to create new or enrich existing embeddings. It can also be combined with other embedding methods, making it a versatile approach for inductive node representation learning. Code to reproduce the results is available at https://github.com/Foi
The construction of the High Intensity heavy ion Accelerator Facility (HIAF) has been completed, with current efforts focused on subsystem commissioning. Beam commissioning is scheduled for autumn 2025, marking a critical milestone in the HIAF project. This paper presents high-precision optics calculations for the Booster Ring (BRing) of HIAF, a key component for achieving stable heavy-ion beam acceleration. Leveraging high-precision magnetic field data, each magnet is divided into hundreds of slices, thus establishing a high-precision sliced optics model for BRing. Detailed calculations of BRing's optics are presented in this work. Critical parameters including tunes and betatron functions of the lattice based on the measured magnetic fields and those of the ideal lattice have been compared. The results highlight the impact of realistic magnetic field on beam dynamics and provide essential insights for accelerator tuning and optimization. These findings serve as a fundamental reference for beam commissioning and long-term operation, ensuring beam stability and performance reproducibility in HIAF.
In this paper, we explicitly compute two kinds of algebraic Belyi functions on Bring's curve. One is related to a congruence subgroup of ${\rm SL}_2(\mathbb{Z})$ and the other is related to a congruence subgroup of the triangle group $Δ(2,4,5)\subset \SL_2(\R)$. To carry out the computation, we use elliptic cusp forms of weight 2 for the former case and the automorphism group of Bring's curve for the latter case. We also discuss a suitable base field (a number field) for describing isomorphisms between Hulek-Craig's curve, Bring's curve, and another algebraic model obtained as a modular curve.
In this paper, we present FaceShot, a novel training-free portrait animation framework designed to bring any character into life from any driven video without fine-tuning or retraining. We achieve this by offering precise and robust reposed landmark sequences from an appearance-guided landmark matching module and a coordinate-based landmark retargeting module. Together, these components harness the robust semantic correspondences of latent diffusion models to produce facial motion sequence across a wide range of character types. After that, we input the landmark sequences into a pre-trained landmark-driven animation model to generate animated video. With this powerful generalization capability, FaceShot can significantly extend the application of portrait animation by breaking the limitation of realistic portrait landmark detection for any stylized character and driven video. Also, FaceShot is compatible with any landmark-driven animation model, significantly improving overall performance. Extensive experiments on our newly constructed character benchmark CharacBench confirm that FaceShot consistently surpasses state-of-the-art (SOTA) approaches across any character domain. More re
Animated characters often move in non-physical ways and have proportions that are far from a typical walking robot. This provides an ideal platform for innovation in both mechanical design and stylized motion control. In this paper, we bring Olaf to life in the physical world, relying on reinforcement learning guided by animation references for control. To create the illusion of Olaf's feet moving along his body, we hide two asymmetric legs under a soft foam skirt. To fit actuators inside the character, we use spherical and planar linkages in the arms, mouth, and eyes. Because the walk cycle results in harsh contact sounds, we introduce additional rewards that noticeably reduce impact noise. The large head, driven by small actuators in the character's slim neck, creates a risk of overheating, amplified by the costume. To keep actuators from overheating, we feed temperature values as additional inputs to policies, introducing new rewards to keep them within bounds. We validate the efficacy of our modeling in simulation and on hardware, demonstrating an unmatched level of believability for a costumed robotic character.
The High Intensity heavy ion Accelerator Facility (HIAF) successfully accelerated the 18O6+ beam on October 27, 2025. This paper presents a further simulation study on the high-precision optics, namely sliced optics, of the Booster Ring (BRing) at HIAF based on measured magnetic fields, focusing on three aspects: (1) closed-orbit distortion (COD) and variations in optical parameters induced by errors; (2) closed-orbit correction; (3) dynamic aperture. Specifically, detailed investigations are conducted on COD and optical parameter variations caused by magnet alignment errors and dipole magnet field errors, alongside simulations of closed-orbit correction and detailed calculations of BRing's dynamic aperture. Results show the sliced optics outperforms the original optics in COD control. Without chromaticity correction, its dynamic aperture is superior to the original; after chromaticity correction, it remains comparable. This study provides valuable insights for accelerator tuning and optimization.
Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson's terms. Second, we construct a Legal Question Bank (LQB), which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive CLIC Recommender (CRec). Given a user's verbal description of a legal situation that requires a legal solution, CRec interprets the user's input and shortlists questions from the question bank that are most likely r
In the area of multi-objective evolutionary algorithms (MOEAs), there is a trend of using an archive to store non-dominated solutions generated during the search. This is because 1) MOEAs may easily end up with the final population containing inferior solutions that are dominated by other solutions discarded during the search process and 2) the population that has a commensurable size of the problem's Pareto front is often not practical. In this paper, we theoretically show, for the first time, that using an archive can guarantee speed-ups for MOEAs. Specifically, we prove that for two well-established MOEAs (NSGA-II and SMS-EMOA) on two commonly studied problems (OneMinMax and LeadingOnesTrailingZeroes), using an archive brings a polynomial acceleration on the expected running time. The reason is that with an archive, the size of the population can reduce to a small constant; there is no need for the population to keep all the Pareto optimal solutions found. This contrasts existing theoretical studies for MOEAs where a population with a commensurable size of the problem's Pareto front is needed. The findings in this paper not only provide a theoretical confirmation for an increasi
Multi-tenant cloud computing provides great benefits in terms of resource sharing, elastic pricing, and scalability, however, it also changes the security landscape and introduces the need for strong isolation between the tenants, also inside the network. This paper is motivated by the observation that while multi-tenancy is widely used in cloud computing, the virtual switch designs currently used for network virtualization lack sufficient support for tenant isolation. Hence, we present, implement, and evaluate a virtual switch architecture, MTS, which brings secure design best-practice to the context of multi-tenant virtual networking: compartmentalization of virtual switches, least-privilege execution, complete mediation of all network communication, and reducing the trusted computing base shared between tenants. We build MTS from commodity components, providing an incrementally deployable and inexpensive upgrade path to cloud operators. Our extensive experiments, extending to both micro-benchmarks and cloud applications, show that, depending on the way it is deployed, MTS may produce 1.5-2x the throughput compared to state-of-the-art, with similar or better latency and modest re
The modular group $Γ$ (which is the Hecke group $H_3$) can be used to study triangular maps. Here we use the Hecke group $H_4$ to study the regular map that underlies Bring's surface of genus 4. Our main result is the determination of the 20-sided hyperbolic polygon that gives Bring's surface.