Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving

Multi-camera perception tasks have gained significant attention in the field of autonomous driving. However, existing frameworks based on Lift-Splat-Shoot (LSS) in the multi-camera setting cannot produce suitable dense 3D features due to the projection nature and uncontrollable densification process. To resolve this problem, we propose to regulate intermediate dense 3D features with the help of volume rendering. Specifically, we employ volume rendering to process the dense 3D features to obtain corresponding 2D features (e.g., depth maps, semantic maps), which are supervised by associated labels in the training. This manner regulates the generation of dense 3D features on the feature level, providing appropriate dense and unified features for multiple perception tasks. Therefore, our approach is termed Vampire, stands for ``Volume rendering As Multi-camera Perception Intermediate feature REgulator''. Experimental results on the Occ3D and nuScenes datasets demonstrate that Vampire facilitates fine-grained and appropriate extraction of dense 3D features, and is competitive with existing SOTA methods across diverse downstream perception tasks like 3D occupancy prediction, LiDAR segmentation and 3D objection detection, while utilizing moderate GPU resources. We provide a video demonstration in the supplementary materials and Codes are available at github.com/cskkxjk/Vampire.
Pose2Gaze: Generating Realistic Human Gaze Behaviour from Full-body Poses using an Eye-body Coordination Model

While generating realistic body movements, e.g., for avatars in virtual reality, is widely studied in computer vision and graphics, the generation of eye movements that exhibit realistic coordination with the body remains under-explored. We first report a comprehensive analysis of the coordination of human eye and full-body movements during everyday activities based on data from the MoGaze and GIMO datasets. We show that eye gaze has strong correlations with head directions and also full-body motions and there exists a noticeable time delay between body and eye movements. Inspired by the analyses, we then present Pose2Gaze -- a novel eye-body coordination model that first uses a convolutional neural network and a spatio-temporal graph convolutional neural network to extract features from head directions and full-body poses respectively and then applies a convolutional neural network to generate realistic eye movements. We compare our method with state-of-the-art methods that predict eye gaze only from head movements for three different generation tasks and demonstrate that Pose2Gaze significantly outperforms these baselines on both datasets with an average improvement of 26.4% and 21.6% in mean angular error, respectively. Our findings underline the significant potential of cross-modal human gaze behaviour analysis and modelling.
Stronger Graph Transformer with Regularized Attention Scores

Graph Neural Networks are notorious for its memory consumption. A recent Transformer-based GNN called Graph Transformer is shown to obtain superior performances when long range dependencies exist. However, combining graph data and Transformer architecture led to a combinationally worse memory issue. We propose a novel version of"edge regularization technique"that alleviates the need for Positional Encoding and ultimately alleviate GT's out of memory issue. We observe that it is not clear whether having an edge regularization on top of positional encoding is helpful. However, it seems evident that applying our edge regularization technique indeed stably improves GT's performance compared to GT without Positional Encoding.
The PMT System of the TRIDENT Pathfinder Experiment

Next generation neutrino telescopes are highly anticipated to boost the development of neutrino astronomy. A multi-cubic-kilometer neutrino telescope, TRopIcal DEep-sea Neutrino Telescope (TRIDENT), was proposed to be built in the South China Sea. The detector aims to achieve ~ 0.1 degree angular resolution for track-like events at energy above 100 TeV by using hybrid digital optical modules, opening new opportunities for neutrino astronomy. In order to measure the water optical properties and marine environment of the proposed TRIDENT site, a pathfinder experiment was conducted, in which a 100-meter-long string consisting of three optical modules was deployed at a depth of 3420 m to perform in-situ measurements. The central module emits light by housing LEDs, whereas the other two modules detect light with two independent and complementary systems: the PMT and the camera systems. By counting the number of detected photons and analyzing the photon arrival time distribution, the PMT system can measure the absorption and scattering lengths of sea water, which serve as the basic inputs for designing the neutrino telescope. In this paper, we present the design concept, calibration and performance of the PMT system in the pathfinder experiment.
Two-step electronic response to magnetic ordering in a van der Waals ferromagnet

The two-dimensional (2D) material Cr$_2$Ge$_2$Te$_6$ is a member of the class of insulating van der Waals magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te $p$-orbital-dominated bands to undergo changes at the Curie transition temperature T$_C$ while the Cr $d$-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets.
An operational definition of quantum information scrambling

Quantum information scrambling (QIS) is a characteristic feature of several quantum systems, ranging from black holes to quantum communication networks. While accurately quantifying QIS is crucial to understanding many such phenomena, common approaches based on the tripartite information have limitations due to the accessibility issues of quantum mutual information, and do not always properly take into consideration the dependence on the encoding input basis. To address these issues, we propose a novel and computationally efficient QIS quantifier, based on a formulation of QIS in terms of quantum state discrimination. We show that the optimal guessing probability, which reflects the degree of QIS induced by an isometric quantum evolution, is directly connected to the accessible min-information, a generalized channel capacity based on conditional min-entropy, which can be cast as a convex program and thus computed efficiently. By applying our proposal to a range of examples with increasing complexity, we illustrate its ability to capture the multifaceted nature of QIS in all its intricacy.
Initializing Services in Interactive ML Systems for Diverse Users

This paper studies ML systems that interactively learn from users across multiple subpopulations with heterogeneous data distributions. The primary objective is to provide specialized services for different user groups while also predicting user preferences. Once the users select a service based on how well the service anticipated their preference, the services subsequently adapt and refine themselves based on the user data they accumulate, resulting in an iterative, alternating minimization process between users and services (learning dynamics). Employing such tailored approaches has two main challenges: (i) Unknown user preferences: Typically, data on user preferences are unavailable without interaction, and uniform data collection across a large and diverse user base can be prohibitively expensive. (ii) Suboptimal Local Solutions: The total loss (sum of loss functions across all users and all services) landscape is not convex even if the individual losses on a single service are convex, making it likely for the learning dynamics to get stuck in local minima. The final outcome of the aforementioned learning dynamics is thus strongly influenced by the initial set of services offered to users, and is not guaranteed to be close to the globally optimal outcome. In this work, we propose a randomized algorithm to adaptively select very few users to collect preference data from, while simultaneously initializing a set of services. We prove that under mild assumptions on the loss functions, the expected total loss achieved by the algorithm right after initialization is within a factor of the globally optimal total loss with complete user preference data, and this factor scales only logarithmically in the number of services. Our theory is complemented by experiments on real as well as semi-synthetic datasets.
TeamCAD - A Multimodal Interface for Remote Computer Aided Design

Remote collaboration is a common reality of spatial design processes, but tools for computer aided design were made for single users. Via TeamCAD, we introduce a user experience where online remote collaboration experience is more like working on a table. Using speech and gesture recognition based on state of the art machine learning through webcam and microphone input, TeamCAD plugs into existing software through API's, keybindings, and mouse input. We share results from user studies conducted on graduate students from. Our user tests were run on Blender animation software, making simultaneous use of both modalities for given tasks. We mitigated challenges in terms of robustness and latency in readily available voice recognition models. Our prototype has proven to be an intuitive interface, providing a suitable denominator for collaborators with or without previous experience in three-dimensional modeling applications.
Chasing Fairness in Graphs: A GNN Architecture Perspective

There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strategies. For fairness in graphs, recent studies achieve fair representations and predictions through either graph data pre-processing (e.g., node feature masking, and topology rewiring) or fair training strategies (e.g., regularization, adversarial debiasing, and fair contrastive learning). How to achieve fairness in graphs from the model architecture perspective is less explored. More importantly, GNNs exhibit worse fairness performance compared to multilayer perception since their model architecture (i.e., neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness via a new GNN architecture. We propose Fair Message Passing (FMP) designed within a unified optimization framework for GNNs. Notably, FMP explicitly renders sensitive attribute usage in forward propagation for node classification task using cross-entropy loss without data pre-processing. In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. In this way, FMP scheme can aggregate useful information from neighbors and mitigate bias to achieve better fairness and prediction tradeoff performance. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets. The code is available at https://github.com/zhimengj0326/FMP.
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach

StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness. Previous works, such as Alphastar and SCC, achieve impressive performance on tackling StarCraft II , however, still exhibit deficiencies in long term strategic planning and strategy interpretability. Emerging large language model (LLM) agents, such as Voyage and MetaGPT, presents the immense potential in solving intricate tasks. Motivated by this, we aim to validate the capabilities of LLMs on StarCraft II, a highly complex RTS game.To conveniently take full advantage of LLMs` reasoning abilities, we first develop textual StratCraft II environment, called TextStarCraft II, which LLM agent can interact. Secondly, we propose a Chain of Summarization method, including single frame summarization for processing raw observations and multi frame summarization for analyzing game information, providing command recommendations, and generating strategic decisions. Our experiment consists of two parts: first, an evaluation by human experts, which includes assessing the LLMs`s mastery of StarCraft II knowledge and the performance of LLM agents in the game; second, the in game performance of LLM agents, encompassing aspects like win rate and the impact of Chain of Summarization.Experiment results demonstrate that: 1. LLMs possess the relevant knowledge and complex planning abilities needed to address StarCraft II scenarios; 2. Human experts consider the performance of LLM agents to be close to that of an average player who has played StarCraft II for eight years; 3. LLM agents are capable of defeating the built in AI at the Harder(Lv5) difficulty level. We have open sourced the code and released demo videos of LLM agent playing StarCraft II.
COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal

In recent years, there has been a growing interest in exploring dialogues with more complex goals, such as negotiation, persuasion, and emotional support, which go beyond traditional service-focused dialogue systems. Apart from the requirement for much more sophisticated strategic reasoning and communication skills, a significant challenge of these tasks lies in the difficulty of objectively measuring the achievement of their goals in a quantifiable way, making it difficult for existing research to directly optimize the dialogue procedure towards them. In our work, we emphasize the multifaceted nature of complex dialogue goals and argue that it is more feasible to accomplish them by comprehensively considering and jointly promoting their different aspects. To this end, we propose a novel dialogue framework, Cooper, which coordinates multiple specialized agents, each dedicated to a specific dialogue goal aspect separately, to approach the complex objective. Through this divide-and-conquer manner, we make complex dialogue goals more approachable and elicit greater intelligence via the collaboration of individual agents. Experiments on persuasion and emotional support dialogues demonstrate the superiority of our method over a set of competitive baselines. Our codes are available at https://github.com/YiCheng98/Cooper.
Solvable entanglement dynamics in quantum circuits with generalized dual unitarity

We study the non-equilibrium dynamics of kicked Ising models in $1+1$ dimensions which have interactions alternating between odd and even bonds in time. These models give rise to time-evolution equivalent to quantum circuits having both the global property of tri-unitarity (three 'arrows of time') and also the local property of second-level dual-unitarity, which constrains the behavior of pairs of local gates underlying the circuit under a space-time rotation. We identify a broad class of initial product states wherein the effect of the environment on a small subsystem can be exactly represented by influence matrices with simple Markovian structures, resulting in the subsystem's full dynamics being efficiently computable. We further find additional conditions under which the dynamics of entanglement can be solved for all times, yielding rich phenomenology ranging from linear growth at half the maximal speed allowed by locality, followed by saturation to maximum entropy (i.e., thermalization to infinite temperature); to entanglement growth with saturation to extensive but sub-maximal entropy. Our findings extend our knowledge of interacting quantum systems whose thermalizing dynamics can be efficiently and analytically computed, going beyond the well-known examples of integrable models, Clifford circuits, and dual-unitary circuits.
ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room

Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR). However, it also comes with new challenges, requiring strong team coordination and effective OR management. Automatic detection of surgical activities is a key requirement for developing AI-based intelligent tools to tackle these challenges. The current state-of-the-art surgical activity recognition methods however operate on image-based representations and depend on large-scale labeled datasets whose collection is time-consuming and resource-expensive. This work proposes a new sample-efficient and object-based approach for surgical activity recognition in the OR. Our method focuses on the geometric arrangements between clinicians and surgical devices, thus utilizing the significant object interaction dynamics in the OR. We conduct experiments in a low-data regime study for long video activity recognition. We also benchmark our method againstother object-centric approaches on clip-level action classification and show superior performance.
Entanglement transition in deep neural quantum states

Despite the huge theoretical potential of neural quantum states, their use in describing generic, highly-correlated quantum many-body systems still often poses practical difficulties. Customized network architectures are under active investigation to address these issues. For a guided search of suited network architectures a deepened understanding of the link between neural network properties and attributes of the physical system one is trying to describe, is imperative. Drawing inspiration from the field of machine learning, in this work we show how information propagation in deep neural networks impacts the physical entanglement properties of deep neural quantum states. In fact, we link a previously identified information propagation phase transition of a neural network to a similar transition of entanglement in neural quantum states. With this bridge we can identify optimal neural quantum state hyperparameter regimes for representing area as well as volume law entangled states. The former are easily accessed by alternative methods, such as tensor network representations, at least in low physical dimensions, while the latter are challenging to describe generally due to their extensive quantum entanglement. This advance of our understanding of network configurations for accurate quantum state representation helps to develop effective representations to deal with volume-law quantum states, and we apply these findings to describe the ground state (area law state) vs. the excited state (volume law state) properties of the prototypical next-nearest neighbor spin-1/2 Heisenberg model.
Tracking Intrinsic Non-Hermitian Skin Effect in Lossy Lattices

Non-Hermitian skin effect (NHSE), characterized by a majority of eigenstates localized at open boundaries, is one of the most iconic phenomena in non-Hermitian lattices. Despite notable experimental studies implemented, most of them witness only certain signs of the NHSE rather than the intrinsic exponential localization inherent in eigenstates, owing to the ubiquitous and inevitable background loss. Even worse, the experimental observation of the NHSE would be completely obscured in highly lossy cases. Here, we theoretically propose a dual test approach to eliminate the destructive loss effect and track the intrinsic NHSE that is essentially irrelevant to background loss. Experimentally, the effectiveness of this approach is precisely validated by one- and two-dimensional non-Hermitian acoustic lattices. Our study sheds new light on the previously untapped intrinsic aspect of the NHSE, which is of particular significance in non-Hermitian topological physics.
Terrapin Attack: Breaking SSH Channel Integrity By Sequence Number Manipulation

The SSH protocol provides secure access to network services, particularly remote terminal login and file transfer within organizational networks and to over 15 million servers on the open internet. SSH uses an authenticated key exchange to establish a secure channel between a client and a server, which protects the confidentiality and integrity of messages sent in either direction. The secure channel prevents message manipulation, replay, insertion, deletion, and reordering. In this paper, we show that as new encryption algorithms and mitigations were added to SSH, the SSH Binary Packet Protocol is no longer a secure channel: SSH channel integrity (INT-PST) is broken for three widely used encryption modes. This allows prefix truncation attacks where some encrypted packets at the beginning of the SSH channel can be deleted without the client or server noticing it. We demonstrate several real-world applications of this attack. We show that we can fully break SSH extension negotiation (RFC 8308), such that an attacker can downgrade the public key algorithms for user authentication or turn off a new countermeasure against keystroke timing attacks introduced in OpenSSH 9.5. We also identified an implementation flaw in AsyncSSH that, together with prefix truncation, allows an attacker to redirect the victim's login into a shell controlled by the attacker. In an internet-wide scan for vulnerable encryption modes and support for extension negotiation, we find that 77% of SSH servers support an exploitable encryption mode, while 57% even list it as their preferred choice. We identify two root causes that enable these attacks: First, the SSH handshake supports optional messages that are not authenticated. Second, SSH does not reset message sequence numbers when encryption is enabled. Based on this analysis, we propose effective and backward-compatible changes to SSH that mitigate our attacks.