Miracle-dz/UniFAR-FTUs huggingface dataset:
Mutual Distillation Learning For Person Re-Identification

With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements. However, the majority of prior works have traditionally focused on solving the problem via extracting features solely from a single perspective, such as uniform partitioning, hard attention mechanisms, or semantic masks. While these approaches have demonstrated efficacy within specific contexts, they fall short in diverse situations. In this paper, we propose a novel approach, Mutual Distillation Learning For Person Re-identification (termed as MDPR), which addresses the challenging problem from multiple perspectives within a single unified model, leveraging the power of mutual distillation to enhance the feature representations collectively. Specifically, our approach encompasses two branches: a hard content branch to extract local features via a uniform horizontal partitioning strategy and a Soft Content Branch to dynamically distinguish between foreground and background and facilitate the extraction of multi-granularity features via a carefully designed attention mechanism. To facilitate knowledge exchange between these two branches, a mutual distillation and fusion process is employed, promoting the capability of the outputs of each branch. Extensive experiments are conducted on widely used person ReID datasets to validate the effectiveness and superiority of our approach. Notably, our method achieves an impressive $88.7\%/94.4\%$ in mAP/Rank-1 on the DukeMTMC-reID dataset, surpassing the current state-of-the-art results. Our source code is available at https://github.com/KuilongCui/MDPR.
Organic room-temperature polariton condensate in a higher-order topological lattice

Organic molecule exciton-polaritons in photonic lattices are a versatile platform to emulate unconventional phases of matter at ambient conditions, including protected interface modes in topological insulators. Here, we investigate bosonic condensation in the most prototypical higher-order topological lattice: a 2D-version of the Su-Schrieffer-Heeger (SSH) model, supporting both 0D and 1D topological modes. We study fluorescent protein-filled, structured microcavities defining a staggered photonic trapping potential and observe the resulting first- and higher-order topologically protected modes via spatially resolved photoluminescence spectroscopy. We account for the spatial mode patterns by tight-binding calculations and theoretically characterize the topological invariants of the lattice. Under strong optical pumping, we observe bosonic condensation into the topological modes. Via interferometric measurements, we map the spatial first-order coherence in the protected 1D modes extending over 10 microns. Our findings pave the way towards organic on-chip polaritonics using higher-order topology as a tool for the generation of robustly confined polaritonic lasing states.
Modelling and predicting online vaccination views using bow-tie decomposition

Social media has become increasingly important in shaping public vaccination views, especially since the COVID-19 outbreak. This paper uses bow-tie structure to analyse a temporal dataset of directed online social networks that represent the information exchange among anti-vaccination, pro-vaccination and neutral Facebook pages. Bow-tie structure decomposes a network into seven components, with two components, strongly connected component (SCC) and out-periphery component (OUT), emphasized in this paper: SCC is the largest strongly connected component, acting as an ‘information magnifier’, and OUT contains all nodes with a directed path from a node in SCC, acting as an ‘information creator’. We consistently observe statistically significant bow-tie structures with different dominant components for each vaccination group over time. In particular, the anti-vaccination group has a large OUT, and the pro-vaccination group has a large SCC. We further investigate changes in opinions over time, as measured by fan count variations, using agent-based simulations and machine learning models. Across both methods, accounting for bow-tie decomposition better reflects information flow differences among vaccination groups and improves our opinion dynamics prediction results. The modelling frameworks we consider can be applied to any multi-stance temporal network and could form a basis for exploring opinion dynamics using bow-tie structure in a wide range of applications.
Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion

Generative models of macromolecules carry abundant and impactful implications for industrial and biomedical efforts in protein engineering. However, existing methods are currently limited to modeling protein structures or sequences, independently or jointly, without regard to the interactions that commonly occur between proteins and other macromolecules. In this work, we introduce MMDiff, a generative model that jointly designs sequences and structures of nucleic acid and protein complexes, independently or in complex, using joint SE(3)-discrete diffusion noise. Such a model has important implications for emerging areas of macromolecular design including structure-based transcription factor design and design of noncoding RNA sequences. We demonstrate the utility of MMDiff through a rigorous new design benchmark for macromolecular complex generation that we introduce in this work. Our results demonstrate that MMDiff is able to successfully generate micro-RNA and single-stranded DNA molecules while being modestly capable of joint modeling DNA and RNA molecules in interaction with multi-chain protein complexes. Source code: https://github.com/Profluent-Internships/MMDiff.
Stochastic particle-resolved modeling of atmospheric aerosols and their climate impacts

Here we present the development and application of particle-based methods to represent atmospheric aerosols in numerical weather prediction and air quality models. Measurements show that each atmospheric aerosol particle consists of a complex mixture of chemical species, such as soluble inorganic salts and acids, insoluble crustal materials (dust), trace metals, and carbonaceous materials. An aerosol particle population therefore constitutes a high-dimensional distribution in the space of possible compositions. Aerosol particles influence the large-scale dynamics of the atmosphere and climate because they interact with solar radiation, both directly by scattering and absorbing light and indirectly by acting as cloud condensation nuclei (CCN). Their sizes range from nanometers to micrometers, and a major source of difficulty in understanding the climate impact of aerosols is due to scale interactions between nanoscale particles and global-scale weather and climate effects. Limited by computational resources, previous modeling efforts have used simplifying assumptions regarding the aerosol composition, such as assuming that all particles of a given size have identical chemical composition. This can lead to significant errors in the prediction of aerosol climate impacts. To alleviate a priori assumptions regarding aerosol composition, particle-resolved techniques are advantageous because of their ability to simulate the high-dimensional distribution of particle compositions. In this work, we present the development of the particle-resolved aerosol model PartMC-MOSAIC [1] coupled to the Weather Research and Forecast (WRF) model [2], a regional weather model with domains on the order of hundreds of kilometers. The WRF-PartMC-MOSAIC model uses a 3D Eulerian grid for the atmospheric fluid flow, while explicitly resolving the evolution of individual aerosol particles within a representative volume per grid cell. Particles are represented as vectors of composition and exact particle locations within a grid cell are not tracked. Since particle-resolved techniques have high computational cost, efficient stochastic sampling techniques are used to simulate coagulation, emission, dry deposition and advection-diffusion of particles. The cost to simulate coagulation is proportional to number of coagulation events. For dry deposition, cost is proportional to particle removal rates, and for transport it is proportional to advection-diffusion rates. We use this new model to quantify the importance of aerosol composition for the aerosol impact on climate by determining the errors in simulated climate-relevant quantities, such as heating rates and CCN activity, that arise from the simplified aerosol representation.
Sprout: Designing Expressivity for Robots Using Fiber-Embedded Actuator

In this paper, we explore how techniques from soft robotics can help create a new form of robot expression. We present Sprout, a soft expressive robot that conveys its internal states by changing its body shape. Sprout can extend, bend, twist, and expand using fiber-embedded actuators integrated into its construction. These deformations enable Sprout to express its internal states, for example, by expanding to express anger and bending its body sideways to express curiosity. Through two user studies, we investigated how users interpreted Sprout's expressions, their perceptions of Sprout, and their expectations from future iterations of Sprout's design. We argue that the use of soft actuators opens a novel design space for robot expressions to convey internal states, emotions, and intent.
Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries

This paper solves the discretised multiphase flow equations using tools and methods from machine-learning libraries. The idea comes from the observation that convolutional layers can be used to express a discretisation as a neural network whose weights are determined by the numerical method, rather than by training, and hence, we refer to this approach as Neural Networks for PDEs (NN4PDEs). To solve the discretised multiphase flow equations, a multigrid solver is implemented through a convolutional neural network with a U-Net architecture. Immiscible two-phase flow is modelled by the 3D incompressible Navier-Stokes equations with surface tension and advection of a volume fraction field, which describes the interface between the fluids. A new compressive algebraic volume-of-fluids method is introduced, based on a residual formulation using Petrov-Galerkin for accuracy and designed with NN4PDEs in mind. High-order finite-element based schemes are chosen to model a collapsing water column and a rising bubble. Results compare well with experimental data and other numerical results from the literature, demonstrating that, for the first time, finite element discretisations of multiphase flows can be solved using an approach based on (untrained) convolutional neural networks. A benefit of expressing numerical discretisations as neural networks is that the code can run, without modification, on CPUs, GPUs or the latest accelerators designed especially to run AI codes.
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model

Visual question answering (VQA) is a task where an image is given, and a series of questions are asked about the image. To build an efficient VQA algorithm, a large amount of QA data is required which is very expensive. Generating synthetic QA pairs based on templates is a practical way to obtain data. However, VQA models trained on those data do not perform well on complex, human-written questions. To address this issue, we propose a new method called {\it chain of QA for human-written questions} (CoQAH). CoQAH utilizes a sequence of QA interactions between a large language model and a VQA model trained on synthetic data to reason and derive logical answers for human-written questions. We tested the effectiveness of CoQAH on two types of human-written VQA datasets for 3D-rendered and chest X-ray images and found that it achieved state-of-the-art accuracy in both types of data. Notably, CoQAH outperformed general vision-language models, VQA models, and medical foundation models with no finetuning.
Complexity Classification of Product State Problems for Local Hamiltonians

Product states, unentangled tensor products of single qubits, are a ubiquitous ansatz in quantum computation, including for state-of-the-art Hamiltonian approximation algorithms. A natural question is whether we should expect to efficiently solve product state problems on any interesting families of Hamiltonians. We completely classify the complexity of finding minimum-energy product states for Hamiltonians defined by any fixed set of allowed 2-qubit interactions. Our results follow a line of work classifying the complexity of solving Hamiltonian problems and classical constraint satisfaction problems based on the allowed constraints. We prove that estimating the minimum energy of a product state is in P if and only if all allowed interactions are 1-local, and NP-complete otherwise. Equivalently, any family of non-trivial two-body interactions generates Hamiltonians with NP-complete product-state problems. Our hardness constructions only require coupling strengths of constant magnitude. A crucial component of our proofs is a collection of hardness results for a new variant of the Vector Max-Cut problem, which should be of independent interest. Our definition involves sums of distances rather than squared distances and allows linear stretches. A corollary of our classification is a new proof that optimizing product states in the Quantum Max-Cut model (the quantum Heisenberg model) is NP-complete.
Cyberattacks on Adaptive Cruise Control Vehicles: An Analytical Characterization

While automated vehicles (AVs) are expected to revolutionize future transportation systems, emerging AV technologies open a door for malicious actors to compromise intelligent vehicles. As the first generation of AVs, adaptive cruise control (ACC) vehicles are vulnerable to cyberattacks. While recent effort has been made to understanding the impact of attacks on transportation systems, little work has been done to systematically model and characterize the malicious nature of candidate attacks. In this study, we develop a general framework for modeling and synthesizing two types of candidate attacks on ACC vehicles, namely direct attacks on vehicle control commands and false data injection attacks on sensor measurement, with explicit characterization of their adverse effects. Based on linear stability analysis of car-following dynamics, we derive a series of analytical conditions characterizing the malicious nature of potential attacks. This ensures a higher degree of realism in modeling attacks with adverse effects, as opposed to simply considering attacks as constants or random variables. Notably, the conditions derived provide an effective method for strategically synthesizing an array of candidate attacks on ACC vehicles. We conduct extensive simulation to examine the impacts of intelligently designed attacks on microscopic car-following dynamics and macroscopic traffic flow. Numerical results illustrate the mechanism of candidate attacks, offering useful insights into understanding the vulnerability of future transportation systems. The methodology developed allows for further study of the widespread impact of strategically designed attacks on traffic cybersecurity, and is expected to inspire the development of efficient attack detection techniques and advanced vehicle controls.
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking

We introduce Motion2VecSets, a 4D diffusion model for dynamic surface reconstruction from point cloud sequences. While existing state-of-the-art methods have demonstrated success in reconstructing non-rigid objects using neural field representations, conventional feed-forward networks encounter challenges with ambiguous observations from noisy, partial, or sparse point clouds. To address these challenges, we introduce a diffusion model that explicitly learns the shape and motion distribution of non-rigid objects through an iterative denoising process of compressed latent representations. The diffusion-based prior enables more plausible and probabilistic reconstructions when handling ambiguous inputs. We parameterize 4D dynamics with latent vector sets instead of using a global latent. This novel 4D representation allows us to learn local surface shape and deformation patterns, leading to more accurate non-linear motion capture and significantly improving generalizability to unseen motions and identities. For more temporal-coherent object tracking, we synchronously denoise deformation latent sets and exchange information across multiple frames. To avoid the computational overhead, we design an interleaved space and time attention block to alternately aggregate deformation latents along spatial and temporal domains. Extensive comparisons against the state-of-the-art methods demonstrate the superiority of our Motion2VecSets in 4D reconstruction from various imperfect observations, notably achieving a 19% improvement in Intersection over Union (IoU) compared to CaDex for reconstructing unseen individuals from sparse point clouds on the DeformingThings4D-Animals dataset. More detailed information can be found at https://vveicao.github. io/projects/Motion2VecSets/.
Multi-Profile Quadratic Programming (MPQP) for Optimal Gap Selection and Speed Planning of Autonomous Driving

Smooth and safe speed planning is imperative for the successful deployment of autonomous vehicles. This paper presents a mathematical formulation for the optimal speed planning of autonomous driving, which has been validated in high-fidelity simulations and real-road demonstrations with practical constraints. The algorithm explores the inter-traffic gaps in the time and space domain using a breadth-first search. For each gap, quadratic programming finds an optimal speed profile, synchronizing the time and space pair along with dynamic obstacles. Qualitative and quantitative analysis in Carla is reported to discuss the smoothness and robustness of the proposed algorithm. Finally, we present a road demonstration result for urban city driving.
Exposing Hate - Understanding Anti-Immigration Sentiment Spreading on Twitter

Immigration is one of the most salient topics in public debate. Social media heavily influences opinions on immigration, often sparking polarized debates and offline tensions. Studying 220,870 immigration-related tweets in the UK, we assessed the extent of polarization, key content creators and disseminators, and the speed of content dissemination. We identify a high degree of online polarization between pro and anti-immigration communities. We found that the anti-migration community is small but denser and more active than the pro-immigration community with the top 1% of users responsible for over 23% of anti-immigration tweets and 21% of retweets. We also discovered that anti-immigration content spreads also 1.66 times faster than pro-immigration messages and bots have minimal impact on content dissemination. Our findings suggest that identifying and tracking highly active users could curb anti-immigration sentiment, potentially easing social polarization and shaping broader societal attitudes toward migration.
Improving Low-Light Image Recognition Performance Based on Image-adaptive Learnable Module

In recent years, significant progress has been made in image recognition technology based on deep neural networks. However, improving recognition performance under low-light conditions remains a significant challenge. This study addresses the enhancement of recognition model performance in low-light conditions. We propose an image-adaptive learnable module which apply appropriate image processing on input images and a hyperparameter predictor to forecast optimal parameters used in the module. Our proposed approach allows for the enhancement of recognition performance under low-light conditions by easily integrating as a front-end filter without the need to retrain existing recognition models designed for low-light conditions. Through experiments, our proposed method demonstrates its contribution to enhancing image recognition performance under low-light conditions.
Multimodal Urban Areas of Interest Generation via Remote Sensing Imagery and Geographical Prior

Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined polygonal boundaries. The rapid development of urban commerce has led to increasing demands for highly accurate and timely AOI data. However, existing research primarily focuses on coarse-grained functional zones for urban planning or regional economic analysis, and often neglects the expiration of AOI in the real world. They fail to fulfill the precision demands of Mobile Internet Online-to-Offline (O2O) businesses. These businesses require accuracy down to a specific community, school, or hospital. In this paper, we propose a comprehensive end-to-end multimodal deep learning framework designed for simultaneously detecting accurate AOI boundaries and validating the reliability of AOI by leveraging remote sensing imagery coupled with geographical prior, titled AOITR. Unlike conventional AOI generation methods, such as the Road-cut method that segments road networks at various levels, our approach diverges from semantic segmentation algorithms that depend on pixel-level classification. Instead, our AOITR begins by selecting a point-of-interest (POI) of specific category, and uses it to retrieve corresponding remote sensing imagery and geographical prior such as entrance POIs and road nodes. This information helps to build a multimodal detection model based on transformer encoder-decoder architecture to regress the AOI polygon. Additionally, we utilize the dynamic features from human mobility, nearby POIs, and logistics addresses for AOI reliability evaluation via a cascaded network module. The experimental results reveal that our algorithm achieves a significant improvement on Intersection over Union (IoU) metric, surpassing previous methods by a large margin.
Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention

Recently, Vision Transformer has achieved great success in recovering missing details in low-resolution sequences, i.e., the video super-resolution (VSR) task. Despite its superiority in VSR accuracy, the heavy computational burden as well as the large memory footprint hinder the deployment of Transformer-based VSR models on constrained devices. In this paper, we address the above issue by proposing a novel feature-level masked processing framework: VSR with Masked Intra and inter frame Attention (MIA-VSR). The core of MIA-VSR is leveraging feature-level temporal continuity between adjacent frames to reduce redundant computations and make more rational use of previously enhanced SR features. Concretely, we propose an intra-frame and inter-frame attention block which takes the respective roles of past features and input features into consideration and only exploits previously enhanced features to provide supplementary information. In addition, an adaptive block-wise mask prediction module is developed to skip unimportant computations according to feature similarity between adjacent frames. We conduct detailed ablation studies to validate our contributions and compare the proposed method with recent state-of-the-art VSR approaches. The experimental results demonstrate that MIA-VSR improves the memory and computation efficiency over state-of-the-art methods, without trading off PSNR accuracy. The code is available at https://github.com/LabShuHangGU/MIA-VSR.