Miracle-dz/UniFAR-FTUs huggingface dataset:
A Refining Underlying Information Framework for Speech Enhancement

—The advent of deep neural networks (DNN) has significantly improved the performance of monaural speech enhancement (SE). Most of them attempt to implicitly capture the structural features of speech through distribution approximation. However, existing methods are susceptible to the issues of degraded speech and residual noise. This letter is grounded in the Information Bottleneck as an anchor to rethink the SE. By defining the incremental convergence of mutual information between speech characteristics, we elucidate that the acoustic characteristic of speech is crucial in alleviating the above issues, for its explicit introduction contributes to further approximating the optimal information-theoretic upper bound of the optimization. Referring to the chain rule of entropy, we also propose a framework to reconstruct the information composition of the optimization objective, aiming to integrate and refine this underlying characteristic without loss of generality. The visualization reflects consistency with analysis using information theory. Experimental results show that with only 1.18 M additional parameters, the refined CRN has yielded substantial progress over a number of advanced methods. The source code is available at https://github.com/caoruitju/RUI SE.
Independence in the Home: A Wearable Interface for a Person with Quadriplegia to Teleoperate a Mobile Manipulator

Teleoperation of mobile manipulators within a home environment can significantly enhance the independence of individuals with severe motor impairments, allowing them to regain the ability to perform self-care and household tasks. There is a critical need for novel teleoperation interfaces to offer effective alternatives for individuals with impairments who may encounter challenges in using existing interfaces due to physical limitations. In this work, we iterate on one such interface, HAT (Head-Worn Assistive Teleoperation), an inertial-based wearable integrated into any head-worn garment. We evaluate HAT through a 7-day in-home study with Henry Evans, a non-speaking individual with quadriplegia who has participated extensively in assistive robotics studies. We additionally evaluate HAT with a proposed shared control method for mobile manipulators termed Driver Assistance and demonstrate how the interface generalizes to other physical devices and contexts. Our results show that HAT is a strong teleoperation interface across key metrics including efficiency, errors, learning curve, and workload. Code and videos are located on our project website.
Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance

Floods cause large losses to property, life, and livelihoods across the world every year, hindering sustainable development. Safety nets to help absorb financial shocks in disasters, such as insurance, are often unavailable in regions of the world most vulnerable to floods, like Bangladesh. Index-based insurance has emerged as an affordable solution, which considers weather data or information from satellites to create a"flood index"that should correlate with the damage insured. However, existing flood event databases are often incomplete, and satellite sensors are not reliable under extreme weather conditions (e.g., because of clouds), which limits the spatial and temporal resolution of current approaches for index-based insurance. In this work, we explore a novel approach for supporting satellite-based flood index insurance by extracting high-resolution spatio-temporal information from news media. First, we publish a dataset consisting of 40,000 news articles covering flood events in Bangladesh by 10 prominent news sources, and inundated area estimates for each division in Bangladesh collected from a satellite radar sensor. Second, we show that keyword-based models are not adequate for this novel application, while context-based classifiers cover complex and implicit flood related patterns. Third, we show that time series extracted from news media have substantial correlation Spearman's rho$=0.70 with satellite estimates of inundated area. Our work demonstrates that news media is a promising source for improving the temporal resolution and expanding the spatial coverage of the available flood damage data.
Pano-NeRF: Synthesizing High Dynamic Range Novel Views with Geometry from Sparse Low Dynamic Range Panoramic Images

Panoramic imaging research on geometry recovery and High Dynamic Range (HDR) reconstruction becomes a trend with the development of Extended Reality (XR). Neural Radiance Fields (NeRF) provide a promising scene representation for both tasks without requiring extensive prior data. How- ever, in the case of inputting sparse Low Dynamic Range (LDR) panoramic images, NeRF often degrades with under-constrained geometry and is unable to reconstruct HDR radiance from LDR inputs. We observe that the radiance from each pixel in panoramic images can be modeled as both a signal to convey scene lighting information and a light source to illuminate other pixels. Hence, we propose the irradiance fields from sparse LDR panoramic images, which increases the observation counts for faithful geometry recovery and leverages the irradiance-radiance attenuation for HDR reconstruction. Extensive experiments demonstrate that the irradiance fields outperform state-of-the-art methods on both geometry recovery and HDR reconstruction and validate their effectiveness. Furthermore, we show a promising byproduct of spatially-varying lighting estimation. The code is available at https://github.com/Lu-Zhan/Pano-NeRF.
Surveying the ice condensation period at southern polar Mars using a CNN

Before the seasonal polar ice cap starts to expand towards lower latitudes on Mars, small frost patches may condensate out during the cold night and they may remain on the surface even during the day in shady areas. If ice in these areas can persist before the arrival of the contiguous ice cap, they may remain after the recession of it too, until the irradiation increases and the ice is met with direct sunlight. In case these small patches form periodically at the same location, slow chemical changes might occur as well. To see the spatial and temporal occurrence of such ice patches, large number of optical images should be searched for and checked. The aim of this study is to survey the ice condensation period on the surface with an automatized method using a Convolutional Neural Network (CNN) applied to High-Resolution Imaging Science Experiment (HiRISE) imagery from the Mars Reconnaissance Orbiter mission. The CNN trained to recognise small ice patches is automatizing the search, making it feasible to analyse large datasets. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the HiRISE camera. Out of these, 37 images were identified with smaller ice patches, which were used to train the CNN. This approach is applied now to find further images with potential water ice patches in the latitude band between -40{\deg} and -60{\deg}, but contrarily to the training dataset recorded between 140-200{\deg} solar longitude, the images were taken from the condensation period between Ls = 0{\deg} to 90{\deg}. The model was ran on 171 new HiRISE images randomly picked from the given period between -40{\deg} and -60{\deg} latitude band, creating 73155 small image chunks. The model classified 2 images that show small, probably recently condensed frost patches and 327 chunks were predicted to show ice with more than 60% probability.
Towards Robust Multimodal Prompting With Missing Modalities

Recently, multimodal prompting, which introduces learnable missing-aware prompts for all missing modality cases, has exhibited impressive performance. However, it encounters two critical issues: 1) The number of prompts grows exponentially as the number of modalities increases; and 2) It lacks robustness in scenarios with different missing modality settings between training and inference. In this paper, we propose a simple yet effective prompt design to address these challenges. Instead of using missing-aware prompts, we utilize prompts as modality-specific tokens, enabling them to capture the unique characteristics of each modality. Furthermore, our prompt design leverages orthogonality between prompts as a key element to learn distinct information across different modalities and promote diversity in the learned representations. Extensive experiments demonstrate that our prompt design enhances both performance and robustness while reducing the number of prompts.
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection

Recent unsupervised anomaly detection methods often rely on feature extractors pretrained with auxiliary datasets or on well-crafted anomaly-simulated samples. However, this might limit their adaptability to an increasing set of anomaly detection tasks due to the priors in the selection of auxiliary datasets or the strategy of anomaly simulation. To tackle this challenge, we first introduce a prior-less anomaly generation paradigm and subsequently develop an innovative unsupervised anomaly detection framework named GRAD, grounded in this paradigm. GRAD comprises three essential components: (1) a diffusion model (PatchDiff) to generate contrastive patterns by preserving the local structures while disregarding the global structures present in normal images, (2) a self-supervised reweighting mechanism to handle the challenge of long-tailed and unlabeled contrastive patterns generated by PatchDiff, and (3) a lightweight patch-level detector to efficiently distinguish the normal patterns and reweighted contrastive patterns. The generation results of PatchDiff effectively expose various types of anomaly patterns, e.g. structural and logical anomaly patterns. In addition, extensive experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation and demonstrate that GRAD achieves competitive anomaly detection accuracy and superior inference speed.
Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Polyp Segmentation

Automatic polyp segmentation plays a crucial role in the early diagnosis and treatment of colorectal cancer (CRC). However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting polyps poses challenges due to variations in shape, size, and location. To address these issues, we propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised polyp Segmentation (DEC-Seg) from colonoscopy images. First, we propose a Cross-level Feature Aggregation (CFA) module that integrates cross-level adjacent layers to enhance the feature representation ability across different resolutions. To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales. This constraint helps handle variations in polyp sizes and improves the robustness of the model. Additionally, we design a scale-aware perturbation consistency scheme to enhance the robustness of the mean teacher model. Furthermore, we propose a cross-generative consistency scheme, in which the original and perturbed images can be reconstructed using cross-segmentation maps. This consistency constraint allows us to mine effective feature representations and boost the segmentation performance. To produce more accurate segmentation maps, we propose a Dual-scale Complementary Fusion (DCF) module that integrates features from two scale-specific decoders operating at different scales. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our DEC-Seg against other state-of-the-art semi-supervised segmentation approaches. The implementation code will be released at https://github.com/taozh2017/DECSeg.
Monte Carlo Study of Agent-Based Blume-Capel Model for Political Depolarization

In this paper, using Monte Carlo simulations we show that the Blume-Capel model gives rise to the social depolarization. This model borrowed from statistical physics uses the continuous Ising spin varying from -1 to 1 passing by zero to express the political stance of an individual going from ultra-left (-1) to ultra-right (+1). The particularity of the Blume-Capel model is the existence of a $D$-term which favors the state of spin zero which is a neutral stance. We consider the political system of the USA where voters affiliate with two political groups: Democrats or Republicans, or are independent. Each group is composed of a large number of interacting members of the same stance. We represent the general political ambiance (or degree of social turmoil) with a temperature $T$ similar to thermal agitation in statistical physics. When three groups interact with each other, their stances can get closer or further from each other, depending on the nature of their inter-group interactions. We study the dynamics of such variations as functions of the value of the $D$-term of each group. We show that the polarization decreases with increasing $D$. We outline the important role of $T$ in these dynamics. These MC results are in excellent agreement with the mean-field treatment of the same model.
On Safety and Liveness Filtering Using Hamilton-Jacobi Reachability Analysis

Hamilton-Jacobi (HJ) reachability-based filtering provides a powerful framework to co-optimize performance and safety (or liveness) for autonomous systems. Under this filtering scheme, a nominal controller is minimally modified to ensure system safety or liveness. However, the resulting controllers can exhibit abrupt switching and bang-bang behavior, which is not suitable for applications of autonomous systems in the real world. This work presents a novel, unifying framework to design safety and liveness filters through reachability analysis. We explicitly characterize the maximal set of control inputs that ensures safety (or liveness) at a given state. Different safety filters can then be constructed using different subsets of this maximal set along with a projection operator to modify the nominal controller. We use the proposed framework to design three safety filters, each balancing performance, computation time, and smoothness differently. The proposed filters can easily handle dynamics uncertainties, disturbances, and bounded control inputs. We highlight their relative strengths and limitations by applying these filters to autonomous navigation and rocket landing scenarios and on a physical robot testbed. We also discuss practical aspects associated with implementing these filters on real-world autonomous systems. Our research advances the understanding and potential application of reachability-based controllers on real-world autonomous systems.
Non-Hermitian gravitational effects on Bose-Einstein condensate

We investigated the impact of Non-Hermitian gravitational potentials on the spatial distribution of Bose-Einstein condensate (BEC) wave functions. Through numerical solutions of the Gross-Pitaevskii (GP) equation, we observed that the imaginary component of Non-Hermitian gravitational potentials affects the spatial periodicity of the BEC wave function phase, resulting in spatial displacement of the wave function's peak. By formulating equations describing the momentum of the BEC wave function with respect to Non-Hermitian gravitational potential parameters and solving and analyzing them under specific conditions, we provided a reasoned interpretation of the numerical results. Our findings contribute to exploring the physical essence of Non-Hermitian gravitational potentials and their impact on BEC, offering theoretical guidance for potential related experiments.
Deep Reinforcement Learning for Quantitative Trading

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting through extensive financial datasets to pinpoint lucrative investment openings. AI-driven models, particularly those employing ML techniques such as deep learning and reinforcement learning, have shown great prowess in predicting market trends and executing trades at a speed and accuracy that far surpass human capabilities. Its capacity to automate critical tasks, such as discerning market conditions and executing trading strategies, has been pivotal. However, persistent challenges exist in current QT methods, especially in effectively handling noisy and high-frequency financial data. Striking a balance between exploration and exploitation poses another challenge for AI-driven trading agents. To surmount these hurdles, our proposed solution, QTNet, introduces an adaptive trading model that autonomously formulates QT strategies through an intelligent trading agent. Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model. To tackle the challenges posed by volatile financial datasets, we conceptualize the QT mechanism within the framework of a Partially Observable Markov Decision Process (POMDP). Moreover, by embedding imitative learning, the model can capitalize on traditional trading tactics, nurturing a balanced synergy between discovery and utilization. For a more realistic simulation, our trading agent undergoes training using minute-frequency data sourced from the live financial market. Experimental findings underscore the model's proficiency in extracting robust market features and its adaptability to diverse market conditions.
From Weak to Strong Coupling: Quasi-BIC Metasurfaces for Mid-infrared Light-Matter Interactions

Resonant metasurfaces present extraordinary subwavelength light trapping capabilities, which have been critical to the development of high-performance biochemical sensors and surface-enhanced spectroscopy techniques. To date, metasurface-enhanced light-matter interactions in the mid-infrared region have been primarily leveraged in the weak coupling regime. Nevertheless, the strong coupling regime, characterized by the hybrid light-matter states - polaritons, has not been fully explored. Specifically, metasurfaces made from low-loss dielectric resonators underpinned by the bound states in the continuum (BIC) are well suited for quantum coherent energy exchange between their high-Q open cavities and molecules' resonant transitions. This paper delves into the light-matter interactions in metasurface cavities generated by quasi-BIC resonances at mid-infrared frequencies. We thoroughly investigate the material and cavity parameters that define the boundaries between weak and strong coupling. Our findings underscore the transformative potential of dielectric metasurfaces to harness vibrational strong coupling for applications such as cavity polaritonic chemistry, modulation of chemical reactivity with light, and highly sensitive molecular spectroscopy.
Efficient cohesive subgraph search in big attributed graph data

Models for finding cohesive subgraph previously have focused on graphs having no attributes. However, these graphs provide only partial representation of real graph data and miss important attributes describing a variety of features for each vertex in the graphs. As such real graph data are better modelled as attributed graph. Investigations for cohesive subgraph search in attributed graphs are still at preliminary stage. Searching cohesive subgraphs in an attributed graph can discover interesting communities and find useful information for answering keyword queries. In this thesis, several cohesive subgraph models considering spatial and textual attributes are studied, which well fit into various real applications. Firstly, the problem of k-truss search has been well defined and investigated to find the highly correlated user groups in social networks. But there is no previous study to consider the constraint of users’ spatial information in k-truss search, denoted as colocated community search in this thesis. The co-located community can serve many real applications. To search the maximum co-located communities efficiently, we first develop an efficient exact algorithm with several pruning techniques. We further develop an approximation algorithm with adjustable accuracy guarantees and explore more effective pruning rules, which can reduce the computational cost significantly. To accelerate the real-time efficiency, we also devise a novel quadtree based index to support the efficient retrieval of users in a region and optimise the search regions with regards to the given query region. We verify the performance of our proposed algorithms and index using five real datasets. Secondly, we propose a novel parameter-free community model called contextual community, for searching a community in an attributed graph. The proposed model
A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement Learning

Off-dynamics Reinforcement Learning (ODRL) seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics. In this context, traditional RL agents depend excessively on the dynamics of the source environment, resulting in the discovery of policies that excel in this environment but fail to provide reasonable performance in the target one. In the few-shot framework, a limited number of transitions from the target environment are introduced to facilitate a more effective transfer. Addressing this challenge, we propose an innovative approach inspired by recent advancements in Imitation Learning and conservative RL algorithms. The proposed method introduces a penalty to regulate the trajectories generated by the source-trained policy. We evaluate our method across various environments representing diverse off-dynamics conditions, where access to the target environment is extremely limited. These experiments include high-dimensional systems relevant to real-world applications. Across most tested scenarios, our proposed method demonstrates performance improvements compared to existing baselines.
Large-scale Long-tailed Disease Diagnosis on Radiology Images

In this study, we aim to investigate the problem of large-scale, large-vocabulary disease classification for radiologic images, which can be formulated as a multi-modal, multi-anatomy, multi-label, long-tailed classification. Our main contributions are three folds: (i), on dataset construction, we build up an academically accessible, large-scale diagnostic dataset that encompasses 5568 disorders linked with 930 unique ICD-10-CM codes, containing 39,026 cases (192,675 scans). (ii), on model design, we present a novel architecture that enables to process arbitrary number of input scans, from various imaging modalities, which is trained with knowledge enhancement to leverage the rich domain knowledge; (iii), on evaluation, we initialize a new benchmark for multi-modal multi-anatomy long-tailed diagnosis. Our method shows superior results on it. Additionally, our final model serves as a pre-trained model, and can be finetuned to benefit diagnosis on various external datasets.