Miracle-dz/UniFAR-FTUs huggingface dataset:
In situ measurement of low-frequency sea-ice dielectric properties and implications for tracking seasonal evolution of microstructure

10 Abstract. The seasonal evolution of sea-ice microstructure controls key ice properties, including those governing ocean- 15 atmosphere heat and gas exchange, remote-sensing signatures and the role of the ice cover as a habitat. Non-destructive in situ monitoring of sea-ice microstructure is of value for sea-ice research and operations, but remains elusive to date. We examine the potential for the electric properties of sea ice, which is highly sensitive to the brine distribution within the ice, to serve as a proxy for microstructure and, hence, other ice transport properties. Throughout spring of 2013 and 2014, we measured complex dielectric permittivity in the range of 10 Hz to 95 kHz in landfast ice off the coast of Barrow, Alaska. 20 Temperature and salinity measurements and ice samples provide data to characterize ice microstructure in relation to these permittivity measurements. The results reveal a significant correlation between complex dielectric permittivity, brine volume fraction, and microstructural characteristics including pore volume and connectivity, derived from x-ray microtomography of core samples. The influence of temperature and salinity variations, as well as the relationships between ice properties, microstructural characteristics, and dielectric behavior emerge from multivariate analysis of the combined data set. Our 25
Mining High-quality Samples from Raw Data and Majority Voting Method for Multimodal Emotion Recognition

Automatic emotion recognition has a wide range of applications in human-computer interaction. In this paper, we present our work in the Multimodel Emotion Recognition (MER) 2023, which contains three sub-challenges: MER-MULTI, MER-NOISE, and MER-SEMI. We first use a vanilla semi-supervised method to mine high quality samples from the MER-SEMI unlabeled dataset to expand the training set. Specifically, we ensemble three models trained with the official training set by a majority voting method, which is used to select samples with high prediction consistency. The selected samples together with the original training set are further augmented by adding noise. Then, the features of different modalities of expanded dataset are extracted from several pre-trained or fine-tuned models, and they are subsequently used to create different feature combinations to capture more effective emotion representations. Besides, we employ early fusion of different modal features and late fusion of different recognition models to obtain the final prediction. Experimental results show that our proposed method improves the performance over the official baselines by 30.4%, 55.3% and 1.57% for the three sub-challenges and ranks 4, 3, and 5, respectively. The present work sheds light on high-quality data mining and model ensemble by majority voting for multimodal emotion recognition.
AffectFAL: Federated Active Affective Computing with Non-IID Data

Federated affective computing, which deploys traditional affective computing in a distributed framework, achieves a trade-off between privacy and utility, and offers a wide variety of applications in business and society. However, the expensive annotation cost of obtaining reliable emotion labels at the local client remains a barrier to the effective use of local emotional data. Therefore, we propose a federated active affective paradigm to improve the performance of federated affective computing with a limited annotation budget on the client. A major challenge in federated active learning is the inconsistency between the active sampling goals of global and local models, particularly in scenarios with Non-IID data across clients, which exacerbates the problem. To address the above challenge, we propose AffectFAL, a federated active affective computing framework. It incorporates a Preference-aware Group Aggregation module, which obtains global models representing the different emotional preferences among clients. We also devise a tailored De-biased Federated Active Sampling strategy with an improved vote entropy, facilitating class balancing of labeled samples and alleviating the problem of sampling goals inconsistency between the global and local models. We evaluate AffectFAL on diverse benchmarks (image, video and physiological signal) and experimental settings for affective computing. Thorough comparisons with other active sampling strategies demonstrate our method's advantages in affective computing for Non-IID federated learning.
ML R-CNN: A Location-Aware Network for Tooth Instance Segmentation

Dentists work with hundreds of intraoral photos a day to diagnose patients' dental health, which is very time-consuming. Recently, deep learning methods have shown its potential in automated tooth localization and segmentation, thus reducing the workload of dentists and improving the diagnostic efficiency. However, teeth in different locations are similar in shape and size, which remains a serious challenge for obtaining accurate location and segmentation results. To cope with this issue, we propose a location-aware neural network, Mask Location R-CNN(ML R-CNN), which adopts a location head branch to combine location information with dental classification confidence. Besides, an improved backbone is introduced for enhancing the feature extraction. An intraoral photo dataset was constructed and annotated by senior dentists, and the experimental results show that the proposed method considerably improves performance of tooth instance segmentation comparing with other competitive approaches.
Generalizable Label Distribution Learning

Label Distributed Learning (LDL) is an emerging machine learning paradigm that has received extensive research in recent years. Owing to the excellent capability in dealing with label ambiguity, LDL has been widely adopted in many real-world scenarios. Though remarkable progress has been achieved in various tasks, one limitation with existing LDL methods is that they are all based on the i.i. d. assumption that training and test data are identically and independently distributed. As a result, they suffer obvious performance degradation and are no longer applicable when tested in out-of-distribution scenarios, which severely limits the application of LDL in many tasks. In this paper, we identify and investigate the Generalizable Label Distribution Learning (GLDL) problem. To handle such a challenging problem, we delve into the characteristics of GLDL and find that feature-label correlation and label-label correlation are two essential subjects in GLDL. Inspired by this finding, we propose a simple yet effective model-agnostic framework named Domain-Invariant Correlation lEarning (DICE). DICE mines and utilizes the correlation between feature and label that are invariant across different domains to learn a generalizable feature-label correlation by introducing a prior alignment strategy. In the meantime, it leverages a label correlation alignment strategy to further retain the consistency of label-label correlation in different domains. Extensive experiments verify the superior performance of DICE. Our work fills the gap in benchmarks and techniques for practical GLDL problems.
TwinStar: A Practical Multi-path Transmission Framework for Ultra-Low Latency Video Delivery

Ultra-low latency video streaming has received explosive growth in the past few years. However, existing methods all focus on single-path transmission, which is ineffective in dealing with really poor network conditions. To tackle their problems, we propose TwinStar, a novel multi-path framework to improve the experience quality of ultra-low latency video. The core idea of TwinStar is to concurrently leverage multiple paths to mitigate the negative impacts of network jitter on a single path. In particular, by carefully designing the video encoding, data allocation and loss recovery, TwinStar is very robust to handle network dynamics and deliver high-quality video services. We have deployed TwinStar in a commercial cloud gaming platform and evaluated it with real-world networks. The extensive experiments demonstrate that TwinStar significantly outperforms the single-path transmission methods, with 91% reduction in stall ratio and 11% improvement in PSNR across all regions.
Community Food Resource Assessment in Central City South, Phoenix: A Study of Community Capacity Building

Many studies have shown that access to healthy food in the United States is unevenly distributed, and convenience and dollar stores are the prevalent grocery options for many low-income minority communities where supermarkets and other fresh-food retailers are not accessible. I formed a partnership with Phoenix Revitalization Corporation, a community development organization working in Phoenix’s Central City South (CCS) neighborhood, to increase the community's capacity to meet its health goals by improving access to healthy food. I used a community-based participatory approach to align my research with the community’s Quality of Life Plan, and to facilitate collaboration between academic and nonacademic partners. Using interviews, Nutrition Environment Measures Surveys (NEMS), and mapping to analyze the community's food resources, I found that physical, economic, and social barriers impede residents’ access to affordable, nutritious food. My community food assessment provides a baseline for tracking progress to increase availability of nutritious food and residents' access to it, as well as a platform for further communication, education, and action in the community. The research was an essential step in improving access to healthy food for CCS residents, because it produced recommendations to equip and empower the community with strategic, community-specific interventions to reduce the disparities in food access experienced by residents and achieve the health goals of the community’s Quality of Life Plan. April 18, 2011 2:30 PM Wrigley Hall 481 Faculty, students, and the general public are invited. Supervisory Committee: Sharon Harlan, Chair Hallie Eakin David Aftandilian
Shape Recovery from Polarization: A Review

Shape recovery of targets using polarization is an academic hot topic in recent years. The combination of polarizer and optical sensor can analyze the intensity, polarization degree, polarization angle, zenith angle and other information in the light field to construct a normal gradient field for the shape recovery of the target object. Compared with the shape recovery of traditional stereo and depth sensor, shape from polarization has the advantages of higher accuracy, richer detail texture, and less restriction by light and spatial geometry. The shape of the object's surface can be recovered by integrating the gradient field composed of normal parameters. However, both mathematical models suffer from the problem of ambiguous normal parameters. The problem of ambiguity in the polarization gradient field has not been perfectly solved, which has restricted the development of shape from polarization from theory to application. This paper analyzes the basic principles of shape recovery from polarization and the reasons for the ambiguity of the normal. The classical polarization normal correction scheme is summarized in this review. In the process of integral reconstruction of polarization gradient field, the advantages and disadvantages of global integrator and local integrator in SFP algorithm are analyzed. Finally the problems that still need to be solved in the process of shape from polarization and the directions worthy of further research are discussed.
MuSe 2023 Challenge: Multimodal Prediction of Mimicked Emotions, Cross-Cultural Humour, and Personalised Recognition of Affects

The 4th Multimodal Sentiment Analysis Challenge (MuSe) focuses on Multimodal Prediction of Mimicked Emotions, Cross-Cultural Humour, and Personalised Recognition of Affects. The workshop takes place in conjunction with ACM Multimedia'23. We provide three datasets as part of the challenge: (i) The Hume-Vidmimic dataset which offers 30+ hours of expressive behaviour data from 557 participants. It involves mimicking and rating emotions: Approval, Disappointment, and Uncertainty. This multimodal resource is valuable for studying human emotional expressions. (ii) The 2023 edition of the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset comprises German football press conference recordings within the training set, while videos of English football press conferences are included in the unseen test set. This unique configuration offers a cross-cultural evaluation environment for humour recognition. (iii) The Ulm-Trier Social Stress Test (Ulm-TSST) dataset contains recordings of subjects under stress. It involves arousal and valence signals, with some test labels provided to aid personalisation. Based on these datasets, we formulate three multimodal affective computing challenges: (1) Mimicked Emotions Sub-Challenge (MuSe-Mimic) for categorical emotion prediction, (2) Cross-Cultural Humour Detection Sub-Challenge (MuSe-Humour) for cross-cultural humour detection, and (3) Personalisation Sub-Challenge (MuSe-Personalisation) for personalised dimensional emotion recognition. In this summary, we outline the challenge's motivation, participation guidelines, conditions, and results.
SCLAV: Supervised Cross-modal Contrastive Learning for Audio-Visual Coding

Audio and vision are important senses for high-level cognition, and their special strong correlation makes audio-visual coding a crucial factor in many multimodal tasks. However, there are two challenges in audio-visual coding. First, the heterogeneity of multimodal data often leads to misalignment of cross-modal features under the same sample, which reduces their representation quality. Second, most self-supervised learning frameworks are constructed based on instance semantics, and the generated pseudo labels introduce additional classification noise. To address these challenges, we propose a Supervised Cross-modal Contrastive Learning Framework for Audio-Visual Coding (SCLAV). Our framework includes an audio-visual coding network composed of an inter-modal attention interaction module and an intra-modal self-integration module, which leverage multimodal complementary and hidden information for better representation. Additionally, we introduce a supervised cross-modal contrastive loss to minimize the distance between audio and vision features of the same instance, and use weak labels of multimodal data to eliminate the feature-oriented classification noise. Extensive experiments on the AVE and XD-Violence datasets demonstrate that SCLAV outperforms the state-of-the-art results, even with limited computational resources.
Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval

In recent years, hashing methods have been popular in the large-scale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, more and more studies focus on hashing-based fine-grained image retrieval. Existing hashing networks usually generate both local and global features through attention guidance on the same deep activation tensor, which limits the diversity of feature representations. To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval. Specifically, an Attention Dispersion Loss (ADL) is designed to force the descriptors to attend to various local regions and capture diverse subtle details. Moreover, we propose a Stepwise Interactive External Attention (SIEA) to mine critical attributes in each descriptor and construct correlations between fine-grained attributes and objects. The attention mechanism is dedicated to learning discrete attributes, which will not cost additional computations in hash codes generation. Finally, the compact binary codes are learned by preserving pairwise similarities. Experimental results demonstrate that AGMH consistently yields the best performance against state-of-the-art methods on fine-grained benchmark datasets.
SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge

Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensive analysis, multimodal comprehension, and accurate prediction. SMP Challenge is an annual research activity that has spurred academic exploration in this area. This paper summarizes the challenging task, data, and research progress. As a critical resource for evaluating and benchmarking predictive models, we have released a large-scale SMPD benchmark encompassing approximately half a million posts authored by around 70K users. The research progress analysis provides an overall analysis of the solutions and trends in recent years. The SMP Challenge website (www.smp-challenge.com) provides the latest information and news.
TE-KWS: Text-Informed Speech Enhancement for Noise-Robust Keyword Spotting

Keyword spotting (KWS) presents a formidable challenge, particularly in high-noise environments. Traditional denoising algorithms that rely solely on speech have difficulty recovering speech that has been severely corrupted by noise. In this investigation, we develop an adaptive text-informed denoising model to bolster reliable keyword identification in the presence of considerable noise degradation. The whole proposed TE-KWS incorporates a tripartite branch structure, where the speech branch (SB) takes noisy speech as input which provides the raw speech information, the alignment branch (AB) accommodates aligned text input which facilitates accurate restoration of the corresponding speech when text with alignment is preserved, and the text branch (TB) handles unaligned text which prompts the model to autonomously learn the alignment between speech and text. To make the proposed denoising model more beneficial for KWS, following the training of the whole model,the alignment branch (AB) is frozen, and the model is fine-tuned by leveraging its speech restoration and forced alignment capabilities. Subsequently, the input for the text branch (TB) is supplanted with designated keywords, and a heavier denoising penalty is applied on the keywords period, thereby explicitly intensifying the speech restoration ability of the model for keywords. Finally, the Combined Adversarial Domain Adaptation (CADA) is implemented to enhance the robustness of KWS with regard to data pre-and post-speech enhancement (SE). Experimental results indicate that our approach not only markedly ameliorates highly corrupted speech, achieving SOTA performance for marginally corrupted speech, but also bolsters the efficacy and generalizability of prevailing mainstream KWS models.
For Peer Review An Efficient and Fair MAC Protocol Robust to Reactive and Worst-Case Interference

Interference constitutes a major challenge to availability for communication networks operating over a shared medium. This article proposes the medium access (MAC) protocol ANTIJAM which achieves a high and fair throughput even in harsh environments. Our protocol mitigates internal interference, requiring no knowledge about the number of participants in the network. It is also robust to intentional and unintentional external interference, e.g., due to coexisting networks or jammers. We model external interference using a powerful reactive adversary who can jam a (1−ε)-portion of the time steps, where 0 < ε ≤ 1 is an arbitrary constant. The adversary uses carrier sensing to make informed decisions on when it is most harmful to disrupt communications. Moreover, we allow the adversary to be adaptive and to have complete knowledge of the entire protocol history. ANTIJAM makes efficient use of the non-jammed time periods and achieves, if ε is constant, a Θ(1)-competitive throughput. In addition, ANTIJAM features a low convergence time and has excellent fairness properties, such that channel access probabilities do not differ among nodes by more than a small constant factor.
FlatGAN: A Holistic Approach for Robust Flat-Coloring in High-Definition with Understanding Line Discontinuity

The process of drawing digital comics and animations is a complex process that involves multiple stages. Flat-coloring, the task of filling segmented regions in a line art image with uniform tone and hue, is a particularly time-consuming and labor-intensive task. We have identified that artists suffer from not only adjusting colors in overflowing regions due to line discontinuity but also finding to replace misaligned pixels near the line due to region-bleeding problems (aliasing issues). To address these issues, we propose a holistic data generation pipeline (FlatGAN-DG) that awares the region of line discontinuity and augments the input sketch image to build robust models for noise. In addition, we propose a real-time post-processing method (FlatGAN-PP) that automatically finds and replaces miscolored pixels to alleviate the region-bleeding problems (aliasing issues). To enhance inference speed, we build FlatGAN, which shares the parameters of a generator to predict the foreground, background, and trimap at once to learn in a multi-task manner. Our experimental results show that our method outperforms other rule-and learning-based methods on three different datasets with different painting styles. To evaluate the segmented regions, we collect datasets with the annotation of split-score, merge-hard-score, and merge-easy-score. We also introduce a new evaluation metric (Region Score) on these datasets, validating the efficacy of our methods through a user study. Code is available at https://github.com/hanish3464/FlatGAN.
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction

Predicting individual aesthetic preferences holds significant practical applications and academic implications for human society. However, existing studies mainly focus on learning and predicting the commonality of facial attractiveness, with little attention given to Personalized Facial Beauty Prediction (PFBP). PFBP aims to develop a machine that can adapt to individual aesthetic preferences with only a few images rated by each user. In this paper, we formulate this task from a meta-learning perspective that each user corresponds to a meta-task. To address such PFBP task, we draw inspiration from the human aesthetic mechanism that visual aesthetics in society follows a Gaussian distribution, which motivates us to disentangle user preferences into a commonality and an individuality part. To this end, we propose a novel MetaFBP framework, in which we devise a universal feature extractor to capture the aesthetic commonality and then optimize to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism. Unlike conventional meta-learning methods that may struggle with slow adaptation or overfitting to tiny support sets, we propose a novel approach that optimizes a high-order predictor for fast adaptation. In order to validate the performance of the proposed method, we build several PFBP benchmarks by using existing facial beauty prediction datasets rated by numerous users. Extensive experiments on these benchmarks demonstrate the effectiveness of the proposed MetaFBP method.