Team I2R-VI-FF Technical Report on EPIC-KITCHENS VISOR Hand Object Segmentation Challenge 2023

In this report, we present our approach to the EPIC-KITCHENS VISOR Hand Object Segmentation Challenge, which focuses on the estimation of the relation between the hands and the objects given a single frame as input. The EPIC-KITCHENS VISOR dataset provides pixel-wise annotations and serves as a benchmark for hand and active object segmentation in egocentric video. Our approach combines the baseline method, i.e., Point-based Rendering (PointRend) and the Segment Anything Model (SAM), aiming to enhance the accuracy of hand and object segmentation outcomes, while also minimizing instances of missed detection. We leverage accurate hand segmentation maps obtained from the baseline method to extract more precise hand and in-contact object segments. We utilize the class-agnostic segmentation provided by SAM and apply specific hand-crafted constraints to enhance the results. In cases where the baseline model misses the detection of hands or objects, we re-train an object detector on the training set to enhance the detection accuracy. The detected hand and in-contact object bounding boxes are then used as prompts to extract their respective segments from the output of SAM. By effectively combining the strengths of existing methods and applying our refinements, our submission achieved the 1st place in terms of evaluation criteria in the VISOR HOS Challenge

FurinDB: A Database of 20-Residue Furin Cleavage Site Motifs, Substrates and Their Associated Drugs

FurinDB (freely available online at http://www.nuolan.net/substrates.html) is a database of furin substrates. This database includes experimentally verified furin cleavage sites, substrates, species, experimental methods, original publications of experiments and associated drugs targeting furin substrates. The current database release contains 126 furin cleavage sites from three species: mammals, bacteria and viruses. A main feature of this database is that all furin cleavage sites are recorded as a 20-residue motif, including one core region (eight amino acids, P6–P2′) and two flanking solvent accessible regions (eight amino acids, P7–P14, and four amino acids, P3′–P6′), that represent our current understanding of the molecular biology of furin cleavage. This database is important for understanding the molecular evolution and relationships between sequence motifs, 3D structures, cellular functions and physical properties required by furin for cleavage, and for elucidating the molecular mechanisms and the progression of furin cleavage associated human diseases, including pathogenic infections, neurological disorders, tumorigenesis, tumor invasion, angiogenesis, and metastasis. FurinDB database will be a solid addition to the publicly available infrastructure for scientists in the field of molecular biology

Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

Potential Logographic Dyslexics Identified via Self-Reporting during a Questionnaire Survey in Taiwan

According to the patterns of difficulties of the dyslexics that have been reported in Western societies, a questionnaire in traditional Chinese was developed to carry out initial screening among Taiwanese. The questionnaire includes 30 items with four-point scales and 7 open-ended questions. Of the 2133 copies distributed, a total of 1599 questionnaires were collected which gives a 75.0% response rate and 1442 were completed. The mean of 30-item scores collected from 1442 participants is 87.99 ± 11.9. Among these participants, 9 self-reported potential logographic dyslexics have been identified. The individual scores of 30 items of the nine subjects were at least 1 SD to 4.5 SD lower than that of their counterparts. There are two potential logographic dyslexics families show genetic influence. Since there is no standard test for dyslexics, we developed a 30-item questionnaire that can be completed in 15-20 minutes on average. The questionnaire may serve as a low cost, initial screening tool and allows the potential probands to self-report while the formal diagnosis is not available

A LITERATURE ANALYSIS ON 14 CASES OF ALLERGIC SHOCK CAUSED BY SAFFLOWER INJECTION

The objective of this paper was to investigate the characteristics and general rules of domestic safflower injection causing allergic shock, to retrieve the medical journals published domestically, and to make statistical analysis on the cases of allergic shock caused by safflower injection. Results showed that the incidence of allergic shock caused by safflower injection in patients above 40 years old was high: females were more than males. It occurred within 30min after medication, and no patient died after emergency treatment. The study concluded that allergic shock caused by safflower injection is related to many factors, and clinical care personnel should pay more attention to it

Rotation of hydrogen molecules during the dissociative adsorption on the Mg(0001) surface: A first-principles study

Using first-principles calculations, we systematically study the potential energy surfaces and dissociation processes of the hydrogen molecule on the Mg(0001) surface. It is found that during the dissociative adsorption process with the minimum energy barrier, the hydrogen molecule firstly orients perpendicular, and then rotates to be parallel to the surface. It is also found that the orientation of the hydrogen molecule at the transition state is neither perpendicular nor parallel to the surface. Most importantly, we find that the rotation causes a reduction of the calculated dissociation energy barrier for the hydrogen molecule. The underlying electronic reasons for the rotation of the hydrogen molecule is also discussed in our paper.Comment: 14 pages, 4 figure

Sinusoidal Frequency Modulation Fourier-Bessel Series for Multicomponent SFM Signal Estimation and Separation

Multicomponent sinusoidal frequency modulated (SFM) signals are widely used in radar, acoustics, and biomedicine. The instantaneous frequency (IF) characterizes important physical parameters of the real applications. In this paper, a sinusoidal frequency modulation Fourier-Bessel (SFMFB) series is defined for IF estimation. It provides the signal decomposition on the Bessel function basis with a finer resolution, which proposes an extension of the performance and the applicability of the classic Fourier-Bessel transform (FBT). Based on the property analysis of the SFMFB series, an algorithm of IF estimation and signal separation is introduced. Unlike the existing estimation methods which apply sliding windows to make an instantaneous approximation, the proposed method uses the global data, which provides a longer period gain, therefore achieving a better estimation performance. Moreover, considering that most estimation methods are invalid in multicomponent separation, the individual signals are well separated by the proposed algorithm, which facilitates the further monocomponent analysis. A performance comparison between the proposed method, the FBT, and another recently proposed sinusoidal frequency modulation Fourier transform (SFMFT) is also provided. Simulation results indicate that the proposed method outperforms the existing methods in estimation precision and computation load, and it is free of interference which exists in SFMFT

A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023

In this technical report, we present our findings from a study conducted on the EPIC-KITCHENS-100 Unsupervised Domain Adaptation task for Action Recognition. Our research focuses on the innovative application of a differentiable logic loss in the training to leverage the co-occurrence relations between verb and noun, as well as the pre-trained Large Language Models (LLMs) to generate the logic rules for the adaptation to unseen action labels. Specifically, the model's predictions are treated as the truth assignment of a co-occurrence logic formula to compute the logic loss, which measures the consistency between the predictions and the logic constraints. By using the verb-noun co-occurrence matrix generated from the dataset, we observe a moderate improvement in model performance compared to our baseline framework. To further enhance the model's adaptability to novel action labels, we experiment with rules generated using GPT-3.5, which leads to a slight decrease in performance. These findings shed light on the potential and challenges of incorporating differentiable logic and LLMs for knowledge extraction in unsupervised domain adaptation for action recognition. Our final submission (entitled `NS-LLM') achieved the first place in terms of top-1 action recognition accuracy.Comment: Technical report submitted to CVPR 2023 EPIC-Kitchens challenge

GEOCHEMICAL AND CLAY-MINERAL STUDY OF HEALING MUD FROM WUDALIANCHI, NE CHINA

Over the centuries, people have used healing mud (peloids) to draw toxins out of the body, boost the immune system, cure psoriasis, acne, depression, and hair loss. The beauty industry has used mud-clay masks, body wraps, soaps, and baths. The useful properties of mud were established empirically. The most popular healing-mud spars are known in the Dead Sea in Israel, Baden-Baden in Germany, Calistoga in California, Budapest in Hungary, Akhtala and Kumisi in Georgia, Paratunka in Kamchatka, Wudalianchi in China.Over the centuries, people have used healing mud (peloids) to draw toxins out of the body, boost the immune system, cure psoriasis, acne, depression, and hair loss. The beauty industry has used mud-clay masks, body wraps, soaps, and baths. The useful properties of mud were established empirically. The most popular healing-mud spars are known in the Dead Sea in Israel, Baden-Baden in Germany, Calistoga in California, Budapest in Hungary, Akhtala and Kumisi in Georgia, Paratunka in Kamchatka, Wudalianchi in China