LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce

Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback. This study identified computational features, formulated design requirements, and developed LiveRetro, an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels. Through case studies and expert interviews, the system provides deep insights into the relationship between live performance and streaming statistics, enabling efficient strategic analysis from multiple perspectives.Comment: Accepted by IEEE VIS 202

StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback

The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedback for exploring the output space of LLMs to enhance code generation quality. However, the lengthy code generated by LLMs in response to complex human requirements makes RL exploration a challenge. Also, since the unit tests may not cover the complicated code, optimizing LLMs by using these unexecuted code snippets is ineffective. To tackle these challenges, we introduce StepCoder, a novel RL framework for code generation, consisting of two main components: CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks, while FGO only optimizes the model by masking the unexecuted code segments to provide Fine-Grained Optimization. In addition, we furthermore construct the APPS+ dataset for RL training, which is manually verified to ensure the correctness of unit tests. Experimental results show that our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks. Our dataset APPS+ and StepCoder are available online.Comment: 13 pages, 5 figure

Energy evolution mechanism during rockburst development in structures of surrounding rocks of deep rockburst-prone roadways in coal mines

Influenced by the deep high-stress environment, geological structures, and mining disturbance in coal mines, the frequency of rockburst disasters in roadways is increasing. This research analyzed energy evolution characteristics during rockburst development in the elastic bearing zone and energy conversion in the plastic failure zone. The critical energy criteria for structural instability of roadway surrounding rocks were deduced. Numerical software was also applied to simulate the energy evolution during rockburst development in surrounding rocks of rockburst-prone roadways under conditions of different mining depths and coal pillar widths. The occurrence mechanism of rockburst deep in coal mines was analyzed from the perspective of energy in structures of deep roadway surrounding rock in coal mines. The research results show that the critical energy criteria are closely related to the elastic strain energy stored in deep roadway surrounding rocks and the energy absorbed by support systems. The impact energy in roadways is directly proportional to the square of the stress concentration factor k. Moreover, as the mining depth increases, the location of the peak point of maximum energy density gradually shifts to coal ahead of the working face. The larger the mining depth is, the more significantly the energy density is influenced by advanced abutment pressure of the working face and the wider the affected area is. With the increment of the coal pillar width, the distance from the peak point of energy density to the roadway boundary enlarges abruptly at first and then slowly, and the critical coal pillar width for gentle change in the distance is 30 m. Changes in the peak elastic energy density in coal pillars with the coal pillar width can be divided into four stages: the slow increase stage, abrupt increase stage, abrupt decrease stage, and slow decrease stage. The elastic energy density is distributed asymmetrically in deep roadway surrounding rocks in coal mines. Under the action of structures of roadway surrounding rocks, energy evolution in these structures differs greatly during rockburst development under conditions of different coal pillar widths. This research provides an important theoretical basis for the support of rockburst-prone roadways during deep coal mining

Secrets of RLHF in Large Language Models Part II: Reward Modeling

Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization

PI3Ks Maintain the Structural Integrity of T-Tubules in Cardiac Myocytes

Phosphoinositide 3-kinases (PI3Ks) regulate numerous physiological processes including some aspects of cardiac function. Although regulation of cardiac contraction by individual PI3K isoforms has been studied, little is known about the cardiac consequences of downregulating multiple PI3Ks concurrently.Genetic ablation of both p110α and p110β in cardiac myocytes throughout development or in adult mice caused heart failure and death. Ventricular myocytes from double knockout animals showed transverse tubule (T-tubule) loss and disorganization, misalignment of L-type Ca(2+) channels in the T-tubules with ryanodine receptors in the sarcoplasmic reticulum, and reduced Ca(2+) transients and contractility. Junctophilin-2, which is thought to tether T-tubules to the sarcoplasmic reticulum, was mislocalized in the double PI3K-null myocytes without a change in expression level.PI3K p110α and p110β are required to maintain the organized network of T-tubules that is vital for efficient Ca(2+)-induced Ca(2+) release and ventricular contraction. PI3Ks maintain T-tubule organization by regulating junctophilin-2 localization. These results could have important medical implications because several PI3K inhibitors that target both isoforms are being used to treat cancer patients in clinical trials

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Improved Local Ternary Patterns for Automatic Target Recognition in Infrared Imagery

This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP. Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency. Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods

Contour Extraction And Tracking Of Moving Vehicles For Traffic Monitoring

Abstract-- A robust algorithm is proposed in this paper for contour extraction and tracking of moving vehicles. A snake model is modified, in which directional information is utilized to guide the snaxels ’ behavior. Then an adaptive shape restriction is applied to govern the scope of the snake’s motion, and Kalman filter is employed to estimate spatio-temporal relationship between successive frames. Moreover, several refinements are suggested to compensate for the snake’s vulnerability to fake edges. All these steps contribute to a satisfying overall performance in contour extraction and tracking of moving vehicles for real traffic monitoring. Index Terms—Contour extraction, Traffic monitoring, Vehicle trackin