"Reading Between the Heat": Co-Teaching Body Thermal Signatures for Non-intrusive Stress Detection

Stress impacts our physical and mental health as well as our social life. A passive and contactless indoor stress monitoring system can unlock numerous important applications such as workplace productivity assessment, smart homes, and personalized mental health monitoring. While the thermal signatures from a user's body captured by a thermal camera can provide important information about the "fight-flight" response of the sympathetic and parasympathetic nervous system, relying solely on thermal imaging for training a stress prediction model often lead to overfitting and consequently a suboptimal performance. This paper addresses this challenge by introducing ThermaStrain, a novel co-teaching framework that achieves high-stress prediction performance by transferring knowledge from the wearable modality to the contactless thermal modality. During training, ThermaStrain incorporates a wearable electrodermal activity (EDA) sensor to generate stress-indicative representations from thermal videos, emulating stress-indicative representations from a wearable EDA sensor. During testing, only thermal sensing is used, and stress-indicative patterns from thermal data and emulated EDA representations are extracted to improve stress assessment. The study collected a comprehensive dataset with thermal video and EDA data under various stress conditions and distances. ThermaStrain achieves an F1 score of 0.8293 in binary stress classification, outperforming the thermal-only baseline approach by over 9%. Extensive evaluations highlight ThermaStrain's effectiveness in recognizing stress-indicative attributes, its adaptability across distances and stress scenarios, real-time executability on edge platforms, its applicability to multi-individual sensing, ability to function on limited visibility and unfamiliar conditions, and the advantages of its co-teaching approach.Comment: 29 page

A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation

Monocular Re-Localization (MRL) is a critical component in autonomous applications, estimating 6 degree-of-freedom ego poses w.r.t. the scene map based on monocular images. In recent decades, significant progress has been made in the development of MRL techniques. Numerous algorithms have accomplished extraordinary success in terms of localization accuracy and robustness. In MRL, scene maps are represented in various forms, and they determine how MRL methods work and how MRL methods perform. However, to the best of our knowledge, existing surveys do not provide systematic reviews about the relationship between MRL solutions and their used scene map representation. This survey fills the gap by comprehensively reviewing MRL methods from such a perspective, promoting further research. 1) We commence by delving into the problem definition of MRL, exploring current challenges, and comparing ours with existing surveys. 2) Many well-known MRL methods are categorized and reviewed into five classes according to the representation forms of utilized map, i.e., geo-tagged frames, visual landmarks, point clouds, vectorized semantic map, and neural network-based map. 3) To quantitatively and fairly compare MRL methods with various map, we introduce some public datasets and provide the performances of some state-of-the-art MRL methods. The strengths and weakness of MRL methods with different map are analyzed. 4) We finally introduce some topics of interest in this field and give personal opinions. This survey can serve as a valuable referenced materials for MRL, and a continuously updated summary of this survey is publicly available to the community at: https://github.com/jinyummiao/map-in-mono-reloc.Comment: 33 pages, 10 tables, 16 figures, under revie

Remarkable response to PD-1 inhibitor in a patient with extensive-stage small cell lung cancer: a case report and literature review

We report a case of a 59-year-old male diagnosed with extensive-stage small cell lung cancer (SCLC). He received first-line platinum doublet chemotherapy and second-line topotecan-based regimen, but experienced disease progression after each line of therapy. He was then treated with Sintilimab, a PD-1 inhibitor, in combination with nab-paclitaxel in the third-line setting, which resulted in significant tumor shrinkage. Restaging scans showed a partial response per RECIST criteria with 62% reduction in tumor burden. This case highlights the application and efficacy of immune checkpoint inhibitors in extensive-stage SCLC

No-tillage facilitates soil organic carbon sequestration by enhancing arbuscular mycorrhizal fungi-related soil proteins accumulation and aggregation

peer reviewedNo-tillage is known to optimize soil structure and enhance soil organic carbon (SOC) stocks in cropland. However, the exact mechanisms driving the accumulation of SOC are still unclear, especially concerning the regulation of arbuscular mycorrhizal fungi (AMF) communities and diversity in SOC sequestration. Here, this study aims to elucidate the intricate relationship between AMF community, glomalin-related soil proteins (GRSP), and SOC within bulk soil and aggregates across four tillage treatments (i.e. FA, fallow; RT, rotary tillage; DT, deep tillage; NT, no-tillage) based on a 7-year tillage experiment. Results showed that the contents of SOC and GRSP were significantly higher by 1.14–1.46 mg/g and 0.43–0.72 mg/g in the bulk soil under NT relative to RT and DT, respectively. The contribution of GRSP-C to SOC under NT was also higher than RT and DT, especially in > 53 μm particle size. Additionally, NT increased AMF diversity and the abundance of glomerales and diversisporales, all showing a strong positive correlation with GRSP (p 53 μm particle size fraction (R2 = 0.74; p 53 μm) formation and enhance aggregate stability through GRSP levels. Overall, increased AMF diversity and keystone taxa abundance at the order level via no-tillage promoted SOC accumulation through the production of GRSP and the protection of large aggregates. This study highlights that no-tillage is an effective and sustainable soil management strategy for enhancing soil quality in agricultural ecosystems