Unified Transformer Tracker for Object Tracking

As an important area in computer vision, object tracking has formed two separate communities that respectively study Single Object Tracking (SOT) and Multiple Object Tracking (MOT). However, current methods in one tracking scenario are not easily adapted to the other due to the divergent training datasets and tracking objects of both tasks. Although UniTrack \cite{wang2021different} demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on single object tracking. In this work, we present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both SOT and MOT. The correlation between the target and tracking frame features is exploited to localize the target. We demonstrate that both SOT and MOT tasks can be solved within this framework. The model can be simultaneously end-to-end trained by alternatively optimizing the SOT and MOT objectives on the datasets of individual tasks. Extensive experiments are conducted on several benchmarks with a unified model trained on SOT and MOT datasets. Code will be available at https://github.com/Flowerfan/Trackron.Comment: CVPR 202

Detecting Mind Wandering: An Objective Method via Simultaneous Control of Respiration and Fingertip Pressure

Mind wandering happens when one train of thought, related to a current undertaking, is interrupted by unrelated thoughts. The detection and evaluation of mind wandering can greatly help in understanding the attention control mechanism during certain focal tasks. Subjective assessments such as random thought-probe and spontaneous self-report are the ways previous research has assessed mind wandering. Here we propose a task in which participants are asked to simultaneously control respiration and fingertip pressure. They are instructed to click a force sensor at the exact moment of inhalation and exhalation of their respiration. The temporal synchronization between the respiratory signals and the fingertip force pulses offers an objective index to detect mind wandering. Twelve participants engaged in the proposed task in which self-reports of mind wandering are compared with the proposed objective index. The results show that the participants reported significantly more mind-wandering episodes during the trials with a larger temporal synchronization than they did during those trials with a smaller temporal synchronization. The findings suggest that the temporal synchronization might be used as an objective marker of mind wandering in attention training and exploration of the attention control mechanism

Enhancing energy efficiency with a dynamic trust measurement scheme in power distribution network

The application of Intelligent Internet of Things (IIoT) in constructing distribution station areas strongly supports platform transformation, upgrade, and intelligent integration. The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer, with the former using intelligent fusion terminals for real-time data collection and processing. However, the influx of multiple low-voltage in the smart grid raises higher demands for the performance, energy efficiency, and response speed of the substation fusion terminals. Simultaneously, it brings significant security risks to the entire distribution substation, posing a major challenge to the smart grid. In response to these challenges, a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues. The scheme begins by establishing a hierarchical trust measurement model, elucidating the trust relationships among smart IoT terminals. It then incorporates multidimensional measurement factors, encompassing static environmental factors, dynamic behaviors, and energy states. This comprehensive approach reduces the impact of subjective factors on trust measurements. Additionally, the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units, ensuring the prompt identification and elimination of any malicious terminals. This, in turn, enhances the security and reliability of the smart grid environment. The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments. Notably, the scheme outperforms established trust metric models in terms of energy efficiency, showcasing its significant contribution to the field

Nano-Selenium Alleviates Cadmium-Induced Acute Hepatic Toxicity by Decreasing Oxidative Stress and Activating the Nrf2 Pathway in Male Kunming Mice

Cadmium (Cd) is known as a highly toxic heavy metal and has been reported to induce hepatotoxicity in animals. Nano-selenium (NSe) is an antioxidant that plays many biological roles such as oxidative stress alleviation. The purpose of this study is to explore the mechanism of action by which NSe inhibits Cd-induced hepatic toxicity and oxidative stress. Sixty eight-week-old male Kunming mice were randomly divided into four groups (15 mice per group). The control group and cadmium groups received distilled water, whereas the sodium-selenite group received 0.2 mg/kg SSe and the NSe group received 0.2 mg/kg NSe intragastrically for 2 weeks. On the last day, all the other groups were treated with Cd (126 mg/kg) except for the control group. The results obtained in this study showed that NSe alleviated Cd-induced hepatic pathological changes. Furthermore, NSe reduced the activities of ALT and AST as well as the content of MDA, while elevated the activities of T-AOC, T-SOD and GSH (P < 0.05). In addition, the NSe group significantly increased mRNA expressions of Nrf2 pathway related molecules (Nrf2, HO-1, NQO-1, GST, GSH-Px, CAT and SOD) compared to the Cd group (P < 0.05). In conclusion, NSe shows its potentiality to reduce Cd-induced liver injury by inhibiting oxidative stress and activating the Nrf2 pathway

Towards neuroscience of the everyday world (NEW) using functional near infrared spectroscopy

Published in final edited form as: Curr Opin Biomed Eng. 2021 June ; 18: doi:10.1016/j.cobme.2021.100272.Functional near-infrared spectroscopy (fNIRS) assesses human brain activity by noninvasively measuring changes of cerebral hemoglobin concentrations caused by modulation of neuronal activity. Recent progress in signal processing and advances in system design, such as miniaturization, wearability, and system sensitivity, have strengthened fNIRS as a viable and cost-effective complement to functional magnetic resonance imaging, expanding the repertoire of experimental studies that can be performed by the neuroscience community. The availability of fNIRS and electroencephalography for routine, increasingly unconstrained, and mobile brain imaging is leading toward a new domain that we term “Neuroscience of the Everyday World” (NEW). In this light, we review recent advances in hardware, study design, and signal processing, and discuss challenges and future directions.U01EB029856 - National Institutes of HealthAccepted manuscrip

IFSO: A Integrated Framework For Automatic/Semi-automatic Software Refactoring and Analysis

To automatically/semi-automatically improve internal structures of a legacy system, there are several challenges: most available software analysis algorithms focus on only one particular granularity level (e.g., method level, class level) without considering possible side effects on other levels during the process; the quality of a software system cannot be judged by a single algorithm; software analysis is a time-consuming process which typically requires lengthy interactions. In this thesis, we present a framework, IFSO (Integrated Framework for automatic/semi-automatic Software refactoring and analysis), as a foundation for automatic/semi-automatic software refactoring and analysis. Our proposed conceptual model, LSR (Layered Software Representation Model), defines an abstract representation for software using a layered approach. Each layer corresponds to a granularity level. The IFSO framework, which is built upon the LSR model for component-based software, represents software at the system level, component level, class level, method level and logic unit level. Each level can be customized by different algorithms such as cohesion metrics, design heuristics, design problem detection and operations independently. Cooperating between levels together, a global view and an interactive environment for software refactoring and analysis are presented by IFSO. A prototype was implemented for evaluation of our technology. Three case studies were developed based on the prototype: three metrics, dead code removing, low coupled unit detection

Stress Wave Hybrid Imaging for Detecting Wood Internal Defects under Sparse Signals

Stress wave technology is very suitable for detecting internal defects of standing trees, logs, and wood and has gradually become the mainstream technology in this research field. Usually, 12 sensors are positioned equidistantly around the cross-section of tree trunks in order to obtain enough stress wave signals. However, the arrangement of sensors is time-consuming and laborious, and maintaining the accuracy of stress wave imaging under sparse signals is a challenging problem. In this paper, a novel stress wave hybrid imaging method based on compressive sensing and elliptic interpolation is proposed. The spatial structure of the defective area is reconstructed by using the advantages of compressive sensing in sparse signal representation and solution of stress waves, and the healthy area is reconstructed by using the elliptic space interpolation method. Then, feature points are selected and mixed for imaging. The comparative experimental results show that the overall imaging accuracy of the proposed method reaches 89.7%, and the high-quality imaging effect can be guaranteed when the number of sensors is reduced to 10, 8, or even 6

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To automatically/semi-automatically improve internal structures of a legacy system, there are several challenges: most available software analysis algorithms focus on only one particular granularity level (e.g., method level, class level) without considering possible side effects on other levels during the process; the quality of a software system cannot be judged by a single algorithm; software analysis is a time-consuming process which typically requires lengthy interactions. In this thesis, we present a framework, IFSO (Integrated Framework for automatic/semi-automatic Software refactoring and analysis), as a foundation for automatic/semi-automatic software refactoring and analysis. Our proposed conceptual model, LSR (Layered Software Representation Model), defines an abstract representation for software using a layered approach. Each layer corresponds to a granularity level. The IFSO framework, which is built upon the LSR model for component-based software, represents software at the system level, component level, class level, method level and logic unit level. Each level can be customized by different algorithms such as cohesion metrics, design heuristics, design proble

Identity-Based Key-Encapsulation Mechanism from Multilinear Maps

Abstract. We construct an Identity-Based Key Encapsulation Mechanism (IB-KEM) in a generic “leveled ” multilinear map setting and prove its security under multilinear decisional Diffie-Hellmanin assumption in the selective-ID model. Then, we make our IB-KEM translated to the GGH framework, which defined an “approximate ” version of a multilinear group family from ideal lattices, and modify our proof of security to use the GGH graded algebras analogue of multilinear maps.