DepthMOT: Depth Cues Lead to a Strong Multi-Object Tracker

Accurately distinguishing each object is a fundamental goal of Multi-object tracking (MOT) algorithms. However, achieving this goal still remains challenging, primarily due to: (i) For crowded scenes with occluded objects, the high overlap of object bounding boxes leads to confusion among closely located objects. Nevertheless, humans naturally perceive the depth of elements in a scene when observing 2D videos. Inspired by this, even though the bounding boxes of objects are close on the camera plane, we can differentiate them in the depth dimension, thereby establishing a 3D perception of the objects. (ii) For videos with rapidly irregular camera motion, abrupt changes in object positions can result in ID switches. However, if the camera pose are known, we can compensate for the errors in linear motion models. In this paper, we propose \textit{DepthMOT}, which achieves: (i) detecting and estimating scene depth map \textit{end-to-end}, (ii) compensating the irregular camera motion by camera pose estimation. Extensive experiments demonstrate the superior performance of DepthMOT in VisDrone-MOT and UAVDT datasets. The code will be available at \url{https://github.com/JackWoo0831/DepthMOT}

G2T: A simple but versatile framework for topic modeling based on pretrained language model and community detection

It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. However, these approaches suffer from the inability to select appropriate parameters and incomplete models that overlook the quantitative relation between words with topics and topics with text. To solve these issues, we propose graph to topic (G2T), a simple but effective framework for topic modelling. The framework is composed of four modules. First, document representation is acquired using pretrained language models. Second, a semantic graph is constructed according to the similarity between document representations. Third, communities in document semantic graphs are identified, and the relationship between topics and documents is quantified accordingly. Fourth, the word--topic distribution is computed based on a variant of TFIDF. Automatic evaluation suggests that G2T achieved state-of-the-art performance on both English and Chinese documents with different lengths. Human judgements demonstrate that G2T can produce topics with better interpretability and coverage than baselines. In addition, G2T can not only determine the topic number automatically but also give the probabilistic distribution of words in topics and topics in documents. Finally, G2T is publicly available, and the distillation experiments provide instruction on how it works

Loss of Asxl1 leads to myelodysplastic syndrome-like disease in mice

ASXL1 is mutated/deleted with high frequencies in multiple forms of myeloid malignancies, and its alterations are associated with poor prognosis. De novo ASXL1 mutations cause Bohring-Opitz syndrome characterized by multiple congenital malformations. We show that Asxl1 deletion in mice led to developmental abnormalities including dwarfism, anophthalmia, and 80% embryonic lethality. Surviving Asxl1(-/-) mice lived for up to 42 days and developed features of myelodysplastic syndrome (MDS), including dysplastic neutrophils and multiple lineage cytopenia. Asxl1(-/-) mice had a reduced hematopoietic stem cell (HSC) pool, and Asxl1(-/-) HSCs exhibited decreased hematopoietic repopulating capacity, with skewed cell differentiation favoring granulocytic lineage. Asxl1(+/-) mice also developed mild MDS-like disease, which could progress to MDS/myeloproliferative neoplasm, demonstrating a haploinsufficient effect of Asxl1 in the pathogenesis of myeloid malignancies. Asxl1 loss led to an increased apoptosis and mitosis in Lineage(-)c-Kit(+) (Lin(-)c-Kit(+)) cells, consistent with human MDS. Furthermore, Asxl1(-/-) Lin(-)c-Kit(+) cells exhibited decreased global levels of H3K27me3 and H3K4me3 and altered expression of genes regulating apoptosis (Bcl2, Bcl2l12, Bcl2l13). Collectively, we report a novel ASXL1 murine model that recapitulates human myeloid malignancies, implying that Asxl1 functions as a tumor suppressor to maintain hematopoietic cell homeostasis. Future work is necessary to clarify the contribution of microenvironment to the hematopoietic phenotypes observed in the constitutional Asxl1(-/-) mice

Identification and multiply robust estimation of causal effects via instrumental variables from an auxiliary heterogeneous population

Evaluating causal effects in a primary population of interest with unmeasured confounders is challenging. Although instrumental variables (IVs) are widely used to address unmeasured confounding, they may not always be available in the primary population. Fortunately, IVs might have been used in previous observational studies on similar causal problems, and these auxiliary studies can be useful to infer causal effects in the primary population, even if they represent different populations. However, existing methods often assume homogeneity or equality of conditional average treatment effects between the primary and auxiliary populations, which may be limited in practice. This paper aims to remove the homogeneity requirement and establish a novel identifiability result allowing for different conditional average treatment effects across populations. We also construct a multiply robust estimator that remains consistent despite partial misspecifications of the observed data model and achieves local efficiency if all nuisance models are correct. The proposed approach is illustrated through simulation studies. We finally apply our approach by leveraging data from lower income individuals with cigarette price as a valid IV to evaluate the causal effect of smoking on physical functional status in higher income group where strong IVs are not available

Command Filter-Based Finite-Time Constraint Control for Flexible Joint Robots Stochastic System with Unknown Dead Zones

This article studies the problem of finite-time (FT) adaptive constraint control for flexible joint robots (FJR) stochastic system. First, by combining the command filtered backstepping method with FT control, not only does it solve the 'explosion of complexity' problem, but it also ensures that the error of the FJR stochastic system converges in FT. Second, the asymmetric time-varying output constraint problem of FJR stochastic system is solved by designing a nonlinear transformation function (NTF) only depends on the system output, which reduces the difficulty of system stability analyses and relaxes the constraints on the initial value of the output. Third, by exploiting the fuzzy logic system, the adverse effect of the unknown stochastic nonlinear disturbances generated by the harmonic drive of the FJR system is effectively overcome. Furthermore, by utilizing the boundary information of dead-zone slopes, the adverse impact of the dead-zone inputs on the efficacy of control is effectively compensated. Finally, the Lyapunov approach is employed to indicate that the signals are convergent, and the simulation results demonstrate the effectiveness of the control algorithm.</p

Fuzzy Observer-based Command Filtered Adaptive Control of Flexible Joint Robots with Time-varying Output Constraints

Flexible joint robots (FJR) systems are used in many aspects of actual production due to its high compliance, low energy consumption, human-computer interaction safety and other characteristics. A fuzzy observer-based command filtered adaptive control method is applied to make FJR systems with time-varying output constraints (TVOC) and model uncertainties operate safely in a complex environment in this brief. Chiefly, a fuzzy observer is developed to estimate the link's angle velocity and motor angle velocity of the FJR. Next, by combining time-varying barrier Lyapunov function (TVBLF) with fuzzy logic systems, the uncertainties of the FJR model are approximated without violating the TVOC. Besides, the command filtered method with error compensation signal resolves the issue of 'explosion of complexity' and removes the impacts of filtering errors. The stability of the FJR system is verified by Lyapunov stability theory. Simulation shows that the devised approach can insure the TVOC, the validity of the observer and position tracking accuracy of the system.</p

Event-Triggered Adaptive Fuzzy Finite-Time Output Feedback Control for Stochastic Nonlinear Systems With Input and Output Constraints

This article focuses on the problem of designing an adaptive fuzzy event-triggered finite-time output feedback control for stochastic nonlinear systems with input and output constraints. A fuzzy observer is designed to estimate the unmeasured states. The quartic asymmetric time-varying barrier Lyapunov function is established to ensure constraint satisfaction. By utilizing the stochastic theory, finite-time command filtered backstepping method and event-triggered mechanism, a finite-time event-triggered controller is recursively designed, which can not only guarantee finite-time convergent property, but also reduce communication pressure. Meanwhile, the matter of 'explosion of complexity' is removed by introducing the finite-time command filter and the effect of filtered errors is offset by constructing error compensation signals. Moreover, an auxiliary system is introduced to handle the input constraint. Finally, the effectiveness of the theoretical results is demonstrated by the simulation example.</p