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Jun Feng, Liya Zhang, Jihong Tang, Bingbing Zhang, Xiao Xiao, Xiaoyan Shi Department of Neurology, Children’s Hospital of Soochow University, Suzhou, Jiangsu, 215025, People’s Republic of ChinaCorrespondence: Liya Zhang; Jihong Tang, Email zhliyasz@163.com; tjhsuzhou@163.comObjective: To evaluate the efficacy and safety of lacosamide (LCM) monotherapy in the treatment of self-limited epilepsy with centrotemporal spikes (SeLECTS).Methods: In this study, 89 children with SeLECTS who were treated with LCM monotherapy in the Children’s Hospital Affiliated to Soochow University from June 2019 to June 2021 were included. Clinical seizures and spike wave index (SWI) on video EEG during slow-wave sleep were evaluated before and after treatment. The role of LCM monotherapy in improving SWI, controlling clinical seizures and improving cognition was analyzed, and corresponding adverse reactions were documented.Results: There were 52 males and 37 females in this group, with an average age of 7.6 ± 2.1 years. The total effective rate was 93.83% and at 18 months of treatment, the cumulative control rate was 85.19%, the retention rate was 91.01% and the effective rate in terms of EEG spike index was 72.92%, all of which showed a high rate; there was no statistically significant difference in intelligence quotient before and after treatment (P > 0.05). In addition, it was found in the study that the earlier the age of onset, the less obvious the improvement in SWI after LCM treatment; the lower the baseline seizure frequency, the more significant the improvement in SWI after LCM treatment.Conclusion: LCM monotherapy had curative effect and adverse reactions for SeLECTS, with no negative impact on cognition. These significant findings indicate that LCM is likely to become a widely prescribed ASM for the treatment of SeLECTS. Meanwhile, the onset age and baseline seizure frequency had certain value in judging prognosis and predicting curative effect.Keywords: lacosamide, ep

We present a sensor misalignment-tolerant AUV navigation method that leverages measurements from an acoustic array and dead reckoned information. Recent studies have demonstrated the potential use of passive acoustic Direction of Arrival (DoA) measurements for AUV navigation without requiring ranging measurements. However, the sensor misalignment between the acoustic array and the attitude sensor was not accounted for. Such misalignment may deteriorate the navigation accuracy. This paper proposes a novel approach that allows simultaneous AUV navigation, beacon localization, and sensor alignment. An Unscented Kalman Filter (UKF) that enables the necessary calculations to be completed at an affordable computational load is developed. A Nonlinear Least Squares (NLS)-based technique is employed to find an initial solution for beacon localization and sensor alignment as early as possible using a short-term window of measurements. Experimental results demonstrate the performance of the proposed method.

Few-shot learning has been successfully applied to medical image classification as only very few medical examples are available for training. Due to the challenging problem of limited number of annotated medical images, image representations should not be solely derived from a single image modality which is insufficient for characterizing concept classes. In this paper, we propose a new prompting multi-modal model paradigm on medical image classification based on multi-modal foundation models, called PM2. Besides image modality,PM2 introduces another supplementary text input, known as prompt, to further describe corresponding image or concept classes and facilitate few-shot learning across diverse modalities. To better explore the potential of prompt engineering, we empirically investigate five distinct prompt schemes under the new paradigm. Furthermore, linear probing in multi-modal models acts as a linear classification head taking as input only class token, which ignores completely merits of rich statistics inherent in high-level visual tokens. Thus, we alternatively perform a linear classification on feature distribution of visual tokens and class token simultaneously. To effectively mine such rich statistics, a global covariance pooling with efficient matrix power normalization is used to aggregate visual tokens. Then we study and combine two classification heads. One is shared for class token of image from vision encoder and prompt representation encoded by text encoder. The other is to classification on feature distribution of visual tokens from vision encoder. Extensive experiments on three medical datasets show that our PM2 significantly outperforms counterparts regardless of prompt schemes and achieves state-of-the-art performance.

In recent years, there has been an increased academic interest revolving around the beneficial or pernicious effects of ideological extremity and (uncivil) political discussion over democracy. For instance, citizens’ ideological predispositions and higher levels of political discussion have been linked with a more active and vibrant political life. In fact, ideological extremity and uncivil discussion foster institutionalized political engagement. However, less explored in the literature remains whether such polarization and uncivil discussions may be related to unlawful political behavior such as illegal protest. This study contends that one of the main drivers of illegal protest behavior lies in online uncivil political discussion, specifically through the normalization and activation of further incivility. We tested this through a two-wave panel data drawn from a diverse US sample and cross-sectional, lagged, and autoregressive regression models. Mediation analysis was also conducted to test whether uncivil online discussion mediated the relationship between frequency of online political discussion and illegal protest engagement. Overall, we found that illegal protest was particularly associated with online uncivil discussion, while ideological extremity and other forms of online and offline discussions seemed to have no effect on unlawful protest over time.

In recent years, there has been an increased academic interest revolving around the beneficial or pernicious effects of ideological extremity and (uncivil) political discussion over democracy. For instance, citizens’ ideological predispositions and higher levels of political discussion have been linked with a more active and vibrant political life. In fact, ideological extremity and uncivil discussion foster institutionalized political engagement. However, less explored in the literature remains whether such polarization and uncivil discussions may be related to unlawful political behavior such as illegal protest. This study contends that one of the main drivers of illegal protest behavior lies in online uncivil political discussion, specifically through the normalization and activation of further incivility. We tested this through a two-wave panel data drawn from a diverse US sample and cross-sectional, lagged, and autoregressive regression models. Mediation analysis was also conducted to test whether uncivil online discussion mediated the relationship between frequency of online political discussion and illegal protest engagement. Overall, we found that illegal protest was particularly associated with online uncivil discussion, while ideological extremity and other forms of online and offline discussions seemed to have no effect on unlawful protest over time.

In recent years, there has been an increased academic interest revolving around the beneficial or pernicious effects of ideological extremity and (uncivil) political discussion over democracy. For instance, citizens’ ideological predispositions and higher levels of political discussion have been linked with a more active and vibrant political life. In fact, ideological extremity and uncivil discussion foster institutionalized political engagement. However, less explored in the literature remains whether such polarization and uncivil discussions may be related to unlawful political behavior such as illegal protest. This study contends that one of the main drivers of illegal protest behavior lies in online uncivil political discussion, specifically through the normalization and activation of further incivility. We tested this through a two-wave panel data drawn from a diverse US sample and cross-sectional, lagged, and autoregressive regression models. Mediation analysis was also conducted to test whether uncivil online discussion mediated the relationship between frequency of online political discussion and illegal protest engagement. Overall, we found that illegal protest was particularly associated with online uncivil discussion, while ideological extremity and other forms of online and offline discussions seemed to have no effect on unlawful protest over time.

In recent years, there has been an increased academic interest revolving around the beneficial or pernicious effects of ideological extremity and (uncivil) political discussion over democracy. For instance, citizens’ ideological predispositions and higher levels of political discussion have been linked with a more active and vibrant political life. In fact, ideological extremity and uncivil discussion foster institutionalized political engagement. However, less explored in the literature remains whether such polarization and uncivil discussions may be related to unlawful political behavior such as illegal protest. This study contends that one of the main drivers of illegal protest behavior lies in online uncivil political discussion, specifically through the normalization and activation of further incivility. We tested this through a two-wave panel data drawn from a diverse US sample and cross-sectional, lagged, and autoregressive regression models. Mediation analysis was also conducted to test whether uncivil online discussion mediated the relationship between frequency of online political discussion and illegal protest engagement. Overall, we found that illegal protest was particularly associated with online uncivil discussion, while ideological extremity and other forms of online and offline discussions seemed to have no effect on unlawful protest over time.

In recent years, there has been an increased academic interest revolving around the beneficial or pernicious effects of ideological extremity and (uncivil) political discussion over democracy. For instance, citizens’ ideological predispositions and higher levels of political discussion have been linked with a more active and vibrant political life. In fact, ideological extremity and uncivil discussion foster institutionalized political engagement. However, less explored in the literature remains whether such polarization and uncivil discussions may be related to unlawful political behavior such as illegal protest. This study contends that one of the main drivers of illegal protest behavior lies in online uncivil political discussion, specifically through the normalization and activation of further incivility. We tested this through a two-wave panel data drawn from a diverse US sample and cross-sectional, lagged, and autoregressive regression models. Mediation analysis was also conducted to test whether uncivil online discussion mediated the relationship between frequency of online political discussion and illegal protest engagement. Overall, we found that illegal protest was particularly associated with online uncivil discussion, while ideological extremity and other forms of online and offline discussions seemed to have no effect on unlawful protest over time.

In recent years, there has been an increased academic interest revolving around the beneficial or pernicious effects of ideological extremity and (uncivil) political discussion over democracy. For instance, citizens’ ideological predispositions and higher levels of political discussion have been linked with a more active and vibrant political life. In fact, ideological extremity and uncivil discussion foster institutionalized political engagement. However, less explored in the literature remains whether such polarization and uncivil discussions may be related to unlawful political behavior such as illegal protest. This study contends that one of the main drivers of illegal protest behavior lies in online uncivil political discussion, specifically through the normalization and activation of further incivility. We tested this through a two-wave panel data drawn from a diverse US sample and cross-sectional, lagged, and autoregressive regression models. Mediation analysis was also conducted to test whether uncivil online discussion mediated the relationship between frequency of online political discussion and illegal protest engagement. Overall, we found that illegal protest was particularly associated with online uncivil discussion, while ideological extremity and other forms of online and offline discussions seemed to have no effect on unlawful protest over time.

Elemental sulfur is not traditionally considered as an afterglow material, even though it can be endowed with fluorescence properties through processing it into nanodots. Herein, we discovered that elemental sulfur powder could emit room temperature phosphorescence (RTP) with a lifetime of 3.7 ms. A long-lived (>12 s) afterglow emission at 77 K could also be observed by the naked eye. Detailed investigations suggested that such a special phenomenon was attributed to impurity-related traps coupled with conduction and valence bands. After the sulfur is processed into nanodots, the rigid environment formed by the cross-linking of the surface ligands could stabilize the excited charges from quenching. This results in the promotion of RTP intensity and lifetime to achieve an emission lifetime of 200 ms. These results confirm the unique RTP of elemental sulfur powder, and also suggest the potential of sulfur-based materials as versatile components for the development of RTP materials.

In recent years, there has been an increased academic interest revolving around the beneficial or pernicious effects of ideological extremity and (uncivil) political discussion over democracy. For instance, citizens’ ideological predispositions and higher levels of political discussion have been linked with a more active and vibrant political life. In fact, ideological extremity and uncivil discussion foster institutionalized political engagement. However, less explored in the literature remains whether such polarization and uncivil discussions may be related to unlawful political behavior such as illegal protest. This study contends that one of the main drivers of illegal protest behavior lies in online uncivil political discussion, specifically through the normalization and activation of further incivility. We tested this through a two-wave panel data drawn from a diverse US sample and cross-sectional, lagged, and autoregressive regression models. Mediation analysis was also conducted to test whether uncivil online discussion mediated the relationship between frequency of online political discussion and illegal protest engagement. Overall, we found that illegal protest was particularly associated with online uncivil discussion, while ideological extremity and other forms of online and offline discussions seemed to have no effect on unlawful protest over time.

For video recognition task, a global representation summarizing the whole contents of the video snippets plays an important role for the final performance. However, existing video architectures usually generate it by using a simple, global average pooling (GAP) method, which has limited ability to capture complex dynamics of videos. For image recognition task, there exist evidences showing that covariance pooling has stronger representation ability than GAP. Unfortunately, such plain covariance pooling used in image recognition is an orderless representative, which cannot model spatio-temporal structure inherent in videos. Therefore, this paper proposes a Temporal-attentive Covariance Pooling(TCP), inserted at the end of deep architectures, to produce powerful video representations. Specifically, our TCP first develops a temporal attention module to adaptively calibrate spatio-temporal features for the succeeding covariance pooling, approximatively producing attentive covariance representations. Then, a temporal covariance pooling performs temporal pooling of the attentive covariance representations to characterize both intra-frame correlations and inter-frame cross-correlations of the calibrated features. As such, the proposed TCP can capture complex temporal dynamics. Finally, a fast matrix power normalization is introduced to exploit geometry of covariance representations. Note that our TCP is model-agnostic and can be flexibly integrated into any video architectures, resulting in TCPNet for effective video recognition. The extensive experiments on six benchmarks (e.g., Kinetics, Something-Something V1 and Charades) using various video architectures show our TCPNet is clearly superior to its counterparts, while having strong generalization ability. The source code is publicly available., Comment: Accepted to NeurIPS 2021; Project page: https://github.com/ZilinGao/Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition

Jinlong Yuan,1,* Chenlei Huang,2,* Zhenbao Li,1 Xiaochun Jiang,1 Xintong Zhao,1 Niansheng Lai,1 Dayong Xia,1 Degang Wu,1 Bingbing Zhang,1 Xuanzhi Wang,3 Xinggen Fang1 1Department of Neurosurgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, People’s Republic of China; 2Department of Clinical Laboratory, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, People’s Republic of China; 3Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xinggen FangDepartment of Neurosurgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Zheshan West Road 2, Wuhu, 241001, People’s Republic of ChinaEmail 18805536056@163.comObjective: Hemodynamic parameters are associated with the recurrence of intracranial aneurysms (IAs). Studies showed that high velocity and wall shear stress (WSS) were associated with IAs recurrence after endovascular treatment; nevertheless, factors such as small sample size, locations of IAs, and types of IAs (ie, sidewall or bifurcation) were neglected. The purpose of this study was to identify the hemodynamic characteristics associated with recurrence of middle cerebral artery bifurcation aneurysms (MCABAs) after total embolization by the method of computer fluid dynamics (CFD).Methods: Following inclusion criteria, we included 92 MCABAs treated with coils only after total embolization from January 2014 to January 2019. We segmented into recurrent and non-recurrent groups according to follow-up digital subtraction angiography (DSA). The MCABA models, including pre-operatively and immediate post-operatively, were reconstructed using 3D-DSAs. The hemodynamic parame

Jing Xie,1,* Yu Han,2,* Yueling Hong,3,* Wen-wen Li,4 Qilin Pei,3 Xueyi Zhou,4 Bingbing Zhang,5 Ying Wang6 1Chongqing Emergency Medical Center, Department of Endocrinology and Nephrology, The Fourth People’s Hospital of Chongqing, Central Hospital of Chongqing University, Chongqing 400014, People’s Republic of China; 2Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People’s Republic of China; 3Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, People’s Republic of China; 4Department of Pathology, Faculty of Basic Medicine, Chongqing Medical University, Chongqing 400016, People’s Republic of China; 5Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, People’s Republic of China; 6Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying Wang; Bingbing Zhang Email wangyingtgf@163.com; zbb@cqu.edu.cnBackground: Post-stroke depression (PSD) is one of the most common complications in stroke survivors. But, there are still no objective methods to diagnose PSD. This study aims to identify potential biomarkers for diagnosing PSD in middle-aged stroke survivors.Methods: Middle-aged subjects aged 30 to 59 years (92 PSD patients and 89 stroke survivors without depression) were included in this study. Urinary metabolites were detected by gas chromatography-mass spectrometry (GC-MS). Differential urinary metabolites and potential biomarkers were screened by applying statistical analysis.Results: The different urinary metabolic phenotypes between PSD patients and stroke survivors without depression were identified. A total of 12 differential urinary metabolites were accurately identified by usin

Dialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them. It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation. With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data. As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data. Furthermore it supports the integration of arbitrary machine learning models as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing annotations for a dataset. LIDA is fully open source, documented and publicly available [ https://github.com/Wluper/lida ], Comment: 9 pages, 7 figures, 1 table, EMNLP 2019

Underwater source localization problems are complicated and challenging: a) the sound propagation speed is often unknown and the unpredictable ocean current might lead to the uncertainties of sensor parameters (i.e. position and velocity); b) the underwater acoustic signal travels much slower than the radio one in terrestrial environments, thus resulting into a significantly severe Doppler effect; c) energy-efficient techniques are urgently required and hence in favour of the design with a low computational complexity. Considering these issues, we propose a simple and efficient underwater source localization approach based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, which copes with unknown propagation speed and sensor parameter errors. The proposed method mitigates the impact of the Doppler effect for accurately inferring the source parameters (i.e. position and velocity). The Cramér-Rao lower bounds (CRLBs) for this kind of localization are derived and, moreover, the analytical study shows that our method can yield the performance that is very close to the CRLB, particularly under small noise. The numerical results not only confirm the above conclusions but also show that our method outperforms other competing approaches., Circuits and Systems

Underwater source localization problems are complicated and challenging: a) the sound propagation speed is often unknown and the unpredictable ocean current might lead to the uncertainties of sensor parameters (i.e. position and velocity); b) the underwater acoustic signal travels much slower than the radio one in terrestrial environments, thus resulting into a significantly severe Doppler effect; c) energy-efficient techniques are urgently required and hence in favour of the design with a low computational complexity. Considering these issues, we propose a simple and efficient underwater source localization approach based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, which copes with unknown propagation speed and sensor parameter errors. The proposed method mitigates the impact of the Doppler effect for accurately inferring the source parameters (i.e. position and velocity). The Cramér-Rao lower bounds (CRLBs) for this kind of localization are derived and, moreover, the analytical study shows that our method can yield the performance that is very close to the CRLB, particularly under small noise. The numerical results not only confirm the above conclusions but also show that our method outperforms other competing approaches., Signal Processing Systems

Classification tasks are usually analysed and improved through new model architectures or hyperparameter optimisation but the underlying properties of datasets are discovered on an ad-hoc basis as errors occur. However, understanding the properties of the data is crucial in perfecting models. In this paper we analyse exactly which characteristics of a dataset best determine how difficult that dataset is for the task of text classification. We then propose an intuitive measure of difficulty for text classification datasets which is simple and fast to calculate. We show that this measure generalises to unseen data by comparing it to state-of-the-art datasets and results. This measure can be used to analyse the precise source of errors in a dataset and allows fast estimation of how difficult a dataset is to learn. We searched for this measure by training 12 classical and neural network based models on 78 real-world datasets, then use a genetic algorithm to discover the best measure of difficulty. Our difficulty-calculating code ( https://github.com/Wluper/edm ) and datasets ( http://data.wluper.com ) are publicly available., Comment: 27 pages, 6 tables, 3 figures (submitted for publication in June 2018), CoNLL 2018

Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which uses electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena. In this paper, we address this issue and propose two novel semidefinite programming (SDP) approaches which can be solved more efficiently. The numerical results validate our proposed SDP solvers in underwater environments, and indicate that the placement of the anchor nodes influences the RSS-based localization accuracy similarly as in the terrestrial counterpart. We also highlight that adopting traditional terrestrial RSS-based localization methods will fail in underwater scenarios., Accepted Author Manuscript, Circuits and Systems

Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which uses electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena. In this paper, we address this issue and propose two novel semidefinite programming (SDP) approaches which can be solved more efficiently. The numerical results validate our proposed SDP solvers in underwater environments, and indicate that the placement of the anchor nodes influences the RSS-based localization accuracy similarly as in the terrestrial counterpart. We also highlight that adopting traditional terrestrial RSS-based localization methods will fail in underwater scenarios., Accepted Author Manuscript, Signal Processing Systems