Video Question Answering via Attribute-Augmented Attention Network Learning

Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.Comment: Accepted for SIGIR 201

Dynamics for a stochastic delayed SIRS epidemic model

In this paper, we consider a stochastic delayed SIRS epidemic model with seasonal variation. Firstly, we prove that the system is mathematically and biologically well-posed by showing the global existence, positivity and stochastically ultimate boundneness of the solution. Secondly, some sufficient conditions on the permanence and extinction of the positive solutions with probability one are presented. Thirdly, we show that the solution of the system is asymptotical around of the disease-free periodic solution and the intensity of the oscillation depends of the intensity of the noise. Lastly, the existence of stochastic nontrivial periodic solution for the system is obtained

Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts

Finding neural features of suicide attempts (SA) in major depressive disorder (MDD) may be helpful in preventing suicidal behavior. The ventral and medial prefrontal cortex (PFC), as well as the amygdala form a circuit implicated in emotion regulation and the pathogenesis of MDD. The aim of this study was to identify whether patients with MDD who had a history of SA show structural and functional connectivity abnormalities in the amygdala and PFC relative to MDD patients without a history of SA. We measured gray matter volume in the amygdala and PFC and amygdala-PFC functional connectivity using structural and functional magnetic resonance imaging (MRI) in 158 participants [38 MDD patients with a history of SA, 60 MDD patients without a history of SA, and 60 healthy control (HC)]. MDD patients with a history of SA had decreased gray matter volume in the right and left amygdala (F = 30.270, P = 0.000), ventral/medial/dorsal PFC (F = 15.349, P = 0.000), and diminished functional connectivity between the bilateral amygdala and ventral and medial PFC regions (F = 22.467, P = 0.000), compared with individuals who had MDD without a history of SA, and the HC group. These findings provide evidence that the amygdala and PFC may be closely related to the pathogenesis of suicidal behavior in MDD and implicate the amygdala-ventral/medial PFC circuit as a potential target for suicide intervention

Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts

Finding neural features of suicide attempts (SA) in major depressive disorder (MDD) may be helpful in preventing suicidal behavior. The ventral and medial prefrontal cortex (PFC), as well as the amygdala form a circuit implicated in emotion regulation and the pathogenesis of MDD. The aim of this study was to identify whether patients with MDD who had a history of SA show structural and functional connectivity abnormalities in the amygdala and PFC relative to MDD patients without a history of SA. We measured gray matter volume in the amygdala and PFC and amygdala-PFC functional connectivity using structural and functional magnetic resonance imaging (MRI) in 158 participants [38 MDD patients with a history of SA, 60 MDD patients without a history of SA, and 60 healthy control (HC)]. MDD patients with a history of SA had decreased gray matter volume in the right and left amygdala (F = 30.270, P = 0.000), ventral/medial/dorsal PFC (F = 15.349, P = 0.000), and diminished functional connectivity between the bilateral amygdala and ventral and medial PFC regions (F = 22.467, P = 0.000), compared with individuals who had MDD without a history of SA, and the HC group. These findings provide evidence that the amygdala and PFC may be closely related to the pathogenesis of suicidal behavior in MDD and implicate the amygdala-ventral/medial PFC circuit as a potential target for suicide intervention

Verapamil Ameliorates Hepatic Metaflammation by Inhibiting Thioredoxin-Interacting Protein/NLRP3 Pathways

Activation of thioredoxin-interacting protein (TXNIP)/nod-like receptor protein 3 (NLRP3) inflammasome plays a critical role in pathogenesis of non-alcoholic fatty liver disease. This study investigated the protective effects of verapamil on hepatic metaflammation in a rodent model of high-fat (HF) diet-induced obesity (DIO). DIO was induced in a subset of mice provided with HF diet (45% kcal fat). After 10 weeks of HF diet, verapamil was administered by intraperitoneal injection. The experimental groups included the following: (1) normal diet group, (2) normal diet + treatment with verapamil (VER) group, (3) HF control group, (4) HF+VER (25 mg/kg/day) group. After 1 week of each treatment, blood and liver tissues were collected, and glucose control, serum triglyceride (TG) level, inflammation, and TXNIP/NLRP3 inflammasome were analyzed. Verapamil administration caused no alteration in food intake. HF diet impaired glucose control and increased body weight and serum TG levels. Hepatic inflammation was aggravated in HF-fed mice, as demonstrated by increased levels of pro-inflammatory markers interleukin-1β (IL-1β) and IL-18 in the liver. On the other hand, verapamil administration significantly improved glucose control, body weight, and serum TG levels. Verapamil treatment also reduced pro-inflammatory marker levels. These improvements were accompanied by alterations in activation of TXNIP/NLRP3 inflammasome. The observed results demonstrate that verapamil ameliorates hepatic metaflammation by inhibiting TXNIP/NLRP3 pathways

Overexpression of GATA2 Enhances Development and Maintenance of Human Embryonic Stem Cell-Derived Hematopoietic Stem Cell-like Progenitors

GATA2 is essential for the endothelial-to-hematopoietic transition (EHT) and generation of hematopoietic stem cells (HSCs). It is poorly understood how GATA2 controls the development of human pluripotent stem cell (hPSC)-derived HS-like cells. Here, using human embryonic stem cells (hESCs) in which GATA2 overexpression was induced by doxycycline (Dox), we elucidated the dual functions of GATA2 in definitive hematopoiesis before and after the emergence of CD34⁺CD45⁺CD90⁺CD38⁻ HS-like cells. Specifically, GATA2 promoted expansion of hemogenic precursors via the EHT and then helped to maintain HS-like cells in a quiescent state by regulating cell cycle. RNA sequencing showed that hPSC-derived HS-like cells were very similar to human fetal liver-derived HSCs. Our findings will help to elucidate the mechanism that controls the early stages of human definitive hematopoiesis and may help to develop a strategy to generate hPSC-derived HSCs

Overexpression of GATA2 Enhances Development and Maintenance of Human Embryonic Stem Cell-Derived Hematopoietic Stem Cell-like Progenitors

GATA2 is essential for the endothelial-to-hematopoietic transition (EHT) and generation of hematopoietic stem cells (HSCs). It is poorly understood how GATA2 controls the development of human pluripotent stem cell (hPSC)-derived HS-like cells. Here, using human embryonic stem cells (hESCs) in which GATA2 overexpression was induced by doxycycline (Dox), we elucidated the dual functions of GATA2 in definitive hematopoiesis before and after the emergence of CD34+CD45+CD90+CD38– HS-like cells. Specifically, GATA2 promoted expansion of hemogenic precursors via the EHT and then helped to maintain HS-like cells in a quiescent state by regulating cell cycle. RNA sequencing showed that hPSC-derived HS-like cells were very similar to human fetal liver-derived HSCs. Our findings will help to elucidate the mechanism that controls the early stages of human definitive hematopoiesis and may help to develop a strategy to generate hPSC-derived HSCs

Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training

We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users during inference. Our method incorporates a gating mechanism and LID loss, enabling transformer experts to learn language-specific information. By combining gated transformer experts with shared transformer layers, we construct multilingual transformer blocks and utilize linear experts to effectively regularize the joint network. The curriculum training scheme leverages LID to guide the gated experts in improving their respective language performance. Experimental results on a bilingual task involving English and Spanish demonstrate significant improvements, with average relative word error reductions of 12.5% and 7.3% compared to the baseline bilingual and monolingual models, respectively. Notably, our method achieves performance comparable to the upper-bound model trained and inferred with oracle LID. Extending our approach to trilingual, quadrilingual, and pentalingual models reveals similar advantages to those observed in the bilingual models, highlighting its ease of extension to multiple languages

On decoder-only architecture for speech-to-text and large language model integration

Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. However, the seamless integration of speech signals into LLMs has not been explored well. The "decoder-only" architecture has also not been well studied for speech processing tasks. In this research, we introduce Speech-LLaMA, a novel approach that effectively incorporates acoustic information into text-based large language models. Our method leverages Connectionist Temporal Classification and a simple audio encoder to map the compressed acoustic features to the continuous semantic space of the LLM. In addition, we further probe the decoder-only architecture for speech-to-text tasks by training a smaller scale randomly initialized speech-LLaMA model from speech-text paired data alone. We conduct experiments on multilingual speech-to-text translation tasks and demonstrate a significant improvement over strong baselines, highlighting the potential advantages of decoder-only models for speech-to-text conversion