A New Three-Dimensional (3d) Particle Coincidence Imaging System And Its Applications In Strong Field Studies Of Reaction Dynamics In Atoms And Molecules

Electron‐electron interaction is an important and interesting research theme both in chemistry and physics. Experimental study of electron correlation is hindered by the long dead time (the time within which no more than two electrons can be detected) of electron detection system. We developed a new three‐dimensional (3D) particle coincidence imaging system to remove this restriction. The detection system employs a new strategy: It uses a fast‐frame camera to record positional information on 2D MCPs/phosphor detector (so the particle velocities in two dimensions can be measured); It utilizes a high‐speed digitizer to pick up the signal from MCP lead, off‐line analysis is performed on the waveform recorded by the digitizer to get time information (so the velocity in third dimension is measured) with best resolution and accuracy. This particle coincidence imaging system has three major breakthroughs: It achieved 0.64 ns dead time for electron detection; It’s also possible to have true zero dead with less than 1 ns TOF uncertainty; The best TOF resolution reaches 32 ps. This detection system is then implemented in photoion‐photoelectron coincidence detection apparatus to study electron correlation (the main goal) and dynamics in dissociative double ionization. We also developed a new method to probe orbital alignment of atoms in photodissociation by strong field ionization

Inferring Affective Meanings of Words from Word Embedding

Affective lexicon is one of the most important resource in affective computing for text. Manually constructed affective lexicons have limited scale and thus only have limited use in practical systems. In this work, we propose a regression-based method to automatically infer multi-dimensional affective representation of words via their word embedding based on a set of seed words. This method can make use of the rich semantic meanings obtained from word embedding to extract meanings in some specific semantic space. This is based on the assumption that different features in word embedding contribute differently to a particular affective dimension and a particular feature in word embedding contributes differently to different affective dimensions. Evaluation on various affective lexicons shows that our method outperforms the state-of-the-art methods on all the lexicons under different evaluation metrics with large margins. We also explore different regression models and conclude that the Ridge regression model, the Bayesian Ridge regression model and Support Vector Regression with linear kernel are the most suitable models. Comparing to other state-of-the-art methods, our method also has computation advantage. Experiments on a sentiment analysis task show that the lexicons extended by our method achieve better results than publicly available sentiment lexicons on eight sentiment corpora. The extended lexicons are publicly available for access

MTTFsite : cross-cell-type TF binding site prediction by using multi-task learning

Motivation The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data. Results In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained

Power allocation for cache-aided small-cell networks with limited backhaul

Cache-aided small-cell network is becoming an effective method to improve the transmission rate and reduce the backhaul load. Due to the limited capacity of backhaul, less power should be allocated to users whose requested contents do not exist in the local caches to maximize the performance of caching. In this paper, power allocation is considered to improve the performance of cache-aided small-cell networks with limited backhaul, where interference alignment (IA) is utilized to manage interferences among users. Specifically, three power allocation algorithms are proposed. First, we come up with a power allocation algorithm to maximize the sum transmission rate of the network, considering the limitation of backhaul. Second, in order to have more users meet their rate requirements, a power allocation algorithm to minimizing the average outage probability is also proposed. In addition, in order to further improve the users’ experience, a power allocation algorithm that maximizes the average satisfaction of all the users is also designed. Simulation results are provided to show the effectiveness of the three proposed power allocation algorithms for cache-aided small-cell networks with limited backhaul

GPAvatar: Generalizable and Precise Head Avatar from Image(s)

Head avatar reconstruction, crucial for applications in virtual reality, online meetings, gaming, and film industries, has garnered substantial attention within the computer vision community. The fundamental objective of this field is to faithfully recreate the head avatar and precisely control expressions and postures. Existing methods, categorized into 2D-based warping, mesh-based, and neural rendering approaches, present challenges in maintaining multi-view consistency, incorporating non-facial information, and generalizing to new identities. In this paper, we propose a framework named GPAvatar that reconstructs 3D head avatars from one or several images in a single forward pass. The key idea of this work is to introduce a dynamic point-based expression field driven by a point cloud to precisely and effectively capture expressions. Furthermore, we use a Multi Tri-planes Attention (MTA) fusion module in the tri-planes canonical field to leverage information from multiple input images. The proposed method achieves faithful identity reconstruction, precise expression control, and multi-view consistency, demonstrating promising results for free-viewpoint rendering and novel view synthesis.Comment: ICLR 2024, code is available at https://github.com/xg-chu/GPAvata

Moxibustion for Diarrhea-Predominant Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background. The complementary and alternative medicines in treatment of diarrhea-predominant irritable bowel syndrome (IBS-D) are controversial. Methods. We searched PubMed, Ovid Embase, Web of Science, Cochrane Library databases, CNKI, Wanfang Database, CBM, VIP, and AMED for randomized controlled trials (RCTs) of moxibustion compared with pharmacological medications in patients with IBS-D. A meta-analysis was performed using both fixed and random-effects models based on heterogeneity across studies. Results. In total, 568 patients in 7 randomized controlled trials were randomly treated with moxibustion and pharmacological medications. The improvement of global IBS-D symptoms and overall scores was significant (P=0.0001 and P<0.0001, resp.) in patients treated by moxibustion only compared to pharmacological medications. The specific IBS-D symptoms of abdominal pain, abdominal distension, abnormal stool, and defecation frequency were alleviated in patients treated by moxibustion compared to pharmacological medications, but no significance was found except for abdominal distension and defecation frequency (P=0.03 and P=0.02, resp.). There were no serious adverse events related to moxibustion. Conclusions. Moxibustion appears to be effective in treating IBS-D compared with pharmacological medications. However, further large, rigorously designed trials are warranted due to insufficient methodological rigor in the included trials