Achievable efficiencies for probabilistically cloning the states

We present an example of quantum computational tasks whose performance is enhanced if we distribute quantum information using quantum cloning. Furthermore we give achievable efficiencies for probabilistic cloning the quantum states used in implemented tasks for which cloning provides some enhancement in performance.Comment: 9 pages, 8 figure

Adding Chinese herbal medicine to probiotics for irritable bowel syndrome-diarrhea: A systematic review and meta-analysis of randomized controlled trials

© 2020 Beijing University of Chinese Medicine Objective: This study assessed whether Chinese herbal medicine (CHM) combined with probiotics/synbiotics for irritable bowel syndrome - diarrhea (IBS-D) was more effective and safer than probiotics/synbiotics alone. Methods: Ten databases were searched for randomized control trials (RCTs) of IBS-D as diagnosed by Manning or Rome criteria. Trials comparing probiotics and probiotics with CHM were included. The Cochrane risk of bias (ROB) was evaluated for each trial. RevMan 5.3 was used to conduct a meta-analysis. Results: Twenty-six RCTs were included (25 Chinese, 1 English), involving 2045 participants. Meta-analysis was conducted on two outcomes: overall symptom improvement and relapse. CHM combined with live Bifidobacterium and Lactobacillus preparations reduced relapse rate (RR 0.28, 95%CI 0.15–0.52, 3 trials, n = 205) compared with probiotics alone. The subgroup analysis showed the benefit of CHM prescriptions based on soothing liver and invigorating spleen (1.28, 1.14–1.44, 3, 244), invigorating spleen and resolving dampness (1.20, 1.03–1.41, 2, 128), or warming and invigorating spleen and kidney formulae (1.27, 1.09–1.46, 2, 210) combined with triple Bifidobacterium preparations than the same probiotics alone which improved overall symptoms for IBS-D. There was unclear bias in almost domains of ROB. Most studies had a high risk of bias due to lack of blinding of investigator and participants, and selective reporting. Conclusions: This study showed that CHM combined with probiotics may reduce relapse rate by 72%, and improve overall symptoms of IBS-D (as diagnosed by Rome II and III) compared to probiotics alone. From the limited subgroup analysis, only soothing liver and invigorating spleen formulae, represented by Tongxie Yaofang, added to triple Bifidobacterium preparations may be superior to the single preparations in terms of overall symptoms. However, due to the poor methodological quality and small sample size of the trials, these findings must be interpreted with caution

The effect of e-cigarettes on smoking cessation and cigarette smoking initiation: An evidence-based rapid review and meta-analysis.

The contribution made by e-cigarettes to smoking cessation continues to be controversial. Reports suggest that teenagers are becoming increasingly addicted to e-cigarettes and that e-cigarette use in adolescents is associated with subsequent cigarette smoking. Systematic searches of eleven databases were conducted (January 2015 to June 2020). Systematic reviews, randomized controlled trials (RCTs) and cohort studies comparing e-cigarettes with placebo e-cigarettes, nicotine replacement therapy (NRT) or no e-cigarette use were included. The two primary outcomes were smoking cessation among smokers and smoking initiation among non-smoking teenagers. The secondary outcome was adverse events. Data were synthesized using risk ratio (RR) or adjusted odds ratio (AOR) with 95% confidence interval (CI). Six systematic reviews, 5 RCTs and 24 cohort studies were identified. For smoking cessation, findings from 4 systematic reviews indicated that e-cigarettes contributed to cessation while one found the opposite. Meta-analysis of 5 RCTs suggested that e-cigarettes were superior to NRT or placebo for smoking cessation (RR=1.55; 95% CI: 1.00-2.40; I =57.6%; low certainty; 5 trials, n=4025). Evidence from 9 cohort studies showed that e-cigarette use was not associated with cessation (AOR=1.16; 95% CI: 0.88-1.54; I =69.0%; n=22220). Subgroup analysis suggested that intensive e-cigarette use may be associated with cessation. In terms of smoking initiation, adolescents who ever used e-cigarettes had a greater risk for smoking initiation than non-users (AOR=2.91; 95% CI: 2.61-3.23; I =61.0%; 15 trials, n=68943), the findings were consistent with one included systematic review. No serious adverse events were reported in the included studies. Low certainty evidence suggests that e-cigarettes appear to be potentially effective for smoking cessation. The use of e-cigarettes in adolescents may be associated with smoking initiation. No serious adverse events were reported. [Abstract copyright: © 2021 Zhang Y.Y. et al.

Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation

We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains. Unlike classical SCSS approaches, we consider a setting where only examples of the sources are available rather than their models, inspiring a data-driven approach. For source models with underlying cyclostationary Gaussian constituents, we establish a lower bound on the attainable mean squared error (MSE) for any separation method, model-based or data-driven. Our analysis further reveals the operation for optimal separation and the associated implementation challenges. As a computationally attractive alternative, we propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator. We demonstrate in simulation that, with suitable domain-informed architectural choices, our U-Net method can approach the optimal performance with substantially reduced computational burden

Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals

We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. In particular, we assume knowledge on the generation process of one of the signals, dubbed signal of interest (SOI), and no knowledge on the generation process of the second signal, referred to as interference. This form of the single-channel source separation problem is also referred to as interference rejection. We show that capturing high-resolution temporal structures (nonstationarities), which enables accurate synchronization to both the SOI and the interference, leads to substantial performance gains. With this key insight, we propose a domain-informed neural network (NN) design that is able to improve upon both "off-the-shelf" NNs and classical detection and interference rejection methods, as demonstrated in our simulations. Our findings highlight the key role communication-specific domain knowledge plays in the development of data-driven approaches that hold the promise of unprecedented gains.Comment: 9 pages, 6 figures, accepted at IEEE GLOBECOM 2022 (this version contains extended proofs

Whistle detection and classification for whales based on convolutional neural networks

Passive acoustic observation of whales is an increasingly important tool for whale research. Accurately detecting whale sounds and correctly classifying them into corresponding whale species are essential tasks, especially in the case when two species of whales vocalize in the same observed area. Whistles are vital vocalizations of toothed whales, such as killer whales and long-finned pilot whales. In this paper, based on deep convolutional neural networks (CNNs), a novel method is proposed to detect and classify whistles of both killer whales and long-finned pilot whales. Compared with traditional methods, the proposed one can automatically learn the sound characteristics from the training data, without specifying the sound features for classification and detection, and thus shows better adaptability to complex sound signals. First, the denoised sound to be analyzed is sent to the trained detection model to estimate the number and positions of the target whistles. The detected whistles are then sent to the trained classification model, which determines the corresponding whale species. A GUI interface is developed to assist with the detection and classification process. Experimental results show that the proposed method can achieve 97% correct detection rate and 95% correct classification rate on the testing set. In the future, the presented method can be further applied to passive acoustic observation applications for some other whale or dolphin species

Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201