NLP Analysis and Recommendation System for Yelp

Yelp provides a valuable platform to share massive restaurant information, but it is difficult for its users to distinguish a relevant one among others. People are overwhelmed by multifarious information and unable to efficaciously glean germane information. Thus, it’s necessary to build a recommendation model which can filter and prioritize information, efficiently recommending appropriate restaurants to Yelp’s users, so the users can make correct decisions. Meanwhile, it can also help businesses to target their potential customers more accurately by sending similar-preference recommendations. This research explores different preferences and topics from Yelp restaurant reviews to understand characteristics of each user and restaurant, and then applies four practical algorithms to provide the most precise and personalized restaurant recommendations for the users

PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization

Deep neural networks (DNNs) are vulnerable to adversarial attacks. It is found empirically that adversarially robust generalization is crucial in establishing defense algorithms against adversarial attacks. Therefore, it is interesting to study the theoretical guarantee of robust generalization. This paper focuses on norm-based complexity, based on a PAC-Bayes approach (Neyshabur et al., 2017). The main challenge lies in extending the key ingredient, which is a weight perturbation bound in standard settings, to the robust settings. Existing attempts heavily rely on additional strong assumptions, leading to loose bounds. In this paper, we address this issue and provide a spectrally-normalized robust generalization bound for DNNs. Compared to existing bounds, our bound offers two significant advantages: Firstly, it does not depend on additional assumptions. Secondly, it is considerably tighter, aligning with the bounds of standard generalization. Therefore, our result provides a different perspective on understanding robust generalization: The mismatch terms between standard and robust generalization bounds shown in previous studies do not contribute to the poor robust generalization. Instead, these disparities solely due to mathematical issues. Finally, we extend the main result to adversarial robustness against general non-$\ell_p$ attacks and other neural network architectures.Comment: NeurIPS 202

Adversarial Rademacher Complexity of Deep Neural Networks

Deep neural networks are vulnerable to adversarial attacks. Ideally, a robust model shall perform well on both the perturbed training data and the unseen perturbed test data. It is found empirically that fitting perturbed training data is not hard, but generalizing to perturbed test data is quite difficult. To better understand adversarial generalization, it is of great interest to study the adversarial Rademacher complexity (ARC) of deep neural networks. However, how to bound ARC in multi-layers cases is largely unclear due to the difficulty of analyzing adversarial loss in the definition of ARC. There have been two types of attempts of ARC. One is to provide the upper bound of ARC in linear and one-hidden layer cases. However, these approaches seem hard to extend to multi-layer cases. Another is to modify the adversarial loss and provide upper bounds of Rademacher complexity on such surrogate loss in multi-layer cases. However, such variants of Rademacher complexity are not guaranteed to be bounds for meaningful robust generalization gaps (RGG). In this paper, we provide a solution to this unsolved problem. Specifically, we provide the first bound of adversarial Rademacher complexity of deep neural networks. Our approach is based on covering numbers. We provide a method to handle the robustify function classes of DNNs such that we can calculate the covering numbers. Finally, we provide experiments to study the empirical implication of our bounds and provide an analysis of poor adversarial generalization

Extensive analysis of D7S486 in primary gastric cancer supports TESTIN as a candidate tumor suppressor gene

<p>Abstract</p> <p>Background</p> <p>High frequency of loss of heterozygosity (LOH) was found at D7S486 in primary gastric cancer (GC). And we found a high frequency of LOH region on 7q31 in primary GC from China, and identified D7S486 to be the most frequent LOH locus. This study was aimed to determine what genes were affected by the LOH and served as tumor suppressor genes (TSGs) in this region. Here, a high-throughput single nucleotide polymorphisms (SNPs) microarray fabricated in-house was used to analyze the LOH status around D7S486 on 7q31 in 75 patients with primary GC. Western blot, immunohistochemistry, and RT-PCR were used to assess the protein and mRNA expression of TESTIN (TES) in 50 and 140 primary GC samples, respectively. MTS assay was used to investigate the effect of TES overexpression on the proliferation of GC cell lines. Mutation and methylation analysis were performed to explore possible mechanisms of TES inactivation in GC.</p> <p>Results</p> <p>LOH analysis discovered five candidate genes (<it>ST7</it>, <it>FOXP2</it>, <it>MDFIC</it>, <it>TES </it>and <it>CAV1</it>) whose frequencies of LOH were higher than 30%. However, only <it>TES </it>showed the potential to be a TSG associated with GC. Among 140 pairs of GC samples, decreased <it>TES </it>mRNA level was found in 96 (68.6%) tumor tissues when compared with matched non-tumor tissues (<it>p </it>< 0.001). Also, reduced TES protein level was detected in 36 (72.0%) of all 50 tumor tissues by Western blot (<it>p </it>= 0.001). In addition, immunohistochemical staining result was in agreement with that of RT-PCR and Western blot. Down regulation of TES was shown to be correlated with tumor differentiation (<it>p </it>= 0.035) and prognosis (<it>p </it>= 0.035, log-rank test). Its overexpression inhibited the growth of three GC cell lines. Hypermethylation of <it>TES </it>promoter was a frequent event in primary GC and GC cell lines. However, no specific gene mutation was observed in the coding region of the <it>TES </it>gene.</p> <p>Conclusions</p> <p>Collectively, all results support the role of <it>TES </it>as a TSG in gastric carcinogenesis and that <it>TES </it>is inactivated primarily by LOH and CpG island methylation.</p

Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection

IntroductionSleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients’ suffering.MethodsTo this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany.ResultsThe experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%.DiscussionAll of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor’s diagnostic process and provide a better medical experience for patients

NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel

How to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (XGBoost). The proposed method used the Latin hypercube sampling method to generate the dataset samples of geo-mechanical parameters and adopted XGBoost to establish the model of the nonlinear relationship between displacements and surrounding rock mechanical parameters. And NSGA–III was used to optimize the surrogate model hyper-parameters. Finally, the failure probability was computed by the optimized surrogate model. The proposed approach was firstly implemented in the analysis of a horseshoe-shaped highway tunnel to illustrate the efficiency of the approach. Then, in comparison to the support vector regression method and the back propagation neural network method, the feasibility, validity and advantages of XGBoost were demonstrated for practical problems. Using XGBoost to achieve Monte Carlo simulation, a surrogate solution can be provided for numerical simulation analysis to overcome the time-consuming reliability evaluation of initial support structures in soft rock tunnels. The proposed method can evaluate quickly the large deformation disaster risks of non-circular deep soft rock tunnels

Determining the Operator for the Public Toll Road

After the BOT road operation contract expires, generally, the road will be transferred to the government, and then the government operates the road independently without charging costs from its users. Facing the huge amount of the operation cost, Chinese government tends to continue to charge the road users to guarantee the high quality of road operation. Then, the government will have to decide whether a private firm or government itself would be suitable to operate the road. A model is presented for decision-making through balancing interests between the government and the private firm with an introduction of an intermediate variable, i.e., bidding price. Three scenarios are investigated in the model, including the optimization of government operation, the optimization of private firm operation, and government operation with an improper decision of the intermediate variable. Improper intermediate variable will result in a higher toll charged by the government than by a private firm. The method of avoiding an improper decision is investigated. The result shows that the intermediate variable should be determined to be the government operation cost, based on which the private operator could be chosen, if available. With consideration of the private operator’s profit to be guaranteed by the government, the maximum subsidy should be equal to the minimum private operator’s profit to be disclosed when the contract is signed

Numerical Simulation of Non-Stationary Parameter Creep Large Deformation Mechanism of Deep Soft Rock Tunnel

The accelerated creep plays an important role in the disasters of soft-rock tunnels under high stress. However, most of previous studies only involved attenuation creep and uniform creep. Large deformation disasters of soft rock occurred during the tunneling process in the Qianzhou&ndash;Sanyangchuan Tunnel, Gusu Province, China. In the paper, we developed the nonlinear generalized Nishihara rheological model with non-stationary parameter creep (NGNRM) to simulate the accelerated creep behaviors of soft rocks under high stress, and implemented it in ABAQUS, to reveal the mechanism of large deformation of soft rock. We proposed the multi-objective back analysis method of surrounding rock mechanical parameters based on the eXtreme Gradient Boosting and the non-dominated sorting genetic algorithm-II. In addition, the orthogonal test design method was used to determine the main parameters affecting the displacement of the tunnel. Using the proposed method, we can evaluate the large deformation mechanism of deep soft rock tunnels, and scientifically determine when to reinforce to prevent a large deformation disaster of the tunnel

Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients

Synthesis of novel erdite nanorods for the activation of peroxymonosulfate during p-nitrophenol wastewater treatment

Fe-bearing salt and minerals are common reagents used in activating peroxymonosulfate (PMS) for Fenton-like oxidation in wastewater treatment. Fe-bearing reagents are used in mass production, which generate abundant Fe-bearing waste sludge in the absence of a reductant for Fe /Fe cycling. Herein, a novel Fe/S-bearing mineral, erdite, was synthesized with a one-step hydrothermal route. The material exerted an Fe/S synergetic effect for p-nitrophenol degradation upon PMS activation and showed a one-dimensional structure similar to that of (FeS ) . It contained short rods with diameters of 100 nm and lengths ranging from 200 to 400 nm. It grew radically to 0.8–2 μm in length upon the addition of MnO . Ps-0.5, prepared by adding MnO in an Mn/Fe molar ratio of 0.5, showed optimal efficiency in removing approximately 99.4% of p-nitrophenol upon PMS activation. Only 3.3% of p-nitrophenol was removed without MnO . The efficiency of p-nitrophenol removal through Ps-0.5 activation was higher than that through FeSO , nanoscale zero-valent iron (nZVI), CuFeS , and MnSO activation. The formed erdite rods were spontaneously hydrolyzed to Fe/S-bearing flocs, in which an electron was used by structural S to reduce Fe to Fe upon PMS activation. The reduction resulted in a high p-nitrophenol removal rate. This study provided new insight into the development of an effective PMS activator in wastewater treatment