Comment on "Research on values of GDF-15 level in the diagnosis of primary liver cancer and evaluation of chemotherapeutic effect"

Comment on research on values of GDF-15 level in the diagnosis of primary liver cancer and evaluation of chemotherapeutic effec

Siamese coding network and pair similarity prediction for near-duplicate image detection

Near-duplicate detection in a dataset involves finding the elements that are closest to a new query element according to a given similarity function and proximity threshold. The brute force approach is very computationally intensive as it evaluates the similarity between the queried item and all items in the dataset. The potential application domain is an image sharing website that checks for plagiarism or piracy every time a new image is uploaded. Among the various approaches, near-duplicate detection was effectively addressed by SimPair LSH (Fisichella et al., in Decker, Lhotská, Link, Spies, Wagner (eds) Database and expert systems applications, Springer, 2014). As the name suggests, SimPair LSH uses locality sensitive hashing (LSH) and computes and stores in advance a small set of near-duplicate pairs present in the dataset and uses them to reduce the candidate set returned for a given query using the Triangle inequality. We develop an algorithm that predicts how the candidate set will be reduced. We also develop a new efficient method for near-duplicate image detection using a deep Siamese coding neural network that is able to extract effective features from images useful for building LSH indices. Extensive experiments on two benchmark datasets confirm the effectiveness of our deep Siamese coding network and prediction algorithm

Fusion cross-section in the 4,6He + 64Zn collisions around the Coulomb barrier

The halo structure is expected to influence the reaction mechanisms in nuclear collisions. The aim of the experiment discussed in the present paper is to compare the fusion excitation functions for the systems 6He + 64Zn and 4He + 64Zn, in order to investigate the effects of the 6He two-neutron halo structure on the fusion reaction mechanism at energies around the Coulomb barrier. In particular, new fusion cross-sections for the 4He + 64Zn systems at sub-barrier energies were measured to cover the same energy region of our previous measurements for 6He + 64Zn. The fusion cross-section was measured by using an activation technique. We have observed an enhancement of the fusion cross-section in the reaction induced by 6He when compared to the one induced by 4He on the same 64Zn target

Event Detection in Wikipedia Edit History Improved by Documents Web Based Automatic Assessment

A majority of current work in events extraction assumes the static nature of relationships in constant expertise knowledge bases. However, in collaborative environments, such as Wikipedia, information and systems are extraordinarily dynamic over time. In this work, we introduce a new approach for extracting complex structures of events from Wikipedia. We advocate a new model to represent events by engaging more than one entities that are generalizable to an arbitrary language. The evolution of an event is captured successfully primarily based on analyzing the user edits records in Wikipedia. Our work presents a basis for a singular class of evolution-aware entity-primarily based enrichment algorithms and will extensively increase the quality of entity accessibility and temporal retrieval for Wikipedia. We formalize this problem case and conduct comprehensive experiments on a real dataset of 1.8 million Wikipedia articles in order to show the effectiveness of our proposed answer. Furthermore, we suggest a new event validation automatic method relying on a supervised model to predict the presence of events in a non-annotated corpus. As the extra document source for event validation, we chose the Web due to its ease of accessibility and wide event coverage. Our outcomes display that we are capable of acquiring 70% precision evaluated on a manually annotated corpus. Ultimately, we conduct a comparison of our strategy versus the Current Event Portal of Wikipedia and discover that our proposed WikipEvent along with the usage of Co-References technique may be utilized to provide new and more data on events

Breakup and n -transfer effects on the fusion reactions Li 6,7 + Sn 120,119 around the Coulomb barrier

This paper presents values of complete fusion cross sections deduced from activation measurements for the reactions Li6+Sn120 and Li7+Sn119, and for a projectile energy range from 17.5 to 28 MeV in the center-of-mass system. A new deconvolution analysis technique is used to link the basic activation data to the actual fusion excitation function. The complete fusion cross sections above the barrier are suppressed by about 70% and 85% with respect to the universal fusion function, used as a standard reference, in the Li6 and Li7 induced reactions, respectively. From a comparison of the excitation functions of the two systems at energies below the barrier, no significant differences can be observed, despite the two systems have different n-transfer Q values. This observation is supported by the results of coupled reaction channels (CRC) calculations

Fair-CMNB: Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization

Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, etc. This calls for handling massive amounts of incoming information with minimal response delay while ensuring fair and high-quality decisions. Although deep learning has achieved success in various domains, its computational complexity may hinder real-time processing, making traditional algorithms more suitable. In this context, we propose a novel adaptation of Naïve Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance through multi-objective optimization (MOO). Class imbalance is an inherent problem in discrimination-aware learning paradigms. To deal with class imbalance, we propose a dynamic instance weighting module that gives more importance to new instances and less importance to obsolete instances based on their membership in a minority or majority class. We have conducted experiments on a range of streaming and static datasets and concluded that our proposed methodology outperforms existing state-of-the-art (SoTA) fairness-aware methods in terms of both discrimination score and balanced accuracy

Can Deep Learning Improve Technical Analysis of Forex Data to Predict Future Price Movements?

The foreign exchange market (Forex) is the world's largest market for trading foreign money, with a trading volume of over 5.1 trillion dollars per day. It is known to be very complicated and volatile. Technical analysis is the observation of past market movements with the aim of predicting future prices and dealing with the effects of market movements. A trading system is based on technical indicators derived from technical analysis. In our work, a complete trading system with a combination of trading rules on Forex time series data is developed and made available to the scientific community. The system is implemented in two phases: In the first phase, each trading rule, both the AI-based rule and the trading rules from the technical indicators, is tested for selection; in the second phase, profitable rules are selected among the qualified rules and combined. Training data is used in the training phase of the trading system. The proposed trading system was extensively trained and tested on historical data from 2010 to 2021. To determine the effectiveness of the proposed method, we also conducted experiments with datasets and methodologies used in recent work by Hernandez-Aguila et al., 2021 and by Munkhdalai et al., 2019. Our method outperforms all other methodologies for almost all Forex markets, with an average percentage gain of 20.2%. A particular focus was on training our AI-based rule with two different architectures: the first is a widely used convolutional network for image classification, i.e. ResNet50; the second is an attention-based network Vision Transformer (ViT). The results provide a clear answer to the main question that guided our research and which is the title of this paper

Sub-barrier radioactive ion beam investigations using a new methodology and analysis for the stacked target technique

For low energy reaction studies involving radioactive ion beams, the experimental reaction yields are generally small due to the low intensity of the beams. For this reason, the stacked target technique has been often used to measure excitation functions. This technique offers considerable advantages since the reaction cross-section at several energies can be simultaneously measured. In a further effort to increase yields, thick targets are also employed. The main disadvantage of the method is the degradation of the beam quality as it passes through the stack due to the statistical nature of energy loss processes and any nonuniformity of the stacked targets. This degradation can lead to ambiguities of associating effective beam energies to reaction product yields for the targets within the stack and, as a consequence, to an error in the determination of the excitation function for the reaction under study. A thorough investigation of these ambiguities is reported, and a best practice procedure of analyzing data obtained using the stacked target technique with radioactive ion beams is recommended. Using this procedure a re-evaluation is reported of some previously published sub-barrier fusion data in order to demonstrate the possibility of misinterpretations of derived excitation functions. In addition, this best practice procedure has been used to evaluate, from a new data set, the sub-barrier fusion excitation function for the reaction Li6+Sn120

Miotomia laparoscopica secondo Heller per acalasia esofagea. C’è bisogno di una fundoplicatio?

The last decade has witnessed radical changes in the treatment of esophageal achalasia due to the development of minimally invasive techniques. Because of the high success rate of the laparoscopic Heller myotomy, a radical shift in the treatment algorithm of these patients has occurred, and today this is the preferred treatment modality for achalasia. This remarkable change is due to the recognition by gastroenterologists and patients that a laparoscopic Heller myotomy outperforms pneumatic dilatation and intra-sphincteric injection of botulinum toxin injection. While there is agreement about the technique of the myotomy per se, some questions still linger about the need for a fundoplication after the myotomy. The following review describes the data present in the literature in order to identify the best procedure that can achieve relief of dysphagia while avoiding development of gastroesophageal reflux