The Impact of Quantum Computing on Present Cryptography

The aim of this paper is to elucidate the implications of quantum computing in present cryptography and to introduce the reader to basic post-quantum algorithms. In particular the reader can delve into the following subjects: present cryptographic schemes (symmetric and asymmetric), differences between quantum and classical computing, challenges in quantum computing, quantum algorithms (Shor's and Grover's), public key encryption schemes affected, symmetric schemes affected, the impact on hash functions, and post quantum cryptography. Specifically, the section of Post-Quantum Cryptography deals with different quantum key distribution methods and mathematicalbased solutions, such as the BB84 protocol, lattice-based cryptography, multivariate-based cryptography, hash-based signatures and code-based cryptography.Comment: 10 pages, 1 figure, 3 tables, journal article - IJACS

Large Scale Spectral Clustering Using Approximate Commute Time Embedding

Spectral clustering is a novel clustering method which can detect complex shapes of data clusters. However, it requires the eigen decomposition of the graph Laplacian matrix, which is proportion to $O(n^3)$ and thus is not suitable for large scale systems. Recently, many methods have been proposed to accelerate the computational time of spectral clustering. These approximate methods usually involve sampling techniques by which a lot information of the original data may be lost. In this work, we propose a fast and accurate spectral clustering approach using an approximate commute time embedding, which is similar to the spectral embedding. The method does not require using any sampling technique and computing any eigenvector at all. Instead it uses random projection and a linear time solver to find the approximate embedding. The experiments in several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods

Word Sense Disambiguation for Exploiting Hierarchical Thesauri in Text Classification

The introduction of hierarchical thesauri (HT) that contain significant semantic information, has led researchers to investigate their potential for improving performance of the text classification task, extending the traditional “bag of words” representation, incorporating syntactic and semantic relationships among words. In this paper we address this problem by proposing a Word Sense Disambiguation (WSD) approach based on the intuition that word proximity in the document implies proximity also in the HT graph. We argue that the high precision exhibited by our WSD algorithm in various humanly-disambiguated benchmark datasets, is appropriate for the classification task. Moreover, we define a semantic kernel, based on the general concept of GVSM kernels, that captures the semantic relations contained in the hierarchical thesaurus. Finally, we conduct experiments using various corpora achieving a systematic improvement in classification accuracy using the SVM algorithm, especially when the training set is small

Testing the effectiveness of unconventional monetary policy in Japan and the United States

Unconventional monetary policy (UMP) may make the effective lower bound (ELB) on the short-term interest rate irrelevant. We develop a theoretical model that underpins our empirical test of this ‘irrelevance hypothesis,’ based on the simple idea that under the hypothesis, the short rate can be excluded in any empirical model that accounts for alternative measures of monetary policy. We test the hypothesis for Japan and the United States using a structural vector autoregressive model with the ELB. We firmly reject the hypothesis but find that UMP has had strong delayed effects

The Cancer Hub Approach for Upper Gastrointestinal Surgery During COVID-19 Pandemic: Outcomes from a UK Cancer Centre.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused unprecedented disruption to global healthcare delivery. In England, the majority of elective surgery was postponed or cancelled to increase intensive care capacity. Our unit instituted the 'RM Partners Cancer Hub' at the Royal Marsden Hospital in London, to deliver ongoing cancer surgery in a 'COVID-lite' setting. This article describes the operational set-up and outcomes for upper gastrointestinal (UGI) cancer resections performed during this period. METHODS: From April 2020 to April 2021, the Royal Marsden Hospital formed the RM Partners Cancer Hub. This approach was designed to coordinate resources and provide as much oncological treatment as feasible for patients across the RM Partners West London Cancer Alliance. A UGI surgical case prioritisation strategy, along with strict infection control pathways and pre-operative screening protocols, was adopted. RESULTS: A total of 231 patients underwent surgery for confirmed or suspected UGI cancer during the RM Partners Cancer Hub, with 213 completed resections and combined 90-day mortality rate of 3.5%. Good short-term survival outcomes were demonstrated with 2-year disease free survival (DFS) and overall survival (OS) for oesophageal (70.8% and 72.9%), gastric (66.7% and 83.3%) and pancreatic cancer resections (68.0% and 88.0%). One patient who developed perioperative COVID-19 during the RM Partners Cancer Hub operation made a full recovery with no lasting clinical sequelae. CONCLUSION: Our experience demonstrates that the RM Partners Cancer Hub approach is a safe strategy for continuing upper gastrointestinal (GI) resectional surgery during future periods of healthcare service disruption

A Knowledge-Based Semantic Kernel for Text Classification

Abstract. Typically, in textual document classification the documents are represented in the vector space using the “Bag of Words ” (BOW) approach. Despite its ease of use, BOW representation cannot handle word synonymy and polysemy problems and does not consider semantic relatedness between words. In this paper, we overcome the shortages of the BOW approach by embedding a known WordNet-based semantic relatedness measure for pairs of words, namely Omiotis, into a seman-tic kernel. The suggested measure incorporates the TF-IDF weighting scheme, thus creating a semantic kernel which combines both seman-tic and statistical information from text. Empirical evaluation with real data sets demonstrates that our approach successfully achieves improved classification accuracy with respect to the standard BOW representation, when Omiotis is embedded in four different classifiers

Does an extensive diagnostic workup for upfront resectable pancreatic cancer result in a delay which affects survival? Results from an international multicentre study

Backgrounds/Aims: Pancreatoduodenectomy (PD) is recommended in fit patients with a carcinoma (PDAC) of the pancreatic head, and a delayed resection may affect survival. This study aimed to correlate the time from staging to PD with long-term survival, and study the impact of preoperative investigations (if any) on the timing of surgery. // Methods: Data were extracted from the Recurrence After Whipple’s (RAW) study, a multicentre retrospective study of PD outcomes. Only PDAC patients who underwent an upfront resection were included. Patients who received neoadjuvant chemo-/radiotherapy were excluded. Group A (PD within 28 days of most recent preoperative computed tomography [CT]) was compared to group B (> 28 days). // Results: A total of 595 patents were included. Compared to group A (median CT-PD time: 12.5 days, interquartile range: 6–21), group B (49 days, 39–64.5) had similar one-year survival (73% vs. 75%, p = 0.6), five-year survival (23% vs. 21%, p = 0.6) and median time-todeath (17 vs. 18 months, p = 0.8). Staging laparoscopy (43 vs. 29.5 days, p = 0.009) and preoperative biliary stenting (39 vs. 20 days, p 0.99) and endoscopic ultrasonography (28 vs. 32 days, p > 0.99) were not. // Conclusions: Although a treatment delay may give rise to patient anxiety, our findings would suggest this does not correlate with worse survival. A delay may be necessary to obtain further information and minimize the number of PD patients diagnosed with early disease recurrence