Library Search UX report summer 2016

During Summer 2016, Imperial College London's Library Information Systems team ran a round of user experience research into the information-seeking behaviour of undergraduate and postgraduate students with a specific focus on the use of the library catalogue and discovery interface. The purpose of the work was to understand user behaviours and preferences to target development of practical improvements to the Library Search interface

QCSP on reflexive tournaments

We give a complexity dichotomy for the Quantified Constraint Satisfaction Problem QCSP(H) when H is a reflexive tournament. It is well known that reflexive tournaments can be split into a sequence of strongly connected components H1,…,Hn so that there exists an edge from every vertex of Hi to every vertex of Hj if and only if

A combinatorial approach to knot recognition

This is a report on our ongoing research on a combinatorial approach to knot recognition, using coloring of knots by certain algebraic objects called quandles. The aim of the paper is to summarize the mathematical theory of knot coloring in a compact, accessible manner, and to show how to use it for computational purposes. In particular, we address how to determine colorability of a knot, and propose to use SAT solving to search for colorings. The computational complexity of the problem, both in theory and in our implementation, is discussed. In the last part, we explain how coloring can be utilized in knot recognition

The STAR MAPS-based PiXeL detector

The PiXeL detector (PXL) for the Heavy Flavor Tracker (HFT) of the STAR experiment at RHIC is the first application of the state-of-the-art thin Monolithic Active Pixel Sensors (MAPS) technology in a collider environment. Custom built pixel sensors, their readout electronics and the detector mechanical structure are described in detail. Selected detector design aspects and production steps are presented. The detector operations during the three years of data taking (2014-2016) and the overall performance exceeding the design specifications are discussed in the conclusive sections of this paper

Treatment Options for Paediatric Anaplastic Large Cell Lymphoma (ALCL): Current Standard and beyond.

Anaplastic Lymphoma Kinase (ALK)-positive Anaplastic Large Cell Lymphoma (ALCL), remains one of the most curable cancers in the paediatric setting; multi-agent chemotherapy cures approximately 65-90% of patients. Over the last two decades, major efforts have focused on improving the survival rate by intensification of combination chemotherapy regimens and employing stem cell transplantation for chemotherapy-resistant patients. More recently, several new and 'renewed' agents have offered the opportunity for a change in the paradigm for the management of both chemo-sensitive and chemo-resistant forms of ALCL. The development of ALK inhibitors following the identification of the EML4-ALK fusion gene in Non-Small Cell Lung Cancer (NSCLC) has opened new possibilities for ALK-positive ALCL. The uniform expression of CD30 on the cell surface of ALCL has given the opportunity for anti-CD30 antibody therapy. The re-evaluation of vinblastine, which has shown remarkable activity as a single agent even in the face of relapsed disease, has led to the consideration of a revised approach to frontline therapy. The advent of immune therapies such as checkpoint inhibition has provided another option for the treatment of ALCL. In fact, the number of potential new agents now presents a real challenge to the clinical community that must prioritise those thought to offer the most promise for the future. In this review, we will focus on the current status of paediatric ALCL therapy, explore how new and 'renewed' agents are re-shaping the therapeutic landscape for ALCL, and identify the strategies being employed in the next generation of clinical trials

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Effect of Tuned Parameters on a LSA MCQ Answering Model

This paper presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of Latent Semantic Analysis (LSA). A difficult task, which consists in answering (French) biology Multiple Choice Questions, is used to test the semantic properties of the truncated singular space and to study the relative influence of main parameters. A dedicated software has been designed to fine tune the LSA semantic space for the Multiple Choice Questions task. With optimal parameters, the performances of our simple model are quite surprisingly equal or superior to those of 7th and 8th grades students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of training data sets. Besides, we present an original entropy global weighting of answers' terms of each question of the Multiple Choice Questions which was necessary to achieve the model's success.Comment: 9 page