Systemic Decision Making for Liquidity Risk Management in Banks

The outbreak of the recent financial crisis reveals significant problems in current bank practices in conventional liquidity risk management. To avoid catastrophic consequences, a holistic view, which captures the dynamic interactions between liquidity and other financial variables, should be taken to help banks make business decisions. However, few studies in the literature have addressed this problem. To fill the research gap, we present a Systemic decision making approach for Liquidity Risk Management (SLRM) as a more advanced alternative to Conventional Liquidity Risk Management (CLRM) by capturing dynamic factors, offering logic visibility, and considering rare but fatal events. We show that SLRM can be used to support managerial decisions in developing contingency plans for liquidity management. SLRM is validated by using real data from Washington Mutual, a US bank failed during the 2008 financial tsunami. Further, we demonstrate that SLRM can also help banks conform to regulatory changes in Basel III

MSDRP: A Deep Learning Model Based on Multisource Data for Predicting Drug Response

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines. Results: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model

Les collocations du champ sémantique des émotions en mandarin

Les collocations sont un phénomène récurrent à travers les langues. Il s’agit d’associations arbitraires de mots où l’un est choisi librement et l’autre est choisi en fonction du premier. En raison justement de leur aspect arbitraire, des collocations qui expriment un même sens peuvent être très différentes d’une langue à l’autre. La lexicologie explicative et combinatoire [=LEC] (Mel’čuk, Clas et Polguère, 1995) utilise un outil formel, les fonctions lexicales, pour décrire les collocations dans plusieurs langues. Cependant, il n’existe pas encore de dictionnaire en mandarin qui décrive systématiquement les collocations au moyen des fonctions lexicales. Ce mémoire présente un modèle de dictionnaire des collocations en mandarin dans le cadre de la lexicologie explicative et combinatoire. À partir d’entrées du champ des émotions choisies dans le DiCoLiLex en français, nous établissons une nomenclature correspondante en mandarin et nous extrayons d’un corpus, à l’aide de diverses méthodes statistiques, les cooccurrents des lexies à l’étude afin de trouver les collocations. Ensuite, nous adaptons la microstructure du dictionnaire aux caractéristiques du mandarin afin de faciliter la description lexicologique des lexies dans la nomenclature chinoise. Puis, nous discutons des cas difficiles rencontrés lors de la rédaction des articles. Nous montrons que certains phénomènes ne cadrent pas bien dans la théorie et proposons des pistes de solutions.Collocations are recurrent across languages. They are arbitrary associations of words where one is freely chosen, while the other is chosen as a function of the first. Because of their arbitrary aspect, collocations that express the same meaning can vary greatly from one language to another. Explanatory and combinatorial lexicology [=ECL] (Mel’čuk et al., 1995) uses a formal tool, lexical functions, to describe collocations in several languages. However, there is still no dictionary of Mandarin that describes collocations systematically by means of lexical functions. This dissertation presents a model of a collocation dictionary of Mandarin within the framework of explanatory and combinatorial lexicology. From entries in the field of emotions picked from DiCoLiLex in French, we establish a corresponding nomenclature in Mandarin and extract from a corpus the cooccurrents of the lexical units under study to find collocations, using various statistical methods. Then, we adapt the microstructure of the dictionary to the characteristics of Mandarin in order to facilitate the lexicological description of the lexical units in the Chinese nomenclature. Finally, we discuss difficult cases encountered during the process and show that some phenomena do not fit well in the theory and propose possible solutions

A Conditional Variational Framework for Dialog Generation

Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.Comment: Accepted by ACL201

Quantum Circuit Implementation and Resource Analysis of LBlock and LiCi

Due to Grover's algorithm, any exhaustive search attack of block ciphers can achieve a quadratic speed-up. To implement Grover,s exhaustive search and accurately estimate the required resources, one needs to implement the target ciphers as quantum circuits. Recently, there has been increasing interest in quantum circuits implementing lightweight ciphers. In this paper we present the quantum implementations and resource estimates of the lightweight ciphers LBlock and LiCi. We optimize the quantum circuit implementations in the number of gates, required qubits and the circuit depth, and simulate the quantum circuits on ProjectQ. Furthermore, based on the quantum implementations, we analyze the resources required for exhaustive key search attacks of LBlock and LiCi with Grover's algorithm. Finally, we compare the resources for implementing LBlock and LiCi with those of other lightweight ciphers.Comment: 29 pages,21 figure