A One Dimensional Ideal Gas of Spinons, or Some Exact Results on the XXX Spin Chain with Long Range Interaction

We describe a few properties of the XXX spin chain with long range interaction. The plan of these notes is: 1. The Hamiltonian 2. Symmetry of the model 3. The irreducible multiplets 4. The spectrum 5. Wave functions and statistics 6. The spinon description 7. The thermodynamicsComment: Latex. Talk given by the first author at the Cargese-1993 workshop "Strings, conformal models and Topological felds theorie

Psychological profile of laryngectomized patients

Larynx cancer is one of the most susceptible form of cancer susceptible to induce alteration of the patient’s psychological profile due to the social role that the larynx has in communication. Oral communication is severely impaired even after voice rehabilitation of the laryngectomized patients, so that the social rehabilitation is somewhat not only a medical but also a social problem. The psychological profile of these patients is altered in a way that dealing with the disease is sometimes neglected and the interaction with the outside world in terms of oral communication is totally abandoned. The starting point for depression in these cases is the acknowledgement of the disease and is, in some cases, the entire medical environment. Facial scarring, the inability to verbally interact with other human, as well as the presence of the tracheostoma, are all deciding factors in the presence of a low self-esteem for these particular patients. Psychological counseling is a mandatory approach for laryngectomized patients, in order to improve their ability to cope with cancer and providing better recovery chances

End-stage head and neck cancer: coping mechanism

Coping mechanisms are patients’ means of adapting to stressful situations and involve psychological and physical changes in behavior. Patients adapt to head and neck cancer in a variety of ways. Head and neck cancers are extremely debilitating, especially in advanced stages of the disease or in end-of-life situations. While an oncology team needs to address the needs of all oncology patients, the advanced terminal patients require special attention. Most of these patients do not cope well with their situation and have a tendency to cease social interactions. Pain is the most frequentlyexperienced medical disability in patients having an end-stage illness experience, and thus an important medical endeavor is to afford dignity to the dying patient facingan incurable disease. In such cases, the medical community should never refuse therapy or to assist a dying patient.In some instances, the patient and family may derive benefit from their religious beliefs

Piecewise Latent Variables for Neural Variational Text Processing

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.Comment: 19 pages, 2 figures, 8 tables; EMNLP 201

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on Cognitive Systems

Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.Comment: ACL 201