Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of outpatient commitment on victimization of people with severe mental illness.

OBJECTIVE The authors' goal was to evaluate the effectiveness of outpatient commitment in reducing victimization among people with severe mental illness. METHOD One hundred eighty-four involuntarily hospitalized patients were randomly assigned to be released (N=99) or to continue under outpatient commitment (N=85) after hospital discharge. An additional group of patients with a recent history of serious violent behavior (N=39) was nonrandomly assigned to at least a brief period of outpatient commitment following hospital disharge. All three groups were followed for 1 year, and case management services plus additional outpatient treatment were provided to all subjects. Outcome data were based on interviews with the patients and informants as well as service records. RESULTS Subjects who were ordered to outpatient commitment were less likely to be criminally victimized than those who were released without outpatient commitment. Multivariate analysis indicated that each additional day of outpatient commitment reduced the risk of criminal victimization and that outpatient commitment had its effect through improved medication adherence, reduced substance use or abuse, and fewer violent incidents. CONCLUSIONS Protection from criminal victimization appears to be a positive, unintended consequence of outpatient commitment.
Implementing PARMACS Macros for Shared Memory Multiprocessor Environments

In order to evaluate the goodness of parallel systems, it is necessary to know how parallel programs behave. The SPLASH-2 applications provide us a realistic workload for such systems. So, we have made different implementations of the PARMACS macros used by SPLASH-2 applications, based on several execution and synchronization models, from classical Unix processes to multithreaded systems. Results have been tested in two different multiprocessor systems (Digital and Silicon Graphics). As parallel constructs in the SPLASH-2 applications are limited to those provided by PARMACS, we can easily study the overhead introduced by synchronization and parallelism management.
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity. An RNN is first pre-trained on data using maximum likelihood estimation (MLE), and the probability distribution over the next token in the sequence learned by this model is treated as a prior policy. Another RNN is then trained using reinforcement learning (RL) to generate higher-quality outputs that account for domain-specific incentives while retaining proximity to the prior policy of the MLE RNN. To formalize this objective, we derive novel off-policy RL methods for RNNs from KL-control. The effectiveness of the approach is demonstrated on two applications; 1) generating novel musical melodies, and 2) computational molecular generation. For both problems, we show that the proposed method improves the desired properties and structure of the generated sequences, while maintaining information learned from data.
Online Timing Analysis for Wearout Detection

CMOS feature size scaling has long been the source of dramatic performance gains. However, because voltage levels have not scaled in step, feature size scaling has come at the cost of increased operating temperatures and current densi ties. Further, since most common wearout mechanisms are highly dependent upon both temperature and current density, relia bility issues, and in particular microprocessor lifetime, have come into question. In this work, we explore the effects of wearout upon a fully synthesized, placed and routed implementation of an embedded microprocessor core and present a generic wearout detection unit. Since most common failure mechanisms may be characterized by a period of increased latency through ailing transistors and interconnects before breakdown, this wearout detection unit serves to predict imminen t failure by conducting online timing analysis. In addition t o measuring signal propagation latency, it also includes a un ique two-level sampling unit which is used to smooth out timing anomalies that may be caused by phenomenon such as temperature spikes, electrical noise, and clock jitter.
Anchoring of Azospirillum brasilense to hydrophobic polystyrene and wheat roots

Summary: The anchoring (irreversible attachment) of Azospirillium brasilense Cd to hydrophobic polystyrene and to root surfaces was compared. Live A. brasilense Cd cells attached in significantly greater numbers to roots than to polystyrene, regardless of treatments made to the surfaces or to the bacterial cells. Triton X-100, Na2EDTA and several bacterial-inhibitory substances reduced bacterial attachment to both surfaces, although this effect was greater with attachment to polystyrene than to roots. Pre-coating with root exudates, bovine serum albumin or gelatin significantly increased anchoring to both surfaces. Manganese-limited cells showed increased anchoring to roots, whereas dead cells adsorbed better to polystyrene. Although the anchoring of A. brasilense Cd to a non-biological surface can be significantly altered by using several promoting or inhibiting substances to affect the properties of both the surface and the bacterial cell, anchoring to root surfaces is less affected by these substances. It is proposed that at least two different quantitative types of anchoring exist in this bacterium: a sparse attachment to a non-biological surface and a prolific attachment to roots.
CSRS: A Context and Sequence Aware Recommendation System

The effectiveness of recommendation systems is improving with the incorporation of richer context. The frequentist recommendation methods such as Markov models are not efficient in simultaneous use of context and preference sequences over items due to state space explosion. On the other end, abstractionist models such as Matrix Factorization where each item or user is represented as a set of abstract features are difficult to explain. An example in this case is, recommending Web Based Trainings (WBTs) to employees, similar to Massively Open Online Courses (MOOCs), wherein use of the sequence information in the recommendation of WBTs would help the user to gradually build expertise in their area of interest. For training recommendation, it is important to identify the held expertise level in technical area, that represents a state and possible sequences of trainings that represent transitions in terms of real world entities such as trainings and associated features. Alternatively the model can estimate expertise as a mixture over a tractable set of latent interests in terms of trainings completed, contextual features such as the training sequences, keywords and user profile. To the best of our knowledge, the state-of-the-art recommendation methods do not consider both explicit context and sequence information in a single model. In this paper, we propose a Context and Sequence Aware Recommendation System (CSRS) based on latent topic modelling framework, identifying topic-memberships for items, contextual features as well as for user interests. We demonstrate benefits of incorporating both context and sequence of items for recommendation on three real world datasets.
Forecasting in port logistics and economics using Time Series Data Mining model

: This paper addresses the question of how to develop forecasting models resulting from business processes that can be embodied in an intelligent decision support system. Moreover the design is suitable for evolving logistics and economic situations in which ports plan or foresee to have an improved economic role. The work presented in this paper also forms one component of a conceptual intelligent decision-making support system ( i -DMSS) for port integration. The key objective of this work is to offer a model-based approach to Time Series Data Mining (TSDM) based on the assumptions that the time series may be produced by an underlying model, and that its flexibility is suitable to perform multivariate time-series analysis encompassing the notion of model selection and statistical learning known as the core of forecasting systems. The interrelated activities to induce domain knowledge are specified as the data collection principles, the descriptive modelling and normative modelling. Results indicate that for the period 2001 to 2005, the commodity throughput of coffee (tons) handled in the port of Buenaventura gains importance in the prediction of the Colombian national exports of coffee, thus indicating that the port operation was able to affect the economy in this regard. The previous period was strongly affected by outliers, creating a random walk process difficult to fit but feasible to produce due to unstable conditions evidenced in the economy.
Problematising the theory–practice terminology: a discourse analysis of students’ statements on Work-integrated Learning

ABSTRACT This study uses a Foucault-inspired discourse analysis to examine two ideas about learning which reinforce the terminology whereby theory means campus-based training and practice means work placements. The purpose is to problematise this theory–practice terminology and provide scope for a non-dualistic alternative. The ideas examined are the idea of theory vs. practice as the point of departure for learning and the idea of theory and practice as harmonious points of departure for learning. These ideas were voiced by interviewed students who discussed the usual design of Work-integrated Learning (WIL) whereby students go to university to learn ‘theory’ and into working life to learn ‘practice’. The analysis shows how the ideas are formed by different ranking orders between theory and practice which are mutually exclusive, while also working together to reinforce the theory–practice terminology. The discussion on how a non-dualistic terminology can emerge highlights how the usual WIL design forms a dualistic setting where the theory–practice terminology thrives and how designing WIL at a third place between university and working life can provide scope for the terminology we seek.
Dialogue Act Recognition using Cue Phrases

Dialogue acts play an important role in modelling discourse phenomena in several components of modern dialogue systems. Many different features have been so far proposed for dialogue act recognition. In this report, we take a cue-based model approach, and use N grams in utterances in dialogue as cue phrases. In our experiment with the switchboard corpus, we obtained 57.1% classification accuracy. We show that our approach is a useful technique to help us detect promising cue phrases for dialogue act recognition.
Towards Robust Routing in HD Tree

The Hierarchically Distributed Tree (HD Tree) is a new data structure within distributed environment. It is designed to better preserve data locality when the number of nodes in a tree grows exponentially. By adding extra links to a corresponding complete tree, multiple routing paths are available between each pair of tree nodes. Therefore the routing strategy for HD Tree could be designed to be highly flexible and error resilient. The routing traffic can be controlled dynamically, making the traffic load balanced among all the nodes. However, within an HD Tree, there are some nodes, which we name SPeers, that are different from the others. They have less connected neighbours. What's more, every SPeer has only one parent and in the case of parent node failure, a leaf SPeer node could be isolated from the system. So a new method by which to connect all the SPeers is needed. In this paper, a new routing strategy is proposed, in order to make the routing between SPeers more error resilient. This strategy could be used as an alternative approach to previously developed algorithms. In addition, it is proven that when combined with other strategies, it will achieve better routing performance.
On the second-order statistics of a dual-hop Weibull fading channel

The average level crossing rates (LCR) and average fade durations (AFD) are useful second-order statistics of a fading channel. In this paper, the LCR and AFD are derived for a dual-hop Weibull fading channel with an amplify-and-forward (AF) relay and a fixed relay gain. Numerical results of the average LCR and AFD are examined for different channel parameters.
Un modèle de représentation des connaissances relatives au contenu dans un système tutoriel intelligent

Nous presentons dans cet article une approche, nommee CREAM (Curriculum REpresentation and Acquisition Model), destinee a la modelisation des connaissances d'une matiere a enseigner dans un STI. Cette approche permet d'organiser la matiere sous trois perspectives : domaine, pedagogie et didactique. L'aspect relie au domaine est mis en œuvre, dans cette approche, par un modele de capacites (CREAM-C ) qui represente et organise les connaissances du domaine suivant des liens logiques existant entre elles ; ces connaissances sont categorisees selon la taxinomie de Gagne. Le volet pedagogique cree un cadre de definition et d'organisation des objectifs d'enseignement (CREAM-0 et CKTN). L'organisation des objectifs consiste notamment a modeliser les predispositions necessaires a leur realisation et a etudier l'impact que peut avoir l'atteinte d'un objectif sur les connaissances du domaine. La dimension didactique revient a produire un modele qui definit et organise les differents moyens tactiques necessaires pour l'enseignement de la matiere consideree (CREAM -R).
Gender Differences of Brain Glucose Metabolic Networks Revealed by FDG-PET: Evidence from a Large Cohort of 400 Young Adults

Background Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. Materials and Methods FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25∼45 years, mean age±SD: 40.9±3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Results Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. Conclusion This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients.
Reflections on the ethical dilemmas involved in promoting self-management

Due to their understanding of self-management, healthcare team members responsible for depressed older persons can experience an ethical dilemma. Each team member contributes important knowledge and experience pertaining to the management of depression, which should be reflected in the management plan. The aim of this study was to explore healthcare team members’ reflections on the ethical dilemmas involved in promoting self-management among depressed older persons. A qualitative design was used and data were collected by means of focus group interviews. The results revealed one main theme: ‘Lack of trust in the community health care system’s commitment to bringing about effectiveness and change, based on three themes; ‘Struggling to ensure the reliable transfer of information about depressed older persons to professionals and family members’, ‘Balancing autonomy, care and dignity’ and ‘Differences in the understanding of responsibility’. Lack of engagement on the part of and trust between the various professional categories who work in the community are extremely counterproductive and have serious implications for patient dignity as well as safety. In conclusion, ethical dilemmas occur when staff members are unable to act in accordance with their professional ethical stance and deliver an appropriate standard of care.
Pathogenesis of occupational asthma

The development of occupational asthma (OA) is likely to result from the complex interaction of environmental and host factors. This article addresses a series of issues relating to the multiple environmental factors that could affect the initiation of OA, including the intrinsic characteristics of causative agents, as well as the influence of the level, mode and route of exposure. Although the clinical and pathological features of OA caused by low molecular weight agents resemble those of immunoglobulin (Ig)E-mediated asthma, the failure to detect specific IgE antibodies against most of these agents and/or poor association with disease status have resulted in intense speculation about alternative or complementary physiopathological mechanisms leading to airway sensitisation. In this contribution, the roles of specific immunoglobulin E and G antibodies, cell-mediated immunity and inflammatory effector cells are critically reviewed. Recent advances in the characterisation of the molecular interactions between chemical sensitisers and human airway proteins provide promising avenues for elucidating the immunological basis of occupational asthma caused by low molecular weight agents.
Impact of word graph density on the quality of posterior probability based confidence measures

Our new experimental results, presented in this paper, clearly prove the dependence between word graph density and the quality of two different confidence measures. Both confidence measures are based on the computation of the posterior probabilities of the hypothesized words and apply the time alignment information of the word graph for confidence score accumulation. We show that the quality of the confidence scores of both confidence measures significantly increases for higher word graph densities. The analyses were carried out on two different German spontaneous speech corpora: on the Verbmobil evaluation corpus [1] and on the NaDia corpus. We achieved a relative reduction of the confidence error rate by up to 41.4 %, compared to the baseline confidence error rate. The results lead us to propose to perform the confidence score calculation – based on posterior probability accumulation – on higher word graph densities in order to get the best results.
Stigmatized‐Identity Cues in Consumer Spaces

Consumers with stigmatized identities may be especially attentive to organizational cues that signal to them whether or not they are welcomed and valued consumers. These stigmatized-identity cues can become a part of a company’s brand, influencing the attractiveness of the company and consumer loyalty, and allowing consumers to draw inferences about an organization’s ideologies and attitudes toward multiple stigmatized groups. Consumers with stigmatized identities attend to stigmatized-identity cues that signal inclusion (or exclusion) toward other stigmatized groups, resulting in stigmatized-identity cue transfers, especially when similarity mindsets are activated, and among consumers who are high in stigma solidarity. Because identity is central in consumer behavior, stigmatized-identity threat cues can at times result in anti-consumption, including boycotts, allowing consumers and companies to engage in collective action. This article reviews this growing body of research, demonstrating the important influence of stigmatized-identity cues on consumer behavior and discussing ways in which companies can better signal their inclusive attitudes and ideologies to attract consumers.
KQML - A Language and Protocol for Knowledge and Information Exchange

This paper describes the design of and experimentation with the Knowledge Query and Manipulation Language (KQML), a new language and protocol for exchanging information and knowledge. This work is part a larger effort, the ARPA Knowledge Sharing Effort which is aimed at developing techniques and methodology for building large-scale knowledge bases which are sharable and reusable. KQML is both a message format and a message-handling protocol to support run-time knowledge sharing among agents. KQML can be used as a language for an application program to interact with an intelligent system or for two or more intelligent systems to share knowledge in support of cooperative problem solving. KQML focuses on an extensible set of performatives, which defines the permissible operations that agents may attempt on each other’s knowledge and goal stores. The performatives comprise a substrate on which to develop higher-level models of inter-agent interaction such as contract nets and negotiation. In addition, KQML provides a basic architecture for knowledge sharing through a special class of agent called communication facilitators which coordinate the interactions of other agents The ideas which underlie the evolving design of KQML are currently being explored through experimental prototype systems which are being used to support several testbeds in such areas as concurrent engineering, intelligent design and intelligent planning and scheduling.
A User Access Policy Based on Dynamic Sensitivity Label

the analysis of Isandhu method [1] points out that it strengthens role relations and provides the support to the notation of trusted subject by using assistant role hierarchies. Additionally, some errors of it are revealed, such as roles leaping, authority magnifying, hierarchical model confusing, etc. Then, a user access policy base on dynamic sensitivity label is presented, it uses dynamic sensitivity label to mark user's private rights and objects' security level, and analyses the constraints to access, and helps user to access objects with speediness and flexibility. It carries out the mandatory access control with confidentiality and integrity, and provides a theoretical basis for flexible and efficient access control policy in the large enterprise systems.
Relationships Between Moral Injury Syndrome Model Variables in Combat Veterans

Moral injury is a unique type of trauma characterized by guilt and shame that may develop after acting inconsistently with one’s moral values or observing moral violations by trusted individuals. According to the moral injury syndrome model, exposure to potentially morally injurious events (pMIEs—e.g., killing combatants or civilians) statistically predicts the development of proposed moral injury symptoms. Moral injury’s core symptoms (e.g., guilt and loss of meaning) are further hypothesized to mediate relationships between pMIEs and secondary symptoms (e.g., depression, anxiety, and posttraumatic stress disorder symptoms). To empirically evaluate these propositions, the relationships between exposure to pMIEs, core symptoms, and secondary symptoms were examined in a community sample of combat veterans (N = 72). pMIE exposure was statistically associated with all proposed moral injury symptoms. Furthermore, the relationships between pMIE exposure and secondary symptoms were all mediated by core symptoms. Results highlight guilt’s centrality in moral injury.