Miracle-dz/UniFAR-FTUs huggingface dataset:
“I Don’t Feel Like I Have a Problem Because I Can Still Go To Work and Function”: Problem Recognition Among Persons With Substance Use Disorders

Abstract Background: Persons with substance use disorders (SUDs) who do not recognize their substance use as problematic are less likely to perceive needing treatment and less motivated to seek help. Factors that contribute to problem recognition among persons with SUDs are poorly understood. Objective: To explore in-depth factors that may explain why those who meet diagnostic criteria for SUDs do not perceive having a substance abuse problem. Methods: We recruited 54 participants with recent (i.e., past-5-year) SUD for qualitative interviews. Participants were recruited via online ads and screened for eligibility through an online survey. Interview questions focused on participants’ alcohol and drug use behaviors, adverse consequences stemming from their substance use, past treatment use experiences, and barriers/reasons for not using specialty treatment. Interviews were thematically coded to identify prominent themes that may explain low problem recognition. Results: We identified two prominent themes that contributed to problem recognition: modifying substance use behaviors to avoid adverse consequences and stigma (i.e., “othering”). Participants who (1) reported adjusting their alcohol and drug use in ways that would not interfere with important life responsibilities, especially work-responsibilities; (2) described those with alcohol and drug problems negatively; and (3) associated treatment with personal defeat were less likely to perceive having a SUD. Conclusions/Importance: These findings can be used to inform intervention strategies aimed at increasing problem recognition among persons with SUDs. Such strategies may facilitate motivation (i.e., desire for help and treatment readiness) to use and complete treatment, thereby reducing the unmet treatment gap among persons with SUDs.
A verified implementation of an applicative language with dynamic storage allocation

A compiler for a subset of the Nqthm logic and a mechanically checked proof of its correctness is described. The Nqthm logic defines an applicative programming language very similar to McCarthy's pure Lisp(20). The compiler compiles programs in the Nqthm logic into the Piton assembly level language (23). The correctness of the compiler is proven by showing that the result of executing the Piton code is the same as produced by the Nqthm interpreter V&C$. The Nqthm logic defines several different abstract data types, or shells, as they are called in Nqthm. The user can also define additional shells. The definition of a shell includes the definition of a constructor function that returns new objects with the type of that shell. These objects can become garbage, so the run-time system of the compiler includes a garbage collector. The proof of the correctness of the compiler has not been entirely mechanically checked. A plan for completing the proof is described.
Advances in the gene therapy of monogenic blood cell diseases

Hematopoietic gene therapy has markedly progressed during the last 15 years both in terms of safety and efficacy. While a number of serious adverse events (SAE) were initially generated as a consequence of genotoxic insertions of gamma‐retroviral vectors in the cell genome, no SAEs and excellent outcomes have been reported in patients infused with autologous hematopoietic stem cells (HSCs) transduced with self‐inactivated lentiviral and gammaretroviral vectors. Advances in the field of HSC gene therapy have extended the number of monogenic diseases that can be treated with these approaches. Nowadays, evidence of clinical efficacy has been shown not only in primary immunodeficiencies, but also in other hematopoietic diseases, including beta‐thalassemia and sickle cell anemia. In addition to the rapid progression of non‐targeted gene therapies in the clinic, new approaches based on gene editing have been developed thanks to the discovery of designed nucleases and improved non‐integrative vectors, which have markedly increased the efficacy and specificity of gene targeting to levels compatible with its clinical application. Based on advances achieved in the field of gene therapy, it can be envisaged that these therapies will soon be part of the therapeutic approaches used to treat life‐threatening diseases of the hematopoietic system.
A new model explaining the origin of different topologies in interaction networks

The architecture of interaction networks has been extensively studied in the past decades, and different topologies have been observed in natural systems. Despite several phenomenological explanations proposed, we still understand little of the mechanisms that generate those topologies. Here we present a mechanistic model based on the integrative hypothesis of specialization, which aims at explaining the emergence of topology and specialization in consumer-resource networks. By following three first-principles and adjusting five parameters, our model was able to generate synthetic weighted networks that show the main patterns of topology and specialization observed in nature. Our results prove that topology emergence is possible without network-level selection. In our simulations, the intensity of trade-offs in the performance of each consumer species on different resource species is the main factor driving network topology. We predict that interaction networks with low species diversity and low dissimilarity between resources should have a nested topology, although more diverse networks with large dissimilarity should have a compound topology. Additionally, our results highlight scale as a key factor. Our model generates predictions consistent with ecological and evolutionary theories and real-world observations. Therefore, it supports the IHS as a useful conceptual framework to study the architecture of interaction networks.
Normative data for sweating rate, sweat sodium concentration, and sweat sodium loss in athletes: An update and analysis by sport

ABSTRACT The purpose of this study was to expand our previously published sweat normative data/analysis (n = 506) to establish sport-specific normative data for whole-body sweating rate (WBSR), sweat [Na+], and rate of sweat Na+ loss (RSSL). Data from 1303 athletes were compiled from observational testing (2000–2017) using a standardized absorbent sweat patch technique to determine local sweat [Na+] and normalized to whole-body sweat [Na+]. WBSR was determined from change in exercise body mass, corrected for food/fluid intake and urine/stool loss. RSSL was the product of sweat [Na+] and WBSR. There were significant differences between sports for WBSR, with highest losses in American football (1.51 ± 0.70 L/h), then endurance (1.28 ± 0.57 L/h), followed by basketball (0.95 ± 0.42 L/h), soccer (0.94 ± 0.38 L/h) and baseball (0.83 ± 0.34 L/h). For RSSL, American football (55.9 ± 36.8 mmol/h) and endurance (51.7 ± 27.8 mmol/h) were greater than soccer (34.6 ± 19.2 mmol/h), basketball (34.5 ± 21.2 mmol/h), and baseball (27.2 ± 14.7 mmol/h). After ANCOVA, significant between-sport differences in adjusted means for WBSR and RSSL remained. In summary, due to the significant sport-specific variation in WBSR and RSSL, American football and endurance have the greatest need for deliberate hydration strategies. Abbreviations: WBSR: whole body sweating rate; SR: sweating rate; Na+: sodium; RSSL: rate of sweat sodium loss
The BoPen : a tangible pointer tracked in six degrees of freedom

In this thesis, I designed and implemented an optical system for freehand interactions in six degrees of freedom. A single camera captures a pen's location and orientation, including roll, tilt, x, y, and z by reading information encoded in a pattern at the infinite focal plane. The pattern design, server-side processing, and application demo software is written in Microsoft C#. NET, while the client-side pattern recognition is integrated into a camera with an on-board DSP and programmed in C. We evaluate a number of prototypes and consider the pen's potential as an input device. Thesis Supervisor: Ramesh Raskar Title: Associate Professor
Zarządzanie wiedzą w organizacjach zhierarchizowanych

An important feature of a modern organization in the hierarchical organization is its formalization. Basic objectives, structures, procedures, technologies, principles of operation are typically stored in the organizational documents. Objectives are presented in the articles of association, organizational structures and operating principles- in the regulations, service books and graphic patterns, and procedures - in the instructions. Formalization defines, perpetuates and makes clear the internal governance organization, but solidifies its functioning and hinders adaptive processes. The process of knowledge management in hierarchical organizations can help to alleviate these shortcomings. This paper presents the importance of knowledge management in hierarchical organizations.
Refactoring UML Diagrams and Models with Model-to-Model Transformations

Software is becoming increasingly important in everyday life and is becoming increasingly complex as well. Techniques have been developed to reduce the complexity, e.g. abstract modelling, model-driven development and refactoring code structure. Refactoring is a systematic approach to restructure code to make it simpler without changing its behaviour. Refactoring has been well investigated and is proven for programming languages but is still a developing concept in model-driven development. The refactoring process has already been applied to UML models in earlier work but the focus has only been on the elements of a UML model without updating the typically associated diagrammatic representation. In this thesis, automated refactorings are developed for restructuring UML activity models together with their diagrammatic representation using the QVT operational transformation language for transforming UML models and diagrams created with the Papyrus UML editor.
Environment based clustering. A new approach

There are a variety of problems that require an automatic classification of a set of data. In this sense, clustering techniques have been widely applied, since they are known for forming classes or groups using a predefined similarity measure. This paper defines a new method which, as opposed to the solutions found so far, does not require any previous information about the data to be classified. The performance of this new proposal has been compared with a winner-take-all type method (WTA), which is widely used in clustering processes, and with the cluster-detection-and-labeling (CDL) method (Torbjorn, 1998), with satisfactory results.
Generalized language identification

Language identification is the task of determining the natural language that a document or part thereof is written in. The central theme of this thesis is generalized language identification, and deals with eliminating the assumptions that limit the applicability of language identification techniques to specific settings that may not be representative of real-world use cases for automatic language identification techniques. Research to date has treated language identification as a supervised machine learning problem, and in this thesis I argue that such a characterization is inadequate, showing how standard document representations do not take into account the variation in a language between different sources of text, and developing a representation that is robust to such variation. I also develop a method that allows for language identification of multilingual documents, i.e. documents that contain text in more than one language. Finally, I investigate the robustness of existing off-the-shelf language identification methods on a novel and challenging domain.
A new anomaly detection method based on hierarchical HMM

The state transition, which is hidden in the hidden Markov model (HMM), can be used to characterize the intrinsic difference between normal action and intrusion behavior. So HMM is an efficient way to detect anomalies. A new anomaly detection method based on a hierarchical HMM is proposed based on the concept of normal database and abnormal database. It is shown by analysis and simulation results that the proposed method is effective to increase the accuracy of anomaly detection.
An alternative proof of convergence for Kung-Diamantaras APEX algorithm

The problem of adaptive principal components extraction (APEX) has gained much interest. In 1990, a new neuro-computation algorithm for this purpose was proposed by S. Y. Kung and K. I. Diamautaras. (see ICASSP 90, p.861-4, vol.2, 1990). An alternative proof is presented to illustrate that the K-D algorithm is in fact richer than has been proved before. The proof shows that the neural network will converge and the principal components can be extracted, without assuming that some of projections of synaptic weight vectors have diminished to zero. In addition, the authors show that the K-D algorithm converges exponentially. <<ETX>>
Children Online: A survey of child language and CMC corpora

The collection of representative corpus samples of both child language and online (CMC) language varieties is crucial for linguistic research that is motivated by applications to the protection of children online. In this paper, we present an extensive survey of corpora available for these two areas. Although a significant amount of research has been undertaken both on child language and on CMC language varieties, a much smaller number of datasets are made available as corpora. Especially lacking are corpora which match requirements for verifiable age and gender metadata, although some include self-reported information, which may be unreliable. Our survey highlights the lack of corpus data available for the intersecting area of child language in CMC environments. This lack of available corpus data is a significant drawback for those wishing to undertake replicable studies of child language and online language varieties.
Analogue Neural Vlsi: A Pulse Stream Approach

From the Publisher: Analogue VLSI is an important medium for the implementation of neural networks and the authors' aim in this book is to explore this technique in detail. This book compares a variety of approaches, giving readers an appreciation of analogue neural VLSI through examples of each approach rather than by means of an exhaustive survey.
Design of radial basis function networks using decision trees

A radial basis function (RBF) neural network is considered as one of the universal input-output functional mapping learning systems. Important issues in designing an efficient RBF neural network are the number of neurons and the shape and location of neurons to define local receptive fields in feature space. This paper presents a solution to these problems using decision tree partitioning. A conversion algorithm from decision tree to RBF neural network is described. Two examples are presented to illustrate the proposed approach.
Automatic time alignment of phonemes using acoustic-phonetic information

One requirement for researching and building spoken language systems is the availability of speech data that have been labeled and time-aligned at the phonetic level. Although manual phonetic alignment is considered more accurate than automatic methods, it is too time consuming to be commonly used for aligning large corpora. One reason for the greater accuracy of human labeling is that humans are better able to locate distinct events in the speech signal that correspond to specific phonetic characteristics. The development of the proposed method was motivated by the belief that if an automatic alignment method were to use such acoustic-phonetic information, its accuracy would become closer to that of human performance. Our hypothesis is that the integration of acoustic-phonetic information into a state-of-the-art automatic phonetic alignment system will significantly improve its accuracy and robustness. In developing an alignment system that uses acoustic-phonetic information, we use a measure of intensity discrimination in detecting voicing, glottalization, and burst-related impulses. We propose and implement a method of voicing determination that has average accuracy of 97.25% (which is an average 58% reduction in error over a baseline system), a fundamental-frequency extraction method with average absolute error of 3.12 Hz (representing a 45% reduction in error), and a method for detecting burst-related impulses with accuracy of 86.8% on the TIMIT corpus (which is a 45% reduction in error compared to reported results). In addition to these features, we propose a means of using acoustics-dependent transition information in the HMM framework. One aspect of successful implementation of this method is the use of distinctive phonetic features. To evaluate the proposed and baseline phonetic alignment systems, we measure agreement with manual alignments and robustness. On the TIMIT corpus, the proposed method has 92.57% agreement within 20 msec. The average agreement of the proposed method represents a 28% reduction in error over our state-of-the-art baseline system. In measuring robustness, the proposed method has 14% less standard deviation when evaluated on 12 versions of the TIMIT corpus.
Nonlinear model predictive control

Nonlinear model predictive control (NMPC) has been introduced in commercial applications. Since mid 1996 approximately 50 applications have been commissioned in polymers, chemicals, food, pulp and paper, and oil refining. This industrial presentation provides a brief overview of the commercial NMPC package and presents a summary of a specific polymers application that was chosen to demonstrate the different nonlinear models that can be used in a NMPC application.
Cognitive bubbles

Smith et al. (Econometrica 56(5):1119, 1988) reported large bubbles and crashes in experimental asset markets, a result that has been replicated many times. Here we test whether the occurrence of bubbles depends on the experimental subjects’ cognitive sophistication. In a two-part experiment, we first run a battery of tests to assess the subjects’ cognitive sophistication and classify them into low or high levels. We then invite them separately to two asset market experiments populated only by subjects with either low or high cognitive sophistication. We observe classic bubble and crash patterns in markets populated by subjects with low levels of cognitive sophistication. Yet, no bubbles or crashes are observed with our sophisticated subjects, indicating that cognitive sophistication of the experimental market participants has a strong impact on price efficiency.
Application of multiple channel estimators in MLSE detectors for fast time-varying and frequency selective channels

The performance of conventional MLSE equalisers is far from ideal when the channel is fading rapidly compared to the transmitted symbol period due, in part, to poor channel estimation/tracking of the channel variations. A variant on the standard MLSE technique utilising more than one channel estimator has been previously proposed but the increased performance obtained is at the expense of a significant increase in computational complexity. This paper reports work on investigations into the performance/complexity trade-off between this multiple channel estimator technique and a novel version which reduces the number of channel estimators.
An explicit asynchronous contact algorithm for elastic body–rigid wall interaction

The use of multiple‐time‐step integrators can provide substantial computational savings over traditional one‐time‐step methods for the simulation of solid dynamics, while maintaining desirable properties, such as energy conservation. Contact phenomena generally require either the adoption of an implicit algorithm or the use of unacceptably small time steps to prevent large amount of numerical dissipation from being introduced. This paper introduces a new explicit dynamic contact algorithm that, by taking advantage of asynchronous time stepping, delivers an outstanding energy performance at a much more acceptable computational cost. We demonstrated the performance of the numerical method with several three‐dimensional examples. Copyright © 2011 John Wiley & Sons, Ltd.
Distributed coverage games for mobile visual sensor networks

Motivated by current challenges in data-intensive sensor networks, we formulate a coverage optimization problem for mobile visual sensors as a (constrained) repeated multi-player game. Each visual sensor tries to optimize its own coverage while minimizing the processing cost. We present two distributed learning algorithms where each sensor only remembers its own utility values and actions played during the last plays. These algorithms are proven to be convergent in probability to the set of (constrained) Nash equilibria and global optima of certain coverage performance metric, respectively.
A New Groups Key Distribution for Secure Wireless Multicast

A session key distribution is proposed for secure multicast scheme, which employs decentralized group architecture and accommodates the dynamic group changing efficiently. Compared with the similar schemes, the proposed scheme has advantages with regard to the rekeying cost, the storage overhead and the computation efficiency. It achieves the backward/forward secrecy and has the capacity to provide data confidentiality and integrity.
A generic formal plan recognition theory

Plan recognition is the task of inferring intentions (in terms of plans) from the actions or utterances of observed actors (Kautz, 1986). In this paper, we provide a new logical formalization of plan recognition, which allows us to clearly specify, validate and design the plan recognition process (useful in several domains such as: discourse understanding, human computer interaction, and so on). However, depending on the applications different recognition strategies are used (top-down, bottom-up, and so on). Therefore, to describe the possible strategies, we only chose to formalize the reasoning basic steps of plan recognition. Moreover, only the specific aspects of the recognition problem are formalized and fixed. Therefore, the other aspects can be determined to satisfy the system needs. To illustrate our formalism, we describe two strategies of plan recognition used in many applications. We also present an algorithm useful to the discourse understanding.
Computing Accurate Grammatical Feedback in a Virtual Writing Conference for German-Speaking Elementary-School Children: An Approach Based on Natural Language Generation

We built a natural language processing (NLP) system implementing a "virtual writing conference" for elementary-school children, with German as the target language. Currently, state-of-the-art computer support for writing tasks is restricted to multiple-choice questions or quizzes because automatic parsing of the often ambiguous and fragmentary texts produced by students presents insurmountable problems. Here, we follow a different course by deploying natural language generation technology to evaluate and improve the grammatical quality of learner output. Based on an abstract representation of the story under construction, all paraphrases of simple and combined clauses are generated fully and automatically. From this source, the system produces exercises enabling the learners to improve their sentences. We describe how a pedagogical agent supports the writing process of elementary-school children by providing appropriate feedback.