Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining social networks for personalized email prioritization

Email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. A good way to alleviate email overload is to automatically prioritize received messages according to the priorities of each user. However, research on statistical learning methods for fully personalized email prioritization (PEP) has been sparse due to privacy issues, since people are reluctant to share personal messages and importance judgments with the research community. It is therefore important to develop and evaluate PEP methods under the assumption that only limited training examples can be available, and that the system can only have the personal email data of each user during the training and testing of the model for that user. This paper presents the first study (to the best of our knowledge) under such an assumption. Specifically, we focus on analysis of personal social networks to capture user groups and to obtain rich features that represent the social roles from the viewpoint of a particular user. We also developed a novel semi-supervised (transductive) learning algorithm that propagates importance labels from training examples to test examples through message and user nodes in a personal email network. These methods together enable us to obtain an enriched vector representation of each new email message, which consists of both standard features of an email message (such as words in the title or body, sender and receiver IDs, etc.) and the induced social features from the sender and receivers of the message. Using the enriched vector representation as the input in SVM classifiers to predict the importance level for each test message, we obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection. We obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection: the relative error reduction in MAE was 31% in micro-averaging, and 14% in macro-averaging.
Development of CIE-10 and DSM-IV cannabis disorders / Desarrollo de la CIE-10 y el DSM-IV para trastornos por cánnabis

La CIE-10 y el DSM-IV presentan, con pocas diferencias, la misma clasificacion para los trastornos psiquiatricos por consumo de sustancias. Como ambos instrumentos diagnosticos son meras estructuras, se intenta desarrollar su contenido para los trastornos psiquiatricos relacionados con el cannabis. Se enfatiza el debate cannabis-esquizofrenia.
Improved Multi-criteria Spanners for Ad-Hoc Networks Under Energy and Distance Metrics

This paper studies the problem of topology control in random wireless ad-hoc networks through power assignment for n nodes uniformly distributed in a unit square. In particular, we are interested in asymmetric power assignments so that the induced communication graph has a good distance and energy stretch simultaneously, with additional optimization objectives: both minimizing the total energy consumption, interference level, hop-diameter, and maximizing the network lifetime. We present several power assignments with varying construction time complexity. The probability of our results converges to one as the number of network nodes, n, increases. To the best of our knowledge, these are the first results for spanner construction in wireless ad-hoc networks with provable bounds for both, energy and distance, metrics simultaneously.
Cognitive Electrophysiology of Attention: Signals of the Mind

Cognitive Electrophysiology of Attention explores the fundamental mechanisms of attention and related cognitive functions from cognitive neuroscience perspectives. Attention is an essential cognitive ability that enables humans to process and act upon relevant information while ignoring distracting information, and the capacity to focus attention is at the core of mental functioning. Understanding the neural bases of human attention remains a key challenge for neuroscientists and psychologists, and is essential for translational efforts to treat attentional deficits in a variety of neurological and psychiatric disorders. Cognitive electrophysiology is at the center of a multidisciplinary approach that involves the efforts of psychologists, neuroscientists, neuropsychologists, psychiatrists, and neurologists to identify basic brain mechanisms and develop translational approaches to improve mental health. This edited volume is authored by leading investigators in the field and discusses methods focused on electrophysiological recordings in humans, including electroencephalography (EEG) and event-related potential (ERP) methods, and also incorporates evidence from functional magnetic resonance imaging (fMRI). Cognitive Electrophysiology of Attention illuminates specific models about attentional mechanisms in vision, audition, multisensory integration, memory, and semantic processing in humans. It provides an exhaustive overview of attention processes, going from normal functioning to the pathological, and using a combination of methodological tools. It features an important reference for electrophysiology researchers looking at underlying attention processes rather than the methods themselves. It enables researchers across a broad range of cognitive-process and methodological specialties to stay current on particular hypotheses, findings, and methods. It is edited and authored by the worldwide leaders in the field, affording the broadest, most expert coverage available.
Multiple transition model and enhanced boundary scan architecture to test interconnects for signal integrity

As the technology is shrinking toward 50 nm and the working frequency is going into multi gigahertz range, the effect of interconnects on functionality and performance of system-on-chips is becoming dominant. More specifically, distortion (integrity loss) of signals traveling on high-speed interconnects can no longer be ignored. Here, we propose a new fault model, called multiple transition, and its corresponding test pattern generation mechanism. We also extend the conventional boundary scan architecture to allow testing signal integrity in SoC interconnects. Our extended JTAG architecture collects and outputs the integrity loss information using the enhanced observation cells. The architecture fully complies with the JTAG standard and can be adopted by any SoC that is IEEE 1149.1 compliant.
A Clinical Framework to Facilitate Risk Stratification When Considering an Active Surveillance Alternative to Immediate Biopsy and Surgery in Papillary Microcarcinoma.

BACKGROUND The 2015 American Thyroid Association thyroid cancer management guidelines endorse an active surveillance management approach as an alternative to immediate biopsy and surgery in subcentimeter thyroid nodules with highly suspicious ultrasonographic characteristics and in cytologically confirmed very low risk papillary thyroid cancer (PTC). However, the guidelines provide no specific recommendations with regard to the optimal selection of patients for an active surveillance management approach. This article describes a risk-stratified clinical decision-making framework that was developed by the thyroid cancer disease management team at Memorial Sloan Kettering Cancer Center as the lessons learned from Kuma Hospital in Japan were applied to a cohort of patients with probable or proven papillary microcarcinoma (PMC) who were being evaluated for an active surveillance management approach in the United States. SUMMARY A risk-stratified approach to the evaluation of patients with probable or proven PMC being considered for an active surveillance management approach requires an evaluation of three interrelated but distinct domains: (i) tumor/neck ultrasound characteristics (e.g., size of the primary tumor, the location of the tumor within the thyroid gland); (ii) patient characteristics (e.g., age, comorbidities, willingness to accept observation); and (iii) medical team characteristics (e.g., availability and experience of the multidisciplinary team). Based on an analysis of the critical factors within each of these domains, patients with probable or proven PTC can then be classified as ideal, appropriate, or inappropriate candidates for active surveillance. CONCLUSION Risk stratification utilizing the proposed decision-making framework will improve the ability of clinicians to recognize individual patients with proven or probable PMC who are most likely to benefit from an active surveillance management option while at the same time identifying patients with proven or probable PMC that would be better served with an upfront biopsy and surgical management approach.
Random Pairwise Gossip on $\text{CAT}(\kappa)$ Metric Spaces

In the context of sensor networks, gossip algorithms are a popular, well established technique for achieving consensus when sensor data are encoded in linear spaces. Gossip algorithms also have several extensions to non linear data spaces. Most of these extensions deal with Riemannian manifolds and use Riemannian gradient descent. This paper, instead, exhibits a very simple metric property that does not rely on any differential structure. This property strongly suggests that gossip algorithms could be studied on a broader family than Riemannian manifolds. And it turns out that, indeed, (local) convergence is guaranteed as soon as the data space is a mere CAT(κ) metric space. We also study convergence speed in this setting and establish linear rates for CAT(0) spaces, and local linear rates for CAT(κ) spaces with κ > 0. Numerical simulations on several scenarii, with corresponding state spaces that are either Riemannian manifolds-as in the problem of positive definite matrices consensus-or bare metric spaces-as in the problem of arms consensus-validate the results. This shows that our metric approach not only allows for a simpler and more general mathematical analysis but also paves the way for new kinds of applications that go beyond the Riemannian setting.
Plugin-Orb for Applications in a Pervasive Computing Environment

Pervasive computing environments aim to integrate components which are highly heterogeneous, not only in terms of software technologies, but also in terms of hardware resources. Unfortunately, classic middleware platforms do not appear able to cope with such a level of heterogeneity and new communication mechanisms are thus required. This paper presents Plugin-Orb, an adaptive middleware for mobile and embedded devices with poor resources. Plugin-Orb aims to obtain a communication mechanism which would make a client application able to inter-operate with server objects running over different middleware technologies using an unified communication interface, thus having the programmer able to develop applications without taking into account underlying middleware technologies.
HCP with PSMA: A Robust Spoken Language Parser

"Spoken language" is a field of natural language processing, which deals with transcribed speech utterances. The processing of spoken language is much more complex and complicated than processing standard, grammatically correct natural language, and requires special treatment of typical speech phenomena called "disfluencies", like corrections, interjections and repetitions of words or phrases. We present a parsing technique that utilizes a Hybrid Connectionist Parser (HCP) extended with a Phrase Structure Matching Algorithm (PSMA) for the syntactic processing (parsing) of spoken language. The HCP is a hybrid of a connectionist and a symbolic approach, which builds a neural network dynamically based on a given context-free grammar and an input sentence. It has an advantage over traditional parsers due to the use of graded activation and activation passing in the network. These features were exploited in techniques to detect and repair disfluencies, in combination with the Phrase Structure Matching Algorithm, which operates on the graphical structure of the network. This approach to spoken language parsing is purely syntactical and - in contrast to other work - does not require a modified grammar for spoken language, or information derived from the speech input, nor any pre-marking of disfluencies. We implemented the combined HCP and PSMA parser and tested it on a standard corpus for spoken language, the HCRC Map Task Corpus. The experiments showed very good results, especially for a purely syntactic method, which detects as well as corrects disfluencies.
Pathogenesis of hyperinflation in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a preventable and treatable lung disease characterized by airflow limitation that is not fully reversible. In a significant proportion of patients with COPD, reduced lung elastic recoil combined with expiratory flow limitation leads to lung hyperinflation during the course of the disease. Development of hyperinflation during the course of COPD is insidious. Dynamic hyperinflation is highly prevalent in the advanced stages of COPD, and new evidence suggests that it also occurs in many patients with mild disease, independently of the presence of resting hyperinflation. Hyperinflation is clinically relevant for patients with COPD mainly because it contributes to dyspnea, exercise intolerance, skeletal muscle limitations, morbidity, and reduced physical activity levels associated with the disease. Various pharmacological and nonpharmacological interventions have been shown to reduce hyperinflation and delay the onset of ventilatory limitation in patients with COPD. The aim of this review is to address the more recent literature regarding the pathogenesis, assessment, and management of both static and dynamic lung hyperinflation in patients with COPD. We also address the influence of biological sex and obesity and new developments in our understanding of hyperinflation in patients with mild COPD and its evolution during progression of the disease.
Exploiting Hardware Heterogeneity within the Same Instance Type of Amazon EC2

Cloud computing providers might start with near-homogeneous hardware environment. Over time, the homogeneous environment will most likely evolve into heterogeneous one because of possible upgrades and replacement of outdated hardware. In turn, the hardware heterogeneity will result into performance variation. In this paper, we look into the hardware heterogeneity and the corresponding performance variation within the same instance type of Amazon Elastic Compute Cloud (Amazon EC2). Standard large instance is selected as the example. We find out that there exist three different subtypes of hardware configuration in the standard large instance. Through a set of detailed micro-benchmark and application-level benchmark measurements, we observe that the performance variation within the same subtype of instance is relatively small, whilst the variation between different sub-types can be up to 60%. By selecting better-performing instances to complete the same task, end-users of Amazon EC2 platform can achieve up to 30% cost saving.
Systemic Risk and Stability in Financial Networks

This paper argues that the extent of financial contagion exhibits a form of phase transition: as long as the magnitude of negative shocks affecting financial institutions are sufficiently small, a more densely connected financial network (corresponding to a more diversified pattern of interbank liabilities) enhances financial stability. However, beyond a certain point, dense interconnections serve as a mechanism for the propagation of shocks, leading to a more fragile financial system. Our results thus highlight that the same factors that contribute to resilience under certain conditions may function as significant sources of systemic risk under others. (JEL D85, E44, G21, G28, L14)
LOCOMOTOR PERFORMANCE AT LOW TEMPERATURE AND THE EVOLUTION OF NOCTURNALITY IN GECKOS

Nocturnal geckos are active at body temperatures 10–35°C below the thermal optima for maximum rate of aerobic metabolism (V.O2max) of diurnal lizards. Therefore, given ancestral (diurnal) lizard physiology, nocturnality causes a substantial thermal handicap in locomotor performance. In prior studies, we hypothesized that a low minimum cost of locomotion (Cmin) in geckos was an adaptation that increased locomotor endurance capacity at low, nocturnal temperatures. However, Cmin is only part of an integrated system that, in conjunction with the maximum rate of oxygen consumption, sets the maximum speed that can be sustained aerobically (termed the maximum aerobic speed or MAS). We conducted the first phylogenetic analysis of MAS and V.O2max lizards and found that the greatest changes in MAS, Cmin and V.O2max (at activity temperatures) in the evolutionary history of lizards all coincided with the evolution of nocturnality in geckos. Geckos active at 15–25°C did not become optimized for nocturnal temperatures, or fully offset the thermal effects of nocturnality by evolving maximal rates of oxygen consumption comparable to diurnal lizards active at 35°C. Geckos did evolve MAS twice that of diurnal lizards running at low temperatures by evolving a remarkably low Cmin. Allometric analysis and phylogenetically independent contrasts of V.O2max , Cmin, and MAS indicate a 72% evolutionary decrease in V.O2max , (at activity temperatures) and a 50% evolutionary decrease in Cmin concordant with the evolution of nocturnality in geckos. Experimental measurements show that decreased Cmin in six species of gecko increased MAS by 50–120% compared to diurnal lizards at low temperatures. Thus, geckos sufficiently overcame the near paralyzing effects of nocturnal temperatures, but only offset about 50% of the decrease in MAS resulting from the low maximum rate of oxygen consumption. Although the nocturnal environment remains severely suboptimal, the evolution of a low cost of locomotion in the ancestor of geckos was highly adaptive for nocturnality. We also present a generalized approach to ecophysiological evolution that integrates phylogeny with the causal relationships among environment, physiology, and performance capacity. With respect to a clade, two hypotheses are central to our integrative approach: (1) a change of an environmental variable (e.g., temperature) causes a performance handicap; and (2) evolution of a physiological variable (e.g., minimum cost of locomotion [Cmin]) increases performance in the derived environment. To test the hypothesis that evolution of a physiological variable is adaptive in nature, we suggest determining if individuals in nature perform at levels exceeding the performance capacity of their hypothetical ancestors and if this additional performance capacity is due to the evolution of the physiological variable in question.
Viral and Cellular Components of AAV2 Replication Compartments

Adeno-associated virus 2 (AAV2) is a helpervirus-dependent parvovirus with a bi-phasic life cycle comprising latency in absence and lytic replication in presence of a helpervirus, such as adenovirus (Ad) or herpes simplex virus type 1 (HSV-1). Helpervirus-supported AAV2 replication takes place in replication compartments (RCs) in the cell nucleus where virus DNA replication and transcription occur. RCs consist of a defined set of helper virus-, AAV2-, and cellular proteins. Here we compare the profile of cellular proteins recruited into AAV2 RCs or identified in Rep78-associated complexes when either Ad or HSV-1 is the helpervirus, and we discuss the potential roles of some of these proteins in AAV2 and helpervirus infection.
Business Documentation Derivation from Aspect-driven Enterprise Information Systems

Business documentation provides important overview of the system. It lists implemented business operations and their preconditions and post-conditions, as well as the architecture of the domain model. Such information is valuable for domain experts, who are able to review it, validate the conditions, and help to address errors in early phases of development. However, it is challenging to derive this documentation from existing systems as the business rules are tangled into source code. In this paper, we introduce a novel technique to business documentation derivation from aspect-driven enterprise information systems. These systems isolate business rules in a single focal point and we demonstrate its transformation into business documentation. In the combination with code inspection, we deliver reliable and easy to generate documentation significantly simplifying involvement of domain experts into development. Furthermore, we show the transformation of business rules into a formal language verifiable by a checker to validate the feasibility of business operations.
A Fast Text-Based Communication System for Handicapped Aphasiacs

In general, AAC systems are equipped with special access mechanisms, since the users lack the fine motor-control required to operate such systems with standard peripheral devices. The major problem with such AAC systems is low text typing speed. This paper presents a fast text-based communication system, enabled by finger language and supported by a virtual keyboard, to aim at efficient text entry. A handicapped aphasiac can enter text via the virtual keyboard by wearing a data glove to make proper finger gestures and use them to directly select the letters on the keyboard. The technique centers on the concept of finger language components. We have developed a finger language recognition system that could recognize static finger language components from the finger gestures of a handicapped aphasiac. We then extended the technique to continuous input of finger language components and developed a text-based communication environment for handicapped aphasiacs.
Error Exponents for the Detection of Gauss–Markov Signals Using Randomly Spaced Sensors

We derive the Neyman-Pearson error exponent for the detection of Gauss-Markov signals using randomly spaced sensors. We assume that the sensor spacings, d 1,d 2,. , are drawn independently from a common density fd(.), and we treat both stationary and nonstationary Markov models. Error exponents are evaluated using specialized forms of the strong law of large numbers, and are seen to take on algebraically simple forms involving the parameters of the Markov processes and expectations over fd(.) of certain functions of d 1. These expressions are evaluated explicitly when fd(.) corresponds to i) exponentially distributed sensors with placement density lambda; ii) equally spaced sensors; and iii) the proceeding cases when sensors fail (or equivalently, are asleep) with probability q. Many insights follow. For example, we determine the optimal lambda as a function of q in the nonstationary case. Numerical simulations show that the error exponent predicts trends of the simulated error rate accurately even for small data sizes.