Miracle-dz/UniFAR-FTUs huggingface dataset:
Global And Local Synchronization In Parallel Space-Aware Applications

Space-aware applications are characterized by an explicit representation of a spatial environment in which some entities live and operate by interacting with each other and with the hosting territory. A relevant space-aware application domain is the so-called urban computing, embracing issues like the simulation and implementation of public transportation systems, tra ﬃ c management, urban monitoring and control. The execution of such applications is often distributed on parallel computing nodes, which need to cooperate and exchange data among each other, thus raising synchronization issues. In this paper we analyze time-related characteristics of the computational process in a space-aware application in the case when each node does not need global synchronization (i.e. synchronization with all other nodes) but requires only local synchronization (i.e. synchronization with a subset of neighbor nodes). Performance is evaluated both analytically and numerically. We provide the analytical support to an important conclusion: the mean computation time per step remains ﬁnite irrespective of the number of nodes under local synchronization, while under global synchronization it grows unboundedly as the number of nodes increases. In practical scenarios this corresponds to signiﬁcantly better scalability properties of local synchronization.
Stackelberg Game-Based Models In Energy-Aware Cloud Scheduling

Energy-awareness remians the important problem in today’s cloud computing (CC). Optimization of the energy consumed in cloud data centers and computing servers is usually related to the scheduling problems. It is very difficult to define an optimal scheduling policy without negoative influence into the system performance and task completion time. In this work, we define a general cloud scheduling model based on a Stackelberg game with the workload scheduler and energy-efficiency agent as the main players. In this game, the aim of the scheduler is the minimization of the makespan of the workload, which is achieved by the employ of a genetic scheduling algorithm that maps the workload tasks into the computational nodes. The energy-efficiency agent selects the energy-optimization techniques based on the idea of switchin-off of the idle machines, in response to the scheduler decisions. The efficiency of the proposed model has been tested using a SCORE cloud simmulator. Obtained results show that the proposed model performs better than static energy-optimization strategies, achieving a fair balance between low energy consumption and short queue times and makespan.
Depression and Anxiety in Heart Failure: A Review

Learning objectives After participating in this activity, learners should be better able to: • Identify the relationships between depression, anxiety, and heart failure (HF). • Assess methods for accurately diagnosing depression and anxiety disorders in patients with HF. • Evaluate current evidence for treatment of anxiety and depression in patients with HF. Background In patients with heart failure (HF), depression and anxiety disorders are common and associated with adverse outcomes such as reduced adherence to treatment, poor function, increased hospitalizations, and elevated mortality. Despite the adverse impact of these disorders, anxiety and depression remain underdiagnosed and undertreated in HF patients. Methods We performed a targeted literature review to (1) identify associations between depression, anxiety, and HF, (2) examine mechanisms mediating relationships between these conditions and medical outcomes, (3) identify methods for accurately diagnosing depression and anxiety disorders in HF, and (4) review current evidence for treatments of these conditions in this population. Results Both depression and anxiety disorders are associated with the development and progression of HF, including increased rates of mortality, likely mediated through both physiologic and behavioral mechanisms. Given the overlap between cardiac and psychiatric symptoms, accurately diagnosing depression or anxiety disorders in HF patients can be challenging. Adherence to formal diagnostic criteria and utilization of a clinical interview are the best courses of action in the evaluation process. There is limited evidence for the efficacy of pharmacologic and psychotherapy in patients with HF. However, cognitive-behavioral therapy has been shown to improve mental health outcomes in patients with HF, and selective serotonin reuptake inhibitors appear safe in this cohort. Conclusions Depression and anxiety disorders in HF patients are common, underrecognized, and linked to adverse outcomes. Further research to improve detection and develop effective treatments for these disorders in HF patients is badly needed.
Clinical algorithm to manage anemia in epidermolysis bullosa

Epidermolysis bullosa is a group of rare genetic disorders with multiple organ system involvement. In one severe form, recessive dystrophic epidermolysis bullosa, chronic anemia is common. This report outlines the multifactorial nature of anemia in recessive dystrophic epidermolysis bullosa and presents a practical clinical algorithm based on expert consensus for the diagnosis and treatment of anemia in recessive dystrophic epidermolysis bullosa.
Increased Dkk‐1 plasma levels may discriminate disease subtypes in myeloproliferative neoplasms

Alterations in the bone marrow niche induced by abnormal production of cytokines and other soluble factors have been associated with disease progression in classical BCR‐ABL1 negative myeloproliferative neoplasms (MPN). Variations in circulating proteins might reflect local disease processes and plasma proteome profiling could serve to identify possible diagnostic and prognostic biomarkers. We employed a human cytokine array to screen for 105 distinct analytes in pooled plasma samples obtained from untreated young MPN patients (<35 years) with different clinical phenotypes and driver mutations, as well as from healthy individuals. Among molecules that exhibited significantly increased levels in MPN patients versus controls, the top of the list was represented by Dickkopf‐related protein 1 (Dkk‐1), which also showed the highest potential for discrimination between MPN subtypes. In the next step, a quantitative ELISA was used to measure plasma Dkk‐1 levels in 30 young‐onset MPN—10 essential thrombocythemia (ET), 10 polycythemia vera (PV), 10 pre‐fibrotic primary myelofibrosis (pre‐PMF)—and 10 controls. The results suggested that plasma Dkk‐1 levels could differentiate ET from pre‐PMF, in JAK2 V617F‐positive as well as in CALR‐positive patients, and also ET from PV in JAK2 V617F‐positive patients.
Determining treatment intensity in elderly patients with multiple myeloma

ABSTRACT Introduction: In the majority of cases, multiple myeloma is a disease occurring in elderly patients. In the last decades, a major improvement in myeloma patients’ outcome has been achieved with the introduction of several new drugs. However, this positive outcome was less likely to occur in elderly patients. Areas covered: An overall increase of myeloma cases in elderly patients is expected in the next years. This patient population is highly heterogeneous in terms of physiological functions and ability to resist stressing conditions such as myeloma and its treatment. While physicians cannot prevent the stress arising from the disease itself, the intensity of therapeutic approaches can be tuned according to patients’ predicted tolerance. In this review, we focus on the assessment of patients’ fitness and on available significant data on treatment efficacy and tolerability in elderly patients. Expert commentary: Fit, elderly patients should undergo full-dose therapy to maximize the depth of response, while intermediate and frail patients benefit from reduced-dose regimens in order to avoid toxicity and preserve quality of life. Ongoing trials will provide further evidence to individualize treatment on the basis of geriatric assessment and disease characteristics.
An Extension of the REA Framework to Support Balanced Scorecard Information Requirements

In this paper, we propose extensions to the resource‐event‐agent (REA) framework to encompass the information requirements of the balanced scorecard and other management systems that incorporate nonfinancial measures. The REA conceptual accounting framework was designed to describe the information architecture related to an organization's economic activity (e.g., McCarthy 1982; Dunn et al. 2005). Geerts and McCarthy (2001b, 2002) extended the original REA to include value‐chain level configurations, task‐level configurations, and encompass a broader array of business economic phenomena. Yet, the REA framework remains closely tied to its accounting roots, with a focus on economic events and financial resources. A substantial number of organizations are adopting strategic management systems that include both financial and nonfinancial measures to overcome known limitations of systems based on traditional financial data alone (e.g., Said et al. 2003; Eccles et al. 2001; Ittner et al. 2003). We therefore exam.
On the Complexity of Sampling Vertices Uniformly from a Graph

We study a number of graph exploration problems in the following natural scenario: an algorithm starts exploring an undirected graph from some seed vertex; the algorithm, for an arbitrary vertex v that it is aware of, can ask an oracle to return the set of the neighbors of v. (In the case of social networks, a call to this oracle corresponds to downloading the profile page of user v.) The goal of the algorithm is to either learn something (e.g., average degree) about the graph, or to return some random function of the graph (e.g., a uniform-at-random vertex), while accessing/downloading as few vertices of the graph as possible. Motivated by practical applications, we study the complexities of a variety of problems in terms of the graph's mixing time t_{mix} and average degree d_{avg} - two measures that are believed to be quite small in real-world social networks, and that have often been used in the applied literature to bound the performance of online exploration algorithms. Our main result is that the algorithm has to access Omega (t_{mix} d_{avg} epsilon^{-2} ln delta^{-1}) vertices to obtain, with probability at least 1-delta, an epsilon additive approximation of the average of a bounded function on the vertices of a graph - this lower bound matches the performance of an algorithm that was proposed in the literature. We also give tight bounds for the problem of returning a close-to-uniform-at-random vertex from the graph. Finally, we give lower bounds for the problems of estimating the average degree of the graph, and the number of vertices of the graph.
UMAP 2018 Intelligent User-Adapted Interfaces: Design and Multi-Modal Evaluation (IUadaptMe) Workshop Chairs' Welcome &Organization

It is our great pleasure to welcome you to the UMAP 2018 IUadaptME workshop. The workshop is focused on two main topics. On the one hand, we are interested in proactive user interfaces that support the user in ubiquitous contexts. This includes applications that can be provided as a service and can be used on any mobile device, with no need for installation or software updates; personalized user interfaces that track the user's progress, adapt to her/his needs and prompt her/him without being disruptive or inconvenient and provide analytics and predictive instruments; smart and personalized information services to explore, search, interact, share, practice and discover qualitative content that fits the user's diverse and changing needs; approaches that are focused on understanding and representing content, users, devices and situations where interaction happens, with special focus on semantics.
Comparing human and machine performances in transcribing 18th century handwritten Venetian script

Automatic transcription of handwritten texts has made important progress in the recent years [1] [2] [3]. This increase in performance, essentially due to new architectures combining convolutional neural networks with recurrent neutral networks, opens new avenues for searching in large databases of archival and library records. This paper reports on our recent progress in making million digitized Venetian documents searchable, focusing on a first subset of 18th century fiscal documents from the Venetian State Archives (Condizione di Decima, Quaderni dei Trasporti, Catastici). For this study, about 23’000 image segments containing 55’000 Venetian names of persons and places were manually transcribed by archivists, trained to read such kind of handwritten script, during an annotation phase that lasted 2 years. This annotated dataset was used to train and test a deep learning architecture, with the objective of making the entire set of more than 2 million pages searchable. As described in the following paragraphs, performance levels (about 10% character error rate) are satisfactory for search use cases, which demonstrates that such kinds of approaches are viable at least for this typology of handwritten scripts. This paper compares this level of reading performance with the reading capabilities of Italian-speaking transcribers, preselected with a test based on 100 transcriptions. More than 8500 new human transcriptions were produced, confirming that the amateur transcribers were not as good as the expert. However, on average, the machine outperforms the amateur transcribers in this transcription tasks.
Decrease in force steadiness with aging is associated with increased power of the common but not independent input to motor neurons.

Declines in motor function with advancing age have been attributed to changes occurring at all levels of the neuromuscular system. However, the impact of aging on the control of muscle force by spinal motor neurons is not yet understood. In this study on 20 individuals aged between 24 and 75 yr (13 men, 7 women), we investigated the common synaptic input to motor neurons of the tibialis anterior muscle and its impact on force control. Motor unit discharge times were identified from high-density surface EMG recordings during isometric contractions at forces of 20% of maximal voluntary effort. Coherence analysis between motor unit spike trains was used to characterize the input to motor neurons. The decrease in force steadiness with age ( R2 = 0.6, P < 0.01) was associated with an increase in the amplitude of low-frequency oscillations of functional common synaptic input to motor neurons ( R2 = 0.59; P < 0.01). The relative proportion of common input to independent noise at low frequencies increased with variability (power) in common synaptic input. Moreover, variability in interspike interval did not change and strength of the common input in the gamma band decreased with age ( R2 = 0.22; P < 0.01). The findings indicate that age-related reduction in the accuracy of force control is associated with increased common fluctuations to motor neurons at low frequencies and not with an increase in independent synaptic input. NEW & NOTEWORTHY The influence of aging on the role of spinal motor neurons in accurate force control is not yet understood. We demonstrate that aging is associated with increased oscillations in common input to motor neurons at low frequencies and with a decrease in the relative strength of gamma oscillations. These results demonstrate that the synaptic inputs to motor neurons change across the life span and contribute to a decline in force control.
The use of smartphones for wayfinding by people with mild dementia

The aim of this study was to explore the acceptability, feasibility and usability of older people with mild dementia to use smartphone for wayfinding. Thirty cognitively normal older people and 16 people with mild dementia were recruited to participate in a wayfinding trial in the free-living environment. Five feasibility and three acceptability markers were compared between the groups. Content analysis on the video-recorded trial processes and individual interviews was employed to identify the usability issues. The results found that there were no significant between-group differences on the feasibility markers, except that the people with mild dementia needed significantly more time to complete the wayfinding trial and workshop; or on the acceptability items. Sensory/cognitive impairment and GPS signal reliability affected their usability. Mild dementia does not limit the older people to use smartphones for wayfinding in the free-living environment. Future studies should examine the efficacy and safety of smartphone to promote outdoor independence of the people with mild dementia.
Tight Bounds on Online Checkpointing Algorithms

The problem of online checkpointing is a classical problem with numerous applications that has been studied in various forms for almost 50 years. In the simplest version of this problem, a user has to maintain k memorized checkpoints during a long computation, where the only allowed operation is to move one of the checkpoints from its old time to the current time, and his goal is to keep the checkpoints as evenly spread out as possible at all times. Bringmann, Doerr, Neumann, and Sliacan studied this problem as a special case of an online/offline optimization problem in which the deviation from uniformity is measured by the natural discrepancy metric of the worst case ratio between real and ideal segment lengths. They showed this discrepancy is smaller than 1.59-o(1) for all k and smaller than ln 4-o(1)≈ 1.39 for the sparse subset of k’s, which are powers of 2. In addition, they obtained upper bounds on the achievable discrepancy for some small values of k. In this article, we solve the main problems left open in the above-mentioned paper by proving that ln 4 is a tight upper and lower bound on the asymptotic discrepancy for all large k and by providing tight upper and lower bounds (in the form of provably optimal checkpointing algorithms, some of which are in fact better than those of Bringmann et al.) for all the small values of k ≤ 10. In the last part of the article, we describe some new applications of this online checkpointing problem.
Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study

Background Social anxiety is highly prevalent among college students. Current methodologies for detecting symptoms are based on client self-report in traditional clinical settings. Self-report is subject to recall bias, while visiting a clinic requires a high level of motivation. Assessment methods that use passively collected data hold promise for detecting social anxiety symptoms and supplementing self-report measures. Continuously collected location data may provide a fine-grained and ecologically valid way to assess social anxiety in situ. Objective The objective of our study was to examine the feasibility of leveraging noninvasive mobile sensing technology to passively assess college students’ social anxiety levels. Specifically, we explored the different relationships between mobility and social anxiety to build a predictive model that assessed social anxiety from passively generated Global Positioning System (GPS) data. Methods We recruited 228 undergraduate participants from a Southeast American university. Social anxiety symptoms were assessed using self-report instruments at a baseline laboratory session. An app installed on participants’ personal mobile phones passively sensed data from the GPS sensor for 2 weeks. The proposed framework supports longitudinal, dynamic tracking of college students to evaluate the relationship between their social anxiety and movement patterns in the college campus environment. We first extracted the following mobility features: (1) cumulative staying time at each different location, (2) the distribution of visits over time, (3) the entropy of locations, and (4) the frequency of transitions between locations. Next, we studied the correlation between these features and participants’ social anxiety scores to enhance the understanding of how students’ social anxiety levels are associated with their mobility. Finally, we used a neural network-based prediction method to predict social anxiety symptoms from the extracted daily mobility features. Results Several mobility features correlated with social anxiety levels. Location entropy was negatively associated with social anxiety (during weekdays, r=−0.67; and during weekends, r=−0.51). More (vs less) socially anxious students were found to avoid public areas and engage in less leisure activities during evenings and weekends, choosing instead to spend more time at home after school (4 pm-12 am). Our prediction method based on extracted mobility features from GPS trajectories successfully classified participants as high or low socially anxious with an accuracy of 85% and predicted their social anxiety score (on a scale of 0-80) with a root-mean-square error of 7.06. Conclusions Results indicate that extracting and analyzing mobility features may help to reveal how social anxiety symptoms manifest in the daily lives of college students. Given the ubiquity of mobile phones in our society, understanding how to leverage passively sensed data has strong potential to address the growing needs for mental health monitoring and treatment.
Masculinity and Depression: A Longitudinal Investigation of Multidimensional Masculine Norms Among College Men

The transition from high school to college represents a pivotal developmental period that may result in significant maladjustment for first-year college men. Men may feel pressured to “prove” their masculinity by engaging in traditional masculine behaviors that could be negative for their overall well-being. Although adherence to multidimensional masculine norms has been associated with poorer mental health, no studies have examined the role of masculine norms on prospective depressive symptoms among first-year college men. Examining college men’s adherence to multidimensional masculine norms longitudinally can offer a promising theoretical framework to explain within-group variability in depression symptomatology. The sample included 322 men from the Mid-Atlantic region of the United States. Masculine norms were assessed during the beginning of their first year of college. Depressive symptomatology was assessed 6 months after the first wave of data collection. Masculine norms were positively and negatively related to prospective depression scores, such that men who endorsed the masculine norms of Self-Reliance, Playboy (i.e., desire to have multiple sexual partners), and Violence, had heightened risk, whereas men who endorsed Winning and Power Over Women were less likely to report depressive symptomatology. Distinct masculine norms appear to confer risk for depression while other norms appear to be protective. This study was the first to examine the role of multidimensional masculine norms on prospective depressive symptomatology among college men. The results suggest that practitioners working with men should consider assessing their clients’ adherence to distinct masculine norms and explore how these might be impacting their current mental health.
Degradation of chlorophyll and synthesis of flavonols during autumn senescence—the story told by individual leaves

Chlorophylls are degraded and flavonoids synthesized during autumn senescence of deciduous trees. In the present study, chlorophyll and flavonol contents of individual leaves were monitored non-destructively throughout the autumn. Loss of chlorophyll and synthesis of flavonols were not gradual. Instead, each leaf maintained steady chlorophyll content until rapid chlorophyll degradation, accompanied by flavonol synthesis, was triggered. In ~1 week, the leaf turns yellow and falls. The pattern was similar in birch (Betula pendula), maple (Acer platanoides) and bird cherry (Prunus padus); in rowan (Sorbus aucuparia), very slow gradual chlorophyll degradation occurred on top of the main pattern.