Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultra-wideband Performance in a Dense Multipath Environment with Time and Spatial Diversity

This paper analyzes the performance of direct sequence ultra-wideband (DS-UWB) systems in a very dense and realistic multipath channel scenario with temporal and spatial diversity, considering Monte-Carlo simulation method and semi-analytical development. This work analyzes the impact of antenna elements and the number of rake fingers on the system performance, emphasizing the effects of the pulse shape correlation.
Devising a Cloud Scientific Workflow Platform for Big Data

Scientific workflow management systems (SWFMSs) are facing unprecedented challenges from big data deluge. As revising all the existing workflow applications to fit into Cloud computing paradigm is impractical, thus migrating SWFMSs into the Cloud to leverage the functionalities of both Cloud computing and SWFMSs may provide a viable approach to big data processing. In this paper, we first discuss the challenges for scientific workflow applications and the available solutions in details, and analyze the essential requirements for a scientific computing Cloud platform. Then we propose a service framework to normalize the integration of SWFMS with Cloud computing. Meanwhile, we also present our implementation experience based on the service Framework. At last, we set up a series of experiments to demonstrate the capability of our implementation and use a Montage Image Mosaic Workflow as a showcase of the implementation.
The impact of time of use (ToU)-awareness in energy and opex performance of a cloud backbone

Cloud computing is becoming a leading edge business model by migrating the resources such as software, storage and platform to remote locations in the Internet cloud. Data centers, as the main hosts of these cloud services, receive massive amount of demands and transmit services towards Internet routers, consequently consuming enormous bandwidth in the downstream. Due to high computing power, as well as the cooling power, data centers contribute to a significant amount of the power consumption and the operational expenditures (Opex) of the data center operator. In the Internet backbone where the services are transported between the users and the data centers, IP routers make the dominating portion of the power consumption and Opex associated with the network operator. In this paper, we study the impacts of Time of Use (ToU)-awareness on the Opex and energy-efficiency of the cloud network by introducing a Mixed Integer Linear Programming (MILP)-based design scheme for the cloud backbone, which aims at minimizing the network and data center power consumption. Furthermore, the optimization scheme takes advantage of the varying ToU rates in different locations of the cloud network so that Opex is minimized for the network and data center operators. Numerical results approve that Opex savings through ToU-aware provisioning are at the expense of increased delay per demand. Furthermore, by the end of the day, power-minimized provisioning introduces more savings to the network operator as upstream data center demands are transported towards the data centers at the off-peak locations, which in return leads to higher utilization of network components in longer routes.
Model Predictive Control for Freeway Networks Based on Multi-Class Traffic Flow and Emission Models

The main aim of this paper is to use multi-class macroscopic traffic flow and emission models for Model Predictive Control (MPC) for traffic networks. In particular, we use and compare extended versions of multi-class METANET, FASTLANE, multi-class VT-macro, and multi-class VERSIT+. In addition, end-point penalties based on these multi-class traffic flow and emission models are also included in the objective function of MPC to account for the behavior of the traffic system beyond the prediction horizon. A simulation experiment is implemented to evaluate the multi-class models. The results show that the approaches based on multi-class METANET and the extended emission models (multi-class VT-macro or multi-class VERSIT+) can improve the control performance for the total time spent and the total emissions with respect to the non-control case, and they are more capable of dealing with the queue length constraints than the approaches based on FASTLANE. Including end-point penalties can further improve the control performance with a small sacrifice in the computational efficiency for the approaches based on multi-class METANET but not for the approaches based on FASTLANE.
The Laplacian spectrum of neural networks

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these ‘conventional’ graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network’s structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks.
Medical students' opportunities to participate and learn from activities at an internal medicine ward: an ethnographic study

Objectives To optimise medical students’ early clerkship is a complex task since it is conducted in a context primarily organised to take care of patients. Previous studies have explored medical students’ perceptions of facilitation and hindrance of learning. However, the opportunities for medical student to learn within the culture of acute medicine care have not been fully investigated. This study aimed to explore how medical students approach, interact and socialise in an acute internal medicine ward context, and how spaces for learning are created and used in such a culture. Design and setting Ethnographic observations were performed of medical students' interactions and learning during early clerkship at an acute internal medicine care ward. Field notes were taken, transcribed and analysed qualitatively. Data analysis was guided by Wenger's theory of communities of practice. Participants 21 medical students and 30 supervisors participated. Results Two themes were identified: Nervousness and curiosity—students acted nervously and stressed, especially when they could not answer questions. Over time curiosity could evolve. Unexplored opportunities to support students in developing competence to judge and approach more complex patient-related problems were identified. Invited and involved—students were exposed to a huge variation of opportunities to learn, and to interact and to be involved. Short placements seemed to disrupt the learning process. If and how students became involved also depended on supervisors' activities and students' initiatives. Conclusions This study shed light on how an acute internal medicine ward culture can facilitate medical students' possibilities to participate and learn. Medical students' learning situations were characterised by questions and answers rather than challenging dialogues related to the complexity of presented patient cases. Further, students experienced continuous transfers between learning situations where the potential to be involved differed in a wide variety of ways.
Correspondence analysis to evaluate the transmission of Staphylococcus aureus strains in two New York State maximum-security prisons

SUMMARY Prisons/jails are thought to amplify the transmission of Staphylococcus aureus (SA) particularly methicillin-resistant SA infection and colonisation. Two independently pooled cross-sectional samples of detainees being admitted or discharged from two New York State maximum-security prisons were used to explore this concept. Private interviews of participants were conducted, during which the anterior nares and oropharynx were sampled and assessed for SA colonisation. Log-binomial regression and correspondence analysis (CA) were used to evaluate the prevalence of colonisation at entry as compared with discharge. Approximately 51% of admitted (N = 404) and 41% of discharged (N = 439) female detainees were colonised with SA. Among males, 59% of those admitted (N = 427) and 49% of those discharged (N = 393) were colonised. Females had a statistically significant higher prevalence (1·26: P = 0·003) whereas males showed no significant difference (1·06; P = 0·003) in SA prevalence between entry and discharge. CA demonstrated that some strains, such as spa types t571 and t002, might have an affinity for certain mucosal sites. Contrary to our hypothesis, the prison setting did not amplify SA transmission, and CA proved to be a useful tool in describing the population structure of strains according to time and/or mucosal site.
Link Clustering with Extended Link Similarity and EQ Evaluation Division

Link Clustering (LC) is a relatively new method for detecting overlapping communities in networks. The basic principle of LC is to derive a transform matrix whose elements are composed of the link similarity of neighbor links based on the Jaccard distance calculation; then it applies hierarchical clustering to the transform matrix and uses a measure of partition density on the resulting dendrogram to determine the cut level for best community detection. However, the original link clustering method does not consider the link similarity of non-neighbor links, and the partition density tends to divide the communities into many small communities. In this paper, an Extended Link Clustering method (ELC) for overlapping community detection is proposed. The improved method employs a new link similarity, Extended Link Similarity (ELS), to produce a denser transform matrix, and uses the maximum value of EQ (an extended measure of quality of modularity) as a means to optimally cut the dendrogram for better partitioning of the original network space. Since ELS uses more link information, the resulting transform matrix provides a superior basis for clustering and analysis. Further, using the EQ value to find the best level for the hierarchical clustering dendrogram division, we obtain communities that are more sensible and reasonable than the ones obtained by the partition density evaluation. Experimentation on five real-world networks and artificially-generated networks shows that the ELC method achieves higher EQ and In-group Proportion (IGP) values. Additionally, communities are more realistic than those generated by either of the original LC method or the classical CPM method.
Nursing, society, and health promotion---healing practices: A constructionist historical discourse analysis

The purpose of this discourse analysis of health promotion and healing practices was to describe their functioning historically through practices of governance and risk in the context of neoliberal society. The results portray a constructed subjectivity (identity) among citizens and residents of contemporary society who enact expected health promotion and healing behaviors. Two series of texts were analyzed from a Foucauldian perspective: the Healthy People series from the U.S. Department of Health and Human Services; and the series on Uninsurance published by the Institute of Medicine. The findings generated five themes that comprise the reality of current illness care system rationalities: First, the U.S. illness care system, functioning through technology of insurance or wealth extraction, is dysfunctional as a comprehensive illness care delivery system. Second, health promotion and healing have been subsumed under illness careif they are addressed it is only as discrete indices that comprise compliance monitoring. Third, micro determinants of health (such as behavioral patterns, genetic predispositions, social circumstances, shortfalls in medical care, and environmental exposures), while important, continue to be the single focus of illness care in the U.S. Conversely, macro determinants of health, contingent on macro-level economic and political structures, remain unrecognized as having any bearing on health outcomes. Macro determinants of health frame the configuration of the social infrastructure in which micro determinants of health unfold.
Sexual Risk-Taking among High-Risk Urban Women with and without Histories of Childhood Sexual Abuse: Mediating Effects of Contextual Factors

This study investigated the mechanisms of risk for urban women at high risk for HIV with and without childhood sexual abuse histories. Childhood sexual abuse survivors reported more unprotected intercourse and sexually transmitted infections (STIs). The association of STI locus of control with frequency of unprotected sex was fully mediated by being intoxicated during sex and engaging in sex work, whereas the association between relational control and unprotected sex was not mediated by contextual factors for the childhood sexual abuse group. The mechanisms of risk are different for those with divergent childhood sexual abuse histories and thus interventions should be developed to educate women with a history of childhood sexual abuse about ways to avoid revictimization, particularly within a context of poverty, prostitution, and drug use.
No association between BDNF Val66Met polymorphism and treatment response in obsessive-compulsive disorder in the Japanese population

Aim Brain-derived neurotrophic factor (BDNF) is a member of the neurotrophin family, and it promotes the development and function of dopaminergic and serotonergic neurons. The Met allele of the BDNF Val66Met polymorphism is associated with a decrease in activity-dependent secretion of BDNF compared with the Val allele, and a number of studies have provided evidence for the association between this polymorphism and obsessive-compulsive disorder (OCD). The purpose of this study was to investigate whether this functional variant of the BDNF gene is associated with OCD and treatment response in patients with OCD in the Japanese population. Methods We first performed a case–control association study between the BDNF Val66Met polymorphism and OCD (175 cases and 2,027 controls). Then, we examined an association between this polymorphism and treatment response in 96 patients with OCD. Results We found no significant association between the Met allele and OCD risk or between the Met allele and treatment responses to selective serotonin reuptake inhibitors or serotonin reuptake inhibitor with an atypical antipsychotic (P>0.05). Conclusion Our results suggest that the BDNF Val66Met polymorphism may not be associated as a risk factor for developing OCD or with therapeutic response in patients with OCD in the Japanese population.
A Tale of Two Addiction Theories: the effects of cocaine exposure on cue-induced motivation and action control

Cocaine addiction affects approximately 1.4 million Americans, costing the government billions of dollars and the addicted individual their life. Addiction is characterized by a continued desire to use a drug despite decreased enjoyment from taking it and a desire to abstain. Multiple theories attempt to explain how prolonged drug use induces the chronic brain changes that result in addiction. The incentive sensitization theory suggests that repeated exposure to drugs of abuse alters the neural circuitry that is involved in incentive motivation, the process that allows drugs and their associated stimuli to more strongly encourage drug-seeking behavior. Alternatively, the habit learning theory suggests that drugs of abuse pathologically subvert the reward-learning circuitry, leading to compulsive drug seeking triggered by drug-associated stimuli. Since both theories are based on the idea that drugs of abuse affect the dopaminergic system and alter normal reward processing, it has been proposed that drug exposure may also affect behavior for natural rewards. To understand the mechanisms by which cocaine use modifies reward-seeking behaviors, I have conducted experiments to investigate both addiction theories using cocaine and food rewards as the outcome. I have found that cocaine-paired cues promote cocaine-seeking and taking actions via a Pavlovian motivational mechanism. I have also explored the importance of the contingency of drug delivery and found that cocaine has a general effect on incentive motivation, with both self-administered and experimenter-delivered cocaine enhancing the ability of food-paired cues to motivate food-seeking behavior. This result was not obtained with animals that passively received cocaine infusions, suggesting a role of predictability in the effects of cocaine on incentive motivation. I have also found support for the habit learning theory, demonstrating that cocaine treatment can encourage habitual control of action selection for food rewards, even when feedback is given. However, experimenter-delivered cocaine does not prevent animals from learning to perform a behavior in a goal-directed fashion. Our results lend support to both theories, suggesting that drug induced changes to the dopaminergic systems may be affecting both incentive motivation and reward learning. These results have important implications for our comprehension of the neural processes involved in addiction.
Probe rank approaches for gene selection in oligonucleotide arrays with a small number of replicates

MOTIVATION One major area of interest in analyzing oligonucleotide gene array data is identifying differentially expressed genes. A challenge to biostatisticians is to develop an approach to summarizing probe-level information that adequately reflects the true expression level while accounting for probe variation, chip variation and interaction effects. Various statistical tools, such as MAS and RMA, have been developed to address this issue. In these approaches, the probe level expression data are summarized into gene level data, which are then used for downstream statistical analysis. Since probe variation is often larger than chip variation and there is also a potential interaction effect between probe affinity and treatment effect, strategies such as a gene level analysis, may not be optimal. In this study, we propose a procedure to analyze probe level data for selecting differentially expressed genes under two treatment conditions (groups) with a small number of replicates. The probe level discrepancy between two groups can be measured by a difference of the percentiles of probe perfect-match (PM) ranks or of probe PM weighted ranks. The difference is then compared with a pre-specified threshold to determine differentially expressed genes. The probe level approach takes into account non-homogenous treatment effects and reduces possible cross-hybridization effects across a set of probes. RESULTS The proposed approach is compared with MAS and RMA using two benchmark gene array datasets. Positive predictivity and sensitivity are used for evaluation. Results show the proposed approach has higher positive predictivity and higher sensitivity. AVAILABILITY Available on request from the authors. CONTACT dtchen@uab.edu.
State-dependent alteration in face emotion recognition in depression

Background Negative biases in emotional processing are well recognised in people who are currently depressed but are less well described in those with a history of depression, where such biases may contribute to vulnerability to relapse. Aims To compare accuracy, discrimination and bias in face emotion recognition in those with current and remitted depression. Method The sample comprised a control group (n = 101), a currently depressed group (n = 30) and a remitted depression group (n = 99). Participants provided valid data after receiving a computerised face emotion recognition task following standardised assessment of diagnosis and mood symptoms. Results In the control group women were more accurate in recognising emotions than men owing to greater discrimination. Among participants with depression, those in remission correctly identified more emotions than controls owing to increased response bias, whereas those currently depressed recognised fewer emotions owing to decreased discrimination. These effects were most marked for anger, fear and sadness but there was no significant emotion × group interaction, and a similar pattern tended to be seen for happiness although not for surprise or disgust. These differences were confined to participants who were antidepressant-free, with those taking antidepressants having similar results to the control group. Conclusions Abnormalities in face emotion recognition differ between people with current depression and those in remission. Reduced discrimination in depressed participants may reflect withdrawal from the emotions of others, whereas the increased bias in those with a history of depression could contribute to vulnerability to relapse. The normal face emotion recognition seen in those taking medication may relate to the known effects of antidepressants on emotional processing and could contribute to their ability to protect against depressive relapse.
Clustering to forecast sparse time-series data

Forecasting accurately is essential to successful inventory planning in retail. Unfortunately, there is not always enough historical data to forecast items individually- this is particularly true in e-commerce where there is a long tail of low selling items, and items are introduced and phased out quite frequently, unlike physical stores. In such scenarios, it is preferable to forecast items in well-designed groups of similar items, so that data for different items can be pooled together to fit a single model. In this paper, we first discuss the desiderata for such a grouping and how it differs from the traditional clustering problem. We then describe our approach which is a scalable local search heuristic that can naturally handle the constraints required in this setting, besides being capable of producing solutions competitive with well-known clustering algorithms. We also address the complementary problem of estimating similarity, particularly in the case of new items which have no past sales. Our solution is to regress the sales profile of items against their semantic features, so that given just the semantic features of a new item we can predict its relation to other items, in terms of as yet unobserved sales. Our experiments demonstrate both the scalability of our approach and implications for forecast accuracy.