Miracle-dz/UniFAR-FTUs huggingface dataset:
Network flow-based power optimization under timing constraints in MSV-driven floorplanning

Power consumption has become a crucial problem in modern circuit design. Multiple Supply Voltage (MSV) design is introduced to provide higher flexibility in controlling the power and performance trade-off. One important requirement of MSV design is that timing constraints of the circuit must be satisfied after voltage assignment of the cells. In this paper, we will show that the voltage assignment task on a given netlist can be formulated as a convex cost dual network flow problem and can be solved optimally in polynomial time using a cost-scaling algorithm when the delay choices of each module are continuous in the real or integer domain. We can make use of this approach to obtain a feasible voltage assignment solution in the general cases with power consumption approximating the minimum one. Furthermore, we will propose a framework to optimize power consumption and physical layout of a circuit simultaneously during the floorplanning stage, by embedding this cost-scaling solver into a simulated annealing based floorplanner. This is effective in practice due to the short running time of the solver. We compared our approach with the latest work [9] on the same problem, and the experimental results show that, using our framework, significant improvement on power saving (18% less power cost on average) can be achieved in much less running time (7times faster on average) for all the test cases, which confirms the effectiveness of our approach.
Finding flows in the one-way measurement model

The one-way measurement model is a framework for universal quantum computation in which algorithms are partially described by a graph G of entanglement relations on a collection of qubits. A sufficient condition for an algorithm to perform a unitary embedding between two Hilbert spaces is for the graph G, together with input and output I, O vertices I,O is contained in V(G), to have a flow in the sense introduced by Danos and Kashefi [Phys. Rev. A 74, 052310 (2006)]. For the special case of |I|=|O|, using a graph-theoretic characterization, I show that such flows are unique when they exist. This leads to an efficient algorithm for finding flows by a reduction to solved problems in graph theory.
Extending a graphical toolkit for two-handed interaction

Multimodal interaction combines input from multiple sensors such as pointing devices or speech recognition systems, in order to achieve more fluid and natural interaction. Two-handed interaction has been used recently to enrich graphical interaction. Building applications that use such combined interaction requires new software techniques and frameworks. Using additional devices means that user interface toolkits must be more flexible with regard to input devices and event types. The possibility of parallel interactions must also be taken into account, with consequences on the structure of toolkits. Finally, frameworks must be provided for the combination of events and status of several devices. This paper reports on the extensions we made to the direct manipulation interface toolkit Whizz in order to experiment two-handed interaction. These extensions range from structural adaptations of the toolkit to new techniques for specifying the time-dependent fusion of events.
Fundamental traffic diagrams of elementary road networks

The fundamental traffic diagram gives the relation between the flow and the density of vehicles on a road. This diagram has been obtained for one circular road in the deterministic and stochastic cases. In this paper we revisit the simplest example of two roads and one crossing given in [2] and we study the interaction between crossings on a system of four roads with two crossings. After showing numerically the existence of an average flow, we compute the fundamental diagram of the system and we study its various phases. We compare the fundamental diagrams obtained with different crossing policies (right priority, open-loop and feedback traffic light). We show that the “right priority” policy blocks the system at a specific vehicle density. This jam is explained by the appearance of a road circuit full of vehicles. Finally we show the important influence of the crossing design on the fundamental diagram.
Neuropathological approaches to cerebral aging and neuroplasticity

Cerebral aging is a complex and heterogenous process related to a large variety of molecular changes involving multiple neuronal networks, due to alterations of neurons (synapses, axons, dendrites, etc), particularly affecting strategically important regions, such as hippocampus and prefrontal areas. A substantial proportion of nondemented, cognitively unimpaired elderly subjects show at least mild to moderate, and rarely even severe, Alzheimer-related lesions, probably representing asymptomatic preclinical Alzheimer's disease, and/or mixed pathologies. While the substrate of resilience to cognitive decline in the presence of abundant pathologies has been unclear, recent research has strengthened the concept of cognitive or brain reserve, based on neuroplasticity or the ability of the brain to manage or counteract age-related changes or pathologies by reorganizing its structure, connections, and functions via complex molecular pathways and mechanisms that are becoming increasingly better understood. Part of neuroplasticity is adult neurogenesis in specific areas of the brain, in particular the hippocampal formation important for memory function, the decline of which is common even in “healthy” aging. To obtain further insights into the mechanisms of brain plasticity and adult neurogenesis, as the basis for prevention and potential therapeutic options, is a major challenge of modern neurosciences.
Nursing on the line: Experiences from England and Quebec (Canada)

Some industries, like telecommunications, finance and retail, have been operating call centres for several years and there has been growing academic interest in exploring the work experience of employees taking calls and answering telephone queries assisted by computers. Nurses who staff telephone centres in the healthcare industry are different from their counterparts in other fields. These are highly qualified workers with a strong occupational identity and distinct spheres of competence. However, occupation is not a constant as a mediator of technology and it is argued that the extent to which nurses were able to shape their call centre work differed cross-nationally owing to the simultaneous interplay of different societal constructions of nursing and the national-historical development of tele-health centres. This article uses cross-national qualitative case study research to examine the different effects of occupation, nation and timing of industry formation on the design and experience of call centre work in the healthcare industry.
An Anomaly Detection Scheme Based on Machine Learning for WSN

Security is one of the most important research issues in wireless sensor network (WSN). A Machine Learning (ML) based anomaly detection scheme is proposed, where Bayesian classification algorithm is used to detect anomalous nodes. By the tool NS2, a small number of samples are given and learned, and intrusion detection rules are built, network attack traffic is generated and simulated. And based on this, its detection rate, average detection rate, false positive rate and average false positive rate are evaluated. Experimental results demonstrate that the scheme achieves higher accuracy rate of detection and lower false positive rate than the current important intrusion detection schemes of WSN.
Interactions between Nitrogen and Silicon in Rice and Their Effects on Resistance toward the Brown Planthopper Nilaparvata lugens

Nitrogen (N) and silicon (Si) are two important nutritional elements required for plant growth, and both impact host plant resistance toward insect herbivores. The interaction between the two elements may therefore play a significant role in determining host plant resistance. We investigated this interaction in rice (Oryza sativa L.) and its effect on resistance to the herbivore brown planthopper Nilaparvata lugens (BPH). Our results indicate that high-level (5.76 mM) N fertilization reduced Si accumulation in rice leaves, and furthermore, this decrease was likely due to decreased expression of Si transporters OsLsi1 and OsLsi2. Conversely, reduced N accumulation was observed at high N fertilization levels when Si was exogenously provided, and this was associated with down-regulation of OsAMT1;1 and OsGS1;1, which are involved in ammonium uptake and assimilation, respectively. Under lower N fertilization levels (0.72 and/or 1.44 mM), Si amendment resulted in increased OsNRT1:1, OsGS2, OsFd-GOGAT, OsNADH-GOGAT2, and OsGDH2 expression. Additionally, bioassays revealed that high N fertilization level significantly decreased rice resistance to BPH, and the opposite effect was observed when Si was provided. These results provide additional insight into the antagonistic interaction between Si and N accumulation in rice, and the effects on plant growth and susceptibility to herbivores.
“There Are Too Many, but Never Enough": Qualitative Case Study Investigating Routine Coding of Clinical Information in Depression

Background We sought to understand how clinical information relating to the management of depression is routinely coded in different clinical settings and the perspectives of and implications for different stakeholders with a view to understanding how these may be aligned. Materials and Methods Qualitative investigation exploring the views of a purposefully selected range of healthcare professionals, managers, and clinical coders spanning primary and secondary care. Results Our dataset comprised 28 semi-structured interviews, a focus group, documents relating to clinical coding standards and participant observation of clinical coding activities. We identified a range of approaches to coding clinical information including templates and order entry systems. The challenges inherent in clearly establishing a diagnosis, identifying appropriate clinical codes and possible implications of diagnoses for patients were particularly prominent in primary care. Although a range of managerial and research benefits were identified, there were no direct benefits from coded clinical data for patients or professionals. Secondary care staff emphasized the role of clinical coders in ensuring data quality, which was at odds with the policy drive to increase real-time clinical coding. Conclusions There was overall no evidence of clear-cut direct patient care benefits to inform immediate care decisions, even in primary care where data on patients with depression were more extensively coded. A number of important secondary uses were recognized by healthcare staff, but the coding of clinical data to serve these ends was often poorly aligned with clinical practice and patient-centered considerations. The current international drive to encourage clinical coding by healthcare professionals during the clinical encounter may need to be critically examined.
Semantic Enabled Recommender System for Micro-Blog Users

Quite a number of recent works have concentrated on the task of recommending to Twitter users whom they should follow, among which, the WTF (Who To Follow) service provided by Twitter. Recommenders are based either on the user's network structure, or on some notion of topical similarity with other users, or on both. We present a method for analysis of Twitter users supported by a hierarchical representation of their interests, which we call a Twixonomy. The use of Twixonomy casts both problems of user classification and recommendation as one of itemset mining, where items are either users' authoritative friends or semantic categories associated to friends. In addition to evaluating our profiler and recommender on several populations, we also show that semantic categories allow for very fine-grained population studies, and make it possible to recommend not only whom to follow, but also topics of interest, users interested in the same topic, and more.
Results from a nationwide postmarketing cohort study of patients in Sweden treated with etanercept

Objectives: To describe a nationwide system for postmarketing follow up of new antirheumatic drugs in Sweden, and to analyse safety and effectiveness in an etanercept treated patient cohort. Methods: Etanercept became available in Sweden for prescribing on a named patient basis in 1999. All patients treated were included in a follow up of intensified adverse event reporting and recording of clinical outcome during 24 months, according to the EULAR core set. Results: The mean (SD) disease activity score (DAS 28) value at inclusion among 820 patients recruited on a named patient basis during year 1 was 5.99 (1.19). After two years, 21% (n = 172) of these patients had discontinued the treatment. Of the remaining 648 patients, 68% (n = 442) responded to the treatment. However, in 55% of the responders, the disease activity was intermediate or high (mean DAS 28, 3.37 (1.20)). In all, 540 adverse events were reported in 421 adverse drug reaction (ADR) reports, in 294 patients. The events in 80 reports (19%) were serious. Twenty two per cent of the events were infections, of which 24% (n = 29) were serious. The incidence of serious adverse events remained constant over time. Conclusions: At start of etanercept treatment, patients had high disease activity. Activity remained high in a large proportion of the responding patients. Although serious ADRs occurred during late phases of treatment, no unexpected safety problems arose. No specific indicators of ADR risk were found. The monitoring system that was established may be useful in future postmarketing surveillance.
How Person-Centric Traits Moderate the Influence of Needs Satisfaction on Player Engagement with Games

Whether or not playing video games results in a positive play experience depends on many factors, including aspects of the player that are generally not considered. We present an approach for understanding how several player-centric traits alter the relationship between needs satisfaction and player engagement. Results from three case studies on personality, motivation, and self-esteem show how person-centric traits must be considered in combination with the satisfaction of needs to ultimately predict game engagement. Author
AutoConnect

We present AutoConnect, an automatic method that creates customized, 3D-printable connectors attaching two physical objects together. Users simply position and orient virtual models of the two objects that they want to connect and indicate some auxiliary information such as weight and dimensions. Then, AutoConnect creates several alternative designs that users can choose from for 3D printing. The design of the connector is created by combining two holders, one for each object. We categorize the holders into two types. The first type holds standard objects such as pipes and planes. We utilize a database of parameterized mechanical holders and optimize the holder shape based on the grip strength and material consumption. The second type holds free-form objects. These are procedurally generated shell-gripper designs created based on geometric analysis of the object. We illustrate the use of our method by demonstrating many examples of connectors and practical use cases.
Debating early modern and modern memory: Cultural forms and effects: a critical retrospective

This discussion focuses upon the ways in which early modern and modern cultural debate examines memory both in terms of its functions and nature as human faculty and of its effects as a cultural phenomenon. It seeks to uncover some of the striking synergies as well as the contrary motions in the vigorous cultural debates surrounding the reflex to remember and its implications for various target audiences. Of particular interest will be the ways in which memory was and is pressed into service to forge critical narratives of origin and belonging at both a personal and collective level, notably with reference to Shakespeare’s history plays. Discussion ranges across a number of early modern textual genres (e.g. correspondence, drama, epic poetry, historiography, devotional writing) to probe the prevailing cultural expectations surrounding the exercise of recollection and the consequences of the failure to perform such duties.
An examination of attribute trade and weight stigma in online dating

Weight stigma refers to biased treatment or attitudes based on weight. This has been documented to occur in a variety of settings (including relationships) and can result in many negative consequences, but its impact in the contemporary online dating arena is largely unexplored. Therefore, Study 1 of this project examined who experiences weight stigma in online dating and what factors predicted weight stigma. It was hypothesized that a) women would be more likely than men to experience weight stigma; b) compassion, beliefs about obese persons, attitudes toward obese persons, social dominance orientation, narcissism, objectification, selfclassification of overweight status, and internalized weight bias would predict likelihood of engaging in weight bias. Study 2 examined attribute trade theory in weight stigma and online dating. It was hypothesized that a) women would need more positive attributes to mitigate overweight status than would men; b) compassion, beliefs about obese persons, attitudes toward obese persons, social dominance orientation, narcissism, objectification, self-classification of overweight status, and internalized weight bias would moderate the relationship between the proportion of expected profile “hits” and the number of factors present that may potentially mitigate weight bias. Data were analyzed via logistic and multiple regressions. In Study 1, variables that predicted weight stigma included anonymous feedback condition, photo weight status, female gender of participants, and thinking of oneself as overweight. In a final regression model, self-classified weight, feedback condition, attitudes toward obese persons, and selfobjectification predicted weight bias above and beyond photo weight status. In Study 2, there were no interaction effects, but there were main effects for mitigating factors, beliefs about obese persons, self-objectification, attitudes toward obese persons, and photo gender. Findings illustrated that weight bias appears to occur in online dating scenarios, though some factors may help offset the weight status of the individual.
The effects of ability grouping on the learning of children from low income homes: a systematic review

This paper explores the relationship between socio-economic status, ability grouping and children’s educational outcomes. The study takes the form of a systematic review to examine available evidence-based articles which are selected by following a specific set of inclusion criteria. Articles are then analysed in terms of the research questions which look to examine the relationship between children’s socio-economic background and ability group placement, and the impact of ability grouping on the learning (cognitive and behavioural) of children from low socio-economic households. The review suggests that children from lower socio-economic status backgrounds are more likely to be placed in lower ability groups, sets or streams. This placing is proven to have an overall negative impact on behaviour, as well as failing to close the achievement gap as children in lower groups make slower cognitive progress than children in other groups. Furthermore the general consensus arising from the literature is that ability grouping does not have the positive evidence base to support its use in primary classrooms. Especially when concerning the learning of children from lower socio-economic status backgrounds. Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}
A Survey of Multi-Objective Sequential Decision-Making

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work.
Evaluating the Performance of Two Visual Descriptors Techniques for a Humanoid Robot

A humanoid robot capable of playing soccer needs to know where opponents and team mates are in the soccer field. The robot has to be able to recognize team mates and opponents, inferring information such as distance and estimated location of the other robots. In order to achieve this key requisite, this paper analyze two descriptor algorithms, HAAR and HOG, so that one of them can be used for recognizing humanoid robots with less false positives alarms and with best frame per second rate. They were used with their respective classical classifiers, AdaBoost and SVM. As many different robots are available in RoboCup domain, the descriptor needs to describe features in a way that they can be distinguished from the background at the same time the classification has to have a good generalization capability. Although some limitations appeared in tests, the results were beyond expectations.
On some unresolved issues in finding optimum distributed detection schemes

Optimum distributed detection under the Neyman-Pearson (NP) criterion is considered for a general case with possibly dependent observations from sensor to sensor. The focus is on the parallel architecture. New necessary conditions are presented that relate the threshold used in the NP-optimum fusion rule to those used in the NP-optimum sensor rules. These results clearly illustrate that the necessary conditions for NP optimality have exactly the same form as those for Bayes optimality. Based on these conditions, a new algorithm for finding NP optimum distributed detection schemes is developed. The algorithm allows randomization at the fusion center, which we show is generally needed to achieve optimality. The algorithm allows one to attempt to optimize the fusion rule along with the sensor rules or to find the best schemes among those using each of a set of fixed possible fusion rules.