Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Astronomical Communities Through Co-readership Analysis

Whenever a large group of people are engaged in an activity, communities will form. The nature of these communities depends on the relationship considered. In the group of people who regularly use scholarly literature, a relationship like ``person i and person j have cited the same paper'' might reveal communities of people working in a particular field. On this poster, we will investigate the relationship ``person i and person j have read the same paper''. Using the data logs of the NASA/Smithsonian Astrophysics Data System (ADS), we first determine the population that will participate by requiring that a user queries the ADS at a certain rate. Next, we apply the relationship to this population. The result of this will be an abstract ``relationship space'', which we will describe in terms of various ``representations''. Examples of such ``representations'' are the projection of co-read vectors onto Principal Components and the spectral density of the co-read network. We will show that the co-read relationship results in structure, we will describe this structure and we will provide a first attempt in the classification of this structure in terms of astronomical communities. The ADS is funded by NASA Grant NNG06GG68G.
Why do we lose protected areas? Factors influencing protected area downgrading, downsizing and degazettement in the tropics and subtropics

Protected areas (PAs) are an essential tool for the conservation of biodiversity globally. Previous studies have focussed on the effectiveness of PAs and the design of optimal PA networks. However, not all PAs remain intact permanently; many PAs undergo downgrading, downsizing and/or degazettement (PADDD), a fact largely ignored until recently. The drivers of enacted PADDD events and the factors influencing its spatial occurrence are poorly understood, potentially undermining the efficacy of PAs and PA networks. Here we examine the spatial relationship between PADDD and economic, demographic and structural variables, using a 110‐year data set of 342 enacted PADDD events across 44 countries in the tropics and subtropics. We find that the probability of an enacted PADDD event increases with the size of the PA and through a synergistic interaction between PA size and local population densities. Our results are robust to the under‐reporting of enacted PADDD events that occur among smaller PAs and in regions with lower population density. We find an economic motive for PADDD events, given that the opportunity costs associated with larger PAs are higher, on average, than smaller PAs. Our findings suggest a need for conservation practitioners to better consider PA characteristics, as well as the social, economic and political context in which PAs are situated, to aid the creation of more efficient and sustainable PA networks. In particular, the dynamics of enacted PADDD events highlight the need to explicitly consider PA robustness as a core component of systematic conservation planning for PA networks.
A framework of the factors affecting the evolution of performance measurement systems

The effectiveness of performance measurement is an issue of growing importance to industrialists and academics alike. Many organisations are investing considerable amounts of resource implementing measures that reflect all dimensions of their performance. Consideration is being given to what should be measured today, but little attention is being paid to the question of what should be measured tomorrow. Measurement systems should be dynamic. They have to be modified as circumstances change. Yet few organisations appear to have systematic processes in place for managing the evolution of their measurement systems and few researchers appear to have explored the question, what shapes the evolution of an organisation’s measurement system? The research reported in this paper seeks to address this gap in the literature by presenting data that describes the forces that shape the evolution of the measurement systems used by different organisations.
Image summaries using database saliency

It is useful to have a small set of representative images from a database of thousands of images to summarize its content. There are two key aspects of such image summaries: how to generate them and how to present them. We address both issues. We extend the idea of image saliency to databases and introduce the notion of database saliency. We argue that in image databases, there are certain images that are more uncommon or salient than others and therefore are more interesting. We compute the database saliency value of an image as its total distance from all the pre-defined cluster centers of the database. We demonstrate the use of database saliency in two visualization applications: creating image collages and mosaics using automatically chosen salient images.
Rendering and animating expressive caricatures

We present a novel approach for generating stylized artistic rendering of caricatures from a given face image, with the ability to map any one of the main six expressions and control the degree of its expressiveness on the generated caricature. Our method achieves this by manipulating the facial appearance and expressivity of the caricature, using quadratic deformation model representations of facial expressions. A stroke-based non-photorealistic rendering (NPR) engine is developed to generate the output caricature that appears to be a sketch of the original image. The NPR engine segments the defined rendering path and generates rectangular strokes that approximate each segment. The stroke properties are computed by applying geometric image moments on each segment. To enhance the overall appearance of the caricature, extra strokes are randomly positioned around the rendering path. The results show that from an input neutral facial image, expressive rendered caricatures are generated for the facial expressions neutral, smile, sad, fear, surprise, disgust, and anger. Our approach produces very expressive artistically rendered caricatures, and could lead to future research directions in further extending the method to produce pencil sketch rendering of images or rendering 3D characters to generate cartoon-like 3D characters.
A Joining Shortest Queue with MAP Inputs

In this paper we study a MAP/M/K system with join-the-shortest-queue (JSQ) disci- pline which consists of K servers and K queues with infinite buffers. An arrival joins the shortest queue and in the case that there are n (n ≤ K) queues having the same shortest length, the customer will join any one of these shortest queues with probability 1/n. We model the system as a level- expanding QBD (LDQBD) process with expanding-block-structured generator matrix by defining the sum of the multi-queue lengths as the level. This method enable us to quantify the regular queue- ing measures, including stationary joint queue length distribution and sojourn time distribution. We also compare the effect of JSQ and randomly-join-queue.
A Dry EEG-System for Scientific Research and Brain–Computer Interfaces

Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain–computer interfacing (BCI), EEG recently has become a tool to enhance human–machine interaction. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, event-related potentials (ERP) were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.
Pharmacodynamic biomarkers in model-based drug development in oncology.

INTRODUCTION Model-based drug development (MBDD) is recognized as an initiative able to improve success rates in the development of new anti-cancer agents. The use of pharmacodynamic (PD) biomarkers may be valuable in this context. The implementation of biomarkers in MBDD in oncology is the subject of this review. METHODS Literature was searched for articles and relevant conference abstract concerning application of biomarkers in MBDD in oncology. First, papers are discussed concerning the use of biomarkers in modeling and simulation analyses in preclinical and early clinical phases of drug development. Subsequently, articles concerning late-stage clinical drug development are discussed. RESULTS Only a limited set of articles and conference presentations were identified. As expected, the majority of publications are concerned with targeted anti-cancer drugs. In the early development of novel anti-cancer agents, most publications are concerned to the evaluation of dosing regimens for further clinical evaluation, or the identification of the required levels of target modulation. In general, combined analysis of clinical and preclinical data provide the most informative analyses. The use of biomarkers in late-stage drug development has mainly been confined to the prediction of phase III outcome on the basis of tumor growth data obtained from phase II trials, with tumor growth as biomarker for outcome. CONCLUSION The use of suitable biomarkers in MBDD, has shown its merits in oncology, especially in early clinical development. Considering the low number of reports in literature, we would propose a more active use of presented techniques during all developmental phases of new anticancer agents.
Modernising historical Slovene words

Abstract We propose a language-independent word normalisation method and exemplify it on modernising historical Slovene words. Our method relies on character-level statistical machine translation (CSMT) and uses only shallow knowledge. We present relevant data on historical Slovene, consisting of two (partially) manually annotated corpora and the lexicons derived from these corpora, containing historical word–modern word pairs. The two lexicons are disjoint, with one serving as the training set containing 40,000 entries, and the other as a test set with 20,000 entries. The data spans the years 1750–1900, and the lexicons are split into fifty-year slices, with all the experiments carried out separately on the three time periods. We perform two sets of experiments. In the first one – a supervised setting – we build a CSMT system using the lexicon of word pairs as training data. In the second one – an unsupervised setting – we simulate a scenario in which word pairs are not available. We propose a two-step method where we first extract a noisy list of word pairs by matching historical words with cognate modern words, and then train a CSMT system on these pairs. In both sets of experiments, we also optionally make use of a lexicon of modern words to filter the modernisation hypotheses. While we show that both methods produce significantly better results than the baselines, their accuracy and which method works best strongly correlates with the age of the texts, meaning that the choice of the best method will depend on the properties of the historical language which is to be modernised. As an extrinsic evaluation, we also compare the quality of part-of-speech tagging and lemmatisation directly on historical text and on its modernised words. We show that, depending on the age of the text, annotation on modernised words also produces significantly better results than annotation on the original text.
How do particle physicists learn the programming concepts they need?

The ability to read, use and develop code efficiently and successfully is a key ingredient in modern particle physics. We report the experience of a training program, identified as “Advanced Programming Concepts”, that introduces software concepts, methods and techniques to work effectively on a daily basis in a HEP experiment or other programming intensive fields. This paper illustrates the principles, motivations and methods that shape the “Advanced Computing Concepts” training program, the knowledge base that it conveys, an analysis of the feedback received so far, and the integration of these concepts in the software development process of the experiments as well as its applicability to a wider audience.
In vivo confocal reflectance microscopy in melanoma

Reflectance confocal microscopy (RCM) enables the noninvasive in vivo imaging of the skin with a horizontal axis and a cellular‐level resolution allowing the study of the skin from superficial layers to papillary dermis. It has arisen an important tool in the study of tumors and specially an important role in the characterization of melanoma. Melanocytic lesions present a large number of characteristic findings visible in upper parts of the tumors, such as in the case of melanoma: pagetoid roundish or dendritic cells in superficial epidermis, atypical nests at the dermoepidermal junction, nonedged papillae and atypical nucleated cells in papillary dermis. Several studies have demonstrated that RCM may improve the accuracy in the differentiation of benign and malignant melanocytic lesions as an adjuvant technique to dermoscopy, and three main algorithms have been developed to apply in equivocal lesions. The advantage of in vivo observation in real time of the tumor at the bedside is opening the clinical applications of RCM in the evaluation of melanocytic lesions, and in particular in the study of facial maculae and lentigo maligna melanoma, amelanotic melanoma, and management of subclinical margins, recurrences, or monitoring noninvasive treatment of tumors.
Some issues on dynamic control of parallel kinematic machines

In this paper, a discussion on parallel kinematic machine dynamic control shows that such a control has to be thought over again. Hence, the well-known computed torque control approach is revisited and is shown, when it is performed in the Cartesian space and including a Cartesian space dynamic model, to be definitely relevant for parallel kinematic machines. Moreover, it is shown that greater improvements can be expected with an exteroceptive measure of the end-effector p ose. Finally, experimental results on a complex prototype give a comparison between a linear single-axis control and a computed torque control to prove our assertions.
Cross media topic analytics based on synergetic content and user behavior modeling

Hot topic in cross media, defined as a set of Web documents containing similar semantic information, describes the same real world event with significant social impact. However, cross media topic detection still remains a challenging issue since it is unclear how users interact with the cross media topics, leading to difficulties in identifying the influence of different topics on different user communities. In this paper, we propose a solution framework for cross media topic analysis based on synergetic modeling of multi-modal content and user behavior. First, we detect atom topics by multi-modal topic detection. Second, we propose a multi-resolution user behavior modeling method to discover communities on the active Web users by considering the distribution of related atom topics along the temporal axis. We analyze the topic-topic, topic-community and community-community relations by using the proposed method. Consequently, the macro-topic and macro-community structures can be obtained for better understanding of the interaction between topics and communities. Experiments show the capability of our method in knowledge discovery on cross media topics.
Dance training system using foot mounted sensors

This paper presents a foot mounted system with an Inertial Sensor Unit (IMU) and two proximity sensors for dance training. With the IMU data, the movement of the foot is tracked by an Inertial Navigation (IN) algorithm using Kalman filter. To improve the estimation, the measurements from the proximity sensors and the zero velocity are used to update the position of the foot. Since dancing requires rhythmic and positional accuracy, the orientation and position of the foot are used to analyze the trainee's movement. Along with the foot's pose, the timing in pacing rhythm is also recognized by detecting steps and step moving time.
Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams

A general framework and a holistic concept are proposed in this paper that combine computationally light machine learning from streaming data with the online identification and adaptation of dynamic systems in regard to their structure and parameters. According to this concept, the system is assumed to be decomposable into a set of fuzzily connected simple local models. The main thrust of this paper is in the development of an original approach for the self-design, self-monitoring, self-management, and self-learning of such systems in a dynamic manner from data streams which automatically detect and react to the shift in the data distribution by evolving the system structure. Novelties of this contribution lie in the following: 1) the computationally simple approach (simpl_e_Clustering-simplified evolving Clustering) to data space partitioning by recursive evolving clustering based on the relative position of the new data sample to the mean of the overall data, 2) the learning technique for online structure evolution as a reaction to the shift in the data distribution, 3) the method for online system structure simplification based on utility and inputs/feature selection, and 4) the novel graphical illustration of the spatiotemporal evolution of the data stream. The application domain for this computationally efficient technique ranges from clustering, modeling, prognostics, classification, and time-series prediction to pattern recognition, image segmentation, vector quantization, etc., to more general problems in various application areas, e.g., intelligent sensors, mobile robotics, advanced manufacturing processes, etc.
Exercise Is More Effective at Altering Gut Microbial Composition and Producing Stable Changes in Lean Mass in Juvenile versus Adult Male F344 Rats

The mammalian intestine harbors a complex microbial ecosystem that influences many aspects of host physiology. Exposure to specific microbes early in development affects host metabolism, immune function, and behavior across the lifespan. Just as the physiology of the developing organism undergoes a period of plasticity, the developing microbial ecosystem is characterized by instability and may also be more sensitive to change. Early life thus presents a window of opportunity for manipulations that produce adaptive changes in microbial composition. Recent insights have revealed that increasing physical activity can increase the abundance of beneficial microbial species. We therefore investigated whether six weeks of wheel running initiated in the juvenile period (postnatal day 24) would produce more robust and stable changes in microbial communities versus exercise initiated in adulthood (postnatal day 70) in male F344 rats. 16S rRNA gene sequencing was used to characterize the microbial composition of juvenile versus adult runners and their sedentary counterparts across multiple time points during exercise and following exercise cessation. Alpha diversity measures revealed that the microbial communities of young runners were less even and diverse, a community structure that reflects volatility and malleability. Juvenile onset exercise altered several phyla and, notably, increased Bacteroidetes and decreased Firmicutes, a configuration associated with leanness. At the genus level of taxonomy, exercise altered more genera in juveniles than in the adults and produced patterns associated with adaptive metabolic consequences. Given the potential of these changes to contribute to a lean phenotype, we examined body composition in juvenile versus adult runners. Interestingly, exercise produced persistent increases in lean body mass in juvenile but not adult runners. Taken together, these results indicate that the impact of exercise on gut microbiota composition as well as body composition may depend on the developmental stage during which exercise is initiated.
How CAGE, RAPS4-QF, and AUDIT Can Help Practitioners for Patients Admitted with Acute Alcohol Intoxication in Emergency Departments?

Aims: To help clinicians to identify the severity of alcohol use disorders (AUDs) from optimal thresholds found for recommended scales. Especially, taking account of the high prevalence of alcohol dependence among patients admitted to the emergency department (ED) for acute alcohol intoxication (AAI), we propose to define thresholds of severity of dependence based on the AUDIT score. Methods: All patients admitted to the ED with AAI (blood alcohol level >0.8 g/L), in a 2-month period, were assessed using the CAGE, RAPS-QF, and AUDIT, with the alcohol dependence/abuse section of the mini international neuropsychiatric interview (MINI) used as the gold standard. To explore the relation between the AUDIT and the MINI the sum of the positive items on the MINI (dependence) as a quantitative variable and as an ordinal parameter were analyzed. From the threshold score found for each scale we proposed intervals of severity of AUDs. Results: The mean age of the sample (122 males, 42 females) was 46 years. Approximately 12% of the patients were identified with alcohol abuse and 78% with dependence (DSM-IV). Cut points were determined for the AUDIT in order to distinguish mild and moderate dependence from severe dependence. A strategy of intervention based on levels of severity of AUD was proposed. Conclusion: Different thresholds proposed for the CAGE, RAPS4-QF, and AUDIT could be used to guide the choice of intervention for a patient: brief intervention, brief negotiation interviewing, or longer more intensive motivational intervention.
Process mining: discovering workflow models from event-based data

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we propose a technique for process mining. This technique uses workflow logs to discover the workflow process as it is actually being executed. The process mining technique proposed in this paper can deal with noise and can also be used to validate workflow processes by uncovering and measuring the discrepancies between prescriptive models and actual process executions