Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of daVinci Robot ® in U . S . and Worldwide

130. 9. Guillonneau B, Vallencien G. Laparoscopic radical prostatectomy: the Montsouris technique. J Urol 2000; 163: 1643–9. 10. Abbou CC, Salomon L, Hoznek A, Antiphon P, Cicco A, Saint F, Alame W, Bellot J, Chopin D. Laparoscopic radical prostatectomy: preliminary results. Urology 2000; 55: 630–4. 11. Abbou CC, Hoznek A, Salomon I, Olsson L, Lobontiu A, Saint F, Cicco A, Antiphon P, Chopin D. Laparoscopic radical prostatectomy with a remote controlled robot. J Urol 2001; 165: 1964–6. 12. Menon M, Tewari A, members of the Vattikuti Institute Prostatectomy Team. Robotic radical prostatectomy and the Vattikuti Urology Institute technique; an interim analysis of results and technical points. Urology 2003; 61 (suppl 4A): 15–20. 13. Tewari A, Srivastava A, Menon M, members of the Vattikuti Institute Prostatectomy Team. A prospective comparison of radical retropubic prostatectomy and robot-assisted prostatectomy: experience in one institution. BJU Int 2003; 92: 205–10. 14. Guillonneau B, El-Fettouh H. Laparoscopic radical prostatectomy: oncological evaluation after 1000 cases t Montsouris institute. J Urol 2003; 169: 1261–6. 15. Walsh P, Marschke P, Rinker D, Burnett A. Patient reported urinary continence and sexual dysfunction after anatomic radical prostatectomy. Urology 2000; 55: 58–61. 16. Kumar R, Hemal A, Menon M. Robotic renal and adrenal surgery: present and future. BJU Int 2005; 96: 244–9. 17. Costello A. Beyond Marketing: The real value of robotic radical prostatectomy, BJU Int 2005; 96: 1–6. 18. Scales C, Jones P, Eisenstein E, Perminger G, Albala D. Local cost structures and economics of robot assisted radical prostatectomy. J Urol 2005; 174: 2323–9. 19. Aherling T, Skarecky D, Lee D, Clayman R. Successful transfer of open surgical skills to a laparoscopic environment using a robotic interface: initial experience with laparoscopic radical prostatectomy. J Urol 2003; 169 (suppl): A1649. 20. Menon M, Shrivastava A, Tewari A, Sarle R, Hemal A, Peabody J, Vallancien G. Laparoscopic and robot assisted radical prostatectomy: establishment of a structured program and preliminary analysis of outcomes. J Urol 2002; 168: 945–7. 21. Shalhav A, Dabagia M, Wagner T, Koch M, Lingeman J. Training postgraduate urologists in laproscopic surgery: The current challenge. J Urol 2002; 167: 2135–7. JOURNAL FÜR UROLOGIE UND UROGYNÄKOLOGIE ANTWORTFAX Hiermit bestelle ich ein Jahresabonnement (mindestens 4 Ausgaben) zum Preis von ¤ 36,– (Stand 1.1.2008) (im Ausland zzgl. Versandspesen) Name
APPLICATION OF FINITE ELEMENT ANALYSIS TO EVALUATE IMPLANT FRACTURES

Statement of problem. Higher fracture rates were reported for Branemark implants placed in the maxilla and for 3.75 mm diameter implants installed in the posterior region. Purpose. The purpose of this study was to investigate the fracture of a fixture by finite element analysis and to compare different diameter of fixtures according to the level of alveolar bone resorption. Material and Methods. The single implant and prosthesis was modeled in accordance with the geometric designs for the 3i implant systems. Models were processed by the software programs HyperMesh and ANSA. Three-dimensional finite element models were developed for; (1) a regular titanium implant 3.75 mm in diameter and 13 mm in length (2) a regular titanium implant 4.0 mm in diameter and 13 mm in length (3) a wide titanium implant 5.0 mm in diameter and 13 mm in length each with a cementation type abutment and titanium alloy screw. The abutment screws were subjected to a tightening torque of 30 Ncm. The amount of preload was hypothesized as 650 N, and round and flat type prostheses were 12 mm in diameter, 9 mm in height were loaded to 600 N. Four loading offset points (0, 2, 4, and 6 mm from the center of the implants) were evaluated. To evaluate fixture fracture by alveolar bone resorption, we investigated the stress distribution of the fixtures according to different alveolar bone loss levels (0, 1.5, 3.5, and 5.0 mm of alveolar bone loss). Using these 12 models (four degrees of bone loss and three implant diameters), the effects of loading offset, the effect of alveolar bone resorption and the size of fixtures were evaluated. The PAM-CRASH 2G simulation software was used for analysis of stress. The PAM-VIEW and HyperView programs were used for post processing. Results. The results from our experiment are as follows: 1. Preload maintains implant-abutment joint stability within a limited offset point against occlusal force. 2. Von Mises stress of the implant, abutment screw, abutment, and bone was decreased with increasing of the implant diameter. 3. With severe advancing of alveolar bone resorption, fracture of the 3.75 and the 4.0 mm diameter implant was possible. 4. With increasing of bending stress by loading offset, fracture of the abutment screw was possible.
Meliorating usable document density for online event detection

Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are at times unusable as independent documents, as they may replace previously appeared noun phrases with pronouns, leading OED systems to fail while grouping these replies to their suitable clusters. In this paper, a pronoun resolution system that tries to replace pronouns with relevant nouns over social media data is proposed. Results show significant improvement in performance using the proposed system.
Mechanisms to improve the efficiency of hardware data prefetchers

A well known performance bottleneck in computer architectu re is the so-called memory wall. This term refers to the huge disparity between on-c hip and off-chip access latencies. Historically speaking, the operating frequenc y of processors has increased at a steady pace, while most past advances in memory technology have been in density, not speed. Nowadays, the trend for ever increasing processo r operating frequencies has been replaced by an increasing number of CPU cores per chi p. This will continue to exacerbate the memory wall problem, as several cores now h ave to compete for off-chip data access. As multi-core systems pack more and mo re cores, it is expected that the access latency as observed by each core will continu e to increase. Although the causes of the memory wall have changed, it is, and will con tinue to be in the near future, a very significant challenge in terms of computer arc hite ture design. Prefetching has been an important technique to amortize the ffect of the memory wall. With prefetching, data or instructions that are expec ted to be used in the near future are speculatively moved up in the memory hierarchy, w ere the access latency is smaller. This dissertation focuses on hardware data prefet ching at the last cache level before memory (last level cache, LLC). Prefetching at the LL C usually offers the best performance increase, as this is where the disparity betwee n hit and miss latencies is the largest. Hardware prefetchers operate by examining the miss address stream generated by the cache and identifying patterns and correlations betw e n the misses. Most prefetchers divide the global miss stream in several sub-st reams, according to some pre-specified criteria. This process is known as localizati on. The benefits of localization are well established: it increases the accuracy of t he predictions and helps filtering out spurious, non-predictable misses. However lo calization has one important drawback: since the misses are classified into different sub -streams, important chronological information is lost. A consequence of this is that mo st l calizing prefetchers issue prefetches in an untimely manner, fetching data too fa r in advance. This behavior promotes data pollution in the cache. The first part of this thesis proposes a new class of prefetche rs based on the novel concept of Stream Chaining. With Stream Chaining, the prefe tcher tries to reconstruct the chronological information lost in the process of localization, while at the same time keeping its benefits. We describe two novel Stream C haining prefetching algorithms based on two state of the art localizing prefetch rs: PC/DC and C/DC. We
Lightweight Reflection for Middleware-based Database Replication

Middleware-based database replication approaches have emerged in the last few years as an alternative to traditional database replication implemented within the database kernel. A middleware approach enables third party vendors to provide high availability solutions, a growing practice nowadays in the software industry. However, middleware solutions often lack scalability and exhibit a number of consistency and performance issues. The reason is that in most cases the middleware has to handle the database as a black box, and hence, cannot take advantage of the many optimizations implemented in the database kernel. Thus, middleware solutions often reimplement key functionality but cannot achieve the same efficiency as a kernel implementation. Reflection has been proposed during the last decade as a fruitful paradigm to separate non-functional aspects from functional ones, simplifying software development and maintenance whilst fostering reuse. However, fully reflective databases are not feasible due to the high cost of reflection. Our claim is that by exposing some minimal database functionality through a lightweight reflective interface, efficient and scalable middleware database replication can be attained. In this paper we explore a wide variety of such lightweight reflective interfaces and discuss what kind of replication algorithms they enable. We also discuss implementation alternatives for some of these interfaces and evaluate their performance
Slim-DP: A Light Communication Data Parallelism for DNN

Data parallelism has emerged as a necessary technique to accelerate the training of deep neural networks (DNN). In a typical data parallelism approach, the local workers push the latest updates of all the parameters to the parameter server and pull all merged parameters back periodically. However, with the increasing size of DNN models and the large number of workers in practice, this typical data parallelism cannot achieve satisfactory training acceleration, since it usually suffers from the heavy communication cost due to transferring huge amount of information between workers and the parameter server. In-depth understanding on DNN has revealed that it is usually highly redundant, that deleting a considerable proportion of the parameters will not significantly decline the model performance. This redundancy property exposes a great opportunity to reduce the communication cost by only transferring the information of those significant parameters during the parallel training. However, if we only transfer information of temporally significant parameters of the latest snapshot, we may miss the parameters that are insignificant now but have potential to become significant as the training process goes on. To this end, we design an Explore-Exploit framework to dynamically choose the subset to be communicated, which is comprised of the significant parameters in the latest snapshot together with a random explored set of other parameters. We propose to measure the significance of the parameter by the combination of its magnitude and gradient. Our experimental results demonstrate that our proposed Slim-DP can achieve better training acceleration than standard data parallelism and its communication-efficient version by saving communication time without loss of accuracy.
Visual Estimation of Bacterial Growth Level in Microfluidic Culture Systems

Microfluidic devices are an emerging platform for a variety of experiments involving bacterial cell culture, and has advantages including cost and convenience. One inevitable step during bacterial cell culture is the measurement of cell concentration in the channel. The optical density measurement technique is generally used for bacterial growth estimation, but it is not applicable to microfluidic devices due to the small sample volumes in microfluidics. Alternately, cell counting or colony-forming unit methods may be applied, but these do not work in situ; nor do these methods show measurement results immediately. To this end, we present a new vision-based method to estimate the growth level of the bacteria in microfluidic channels. We use Fast Fourier transform (FFT) to detect the frequency level change of the microscopic image, focusing on the fact that the microscopic image becomes rough as the number of cells in the field of view increases, adding high frequencies to the spectrum of the image. Two types of microfluidic devices are used to culture bacteria in liquid and agar gel medium, and time-lapsed images are captured. The images obtained are analyzed using FFT, resulting in an increase in high-frequency noise proportional to the time passed. Furthermore, we apply the developed method in the microfluidic antibiotics susceptibility test by recognizing the regional concentration change of the bacteria that are cultured in the antibiotics gradient. Finally, a deep learning-based data regression is performed on the data obtained by the proposed vision-based method for robust reporting of data.
Why is the Remaining Useful Life Prediction Uncertain?

This paper discusses the significance and interpretation of uncertainty in the remaining useful life (RUL) prediction of components used in several types of engineering applications, and answers certain fundamental questions such as “Why is the RUL prediction uncertain? ”, “How to interpret the uncertainty in the RUL prediction? ”, and “How to compute the un- certainty in the RUL prediction? ”. Prognostics and RUL pre- diction are affected by various sources of uncertainty. In or- der to make meaningful prognostics-based decision-making, it is important to analyze how these sources of uncertainty affect the remaining useful life prediction, and thereby, compute the overall uncertainty in the remaining useful life pre- diction. The classical (frequentist) and Bayesian (subjective) interpretations of uncertainty and their implications on prognostics are explained, and it is argued that the Bayesian interpretation of uncertainty is more suitable for remaining useful life prediction in the context of condition-based monitoring. Finally, it is demonstrated that the calculation of uncertainty in remaining useful life can be posed as an uncertainty propagation problem, and the practical challenges involved in computing the uncertainty in the remaining useful life prediction are discussed.
Processing queries by linear constraints

The emergence of several new application domains for databases has introduced the need for more efficient complex query handling than databases currently support. These application domains include On-Line AnalyticaIProcessing(OLAP), Geographical Information Systems (GIS), and scientific databases. This paper focuses attention on a form of selection query, expressible in SQL but not evaluated efficiently by current DBMSs, with wide applicability in these new problem domains. We introduce a processing strategy for this class of queries, which we call queries by linear constraints (QBLC). This processing strategy can be implemented with a wide variety of multidimensional indexing structures that include the R-? &ee variants, the k-d-B-pee, the Buddy-Tree, and many more. Note that any processing strategy meant for general database use must guarantee that all correct auswers are returned. Therefore, all numerical techniques we employ uphold &is guarantee. Thus the most distinguishing characteristic of this processing strategy is its safe handling of numerical error (which can result in the dismissal of valid answers). This paper presents several theoretical results about our processing strategy, and the results of several experiments which show that the processing cost of selection queries by linear constraints can be reduced dramatically by using our processing strategy. ‘This work was partially supported by a David and Lucile Packard Foundation Fellowship in Science and Engineering, a Presidential Young Investigator Award, NASA Research Grant NAGW-3921, and OFtD grant 144ET33. , Permission to make digital/hard copies of aIt or part of this mate&l for persons1 or classmom use is grsnted without fee provided thst the copies are not made or distributed for profit or commerckd advulbge, &e copyd&t notice, the title ofthe publication and its d&e appear, and notice is given that WPyright is by permission ofthe ACM, Inc. To copy othe-rwise, to republish tc~ post on servers or to redistribute to I&, requires specific pen&ion and/or fee PODS ‘ 97 Tucson Arizona IJSA ~pyri&t 1997 ACM O-89791-910-6/97/05 . $xso Raghu Ramakrishnan Computer Sciences Department University of Wisconsin-Madison 1210 W. Dayton St., Madison, WI 53706
DISTRIBUTED AND SCALABLE GRAPH PATTERN MATCHING: MODELS AND ALGORITHMS

Graph pattern matching is a fundamental operation for many applications, and it is exhaustively studied in its classical forms. Nevertheless, there are newly emerging applications, like analyzing hyperlinks of the web graph and analyzing associations in a social network, that need to process massive graphs in a timely manner. Regarding the extremely large size of these graphs and knowledge they represent, not only new computing platforms are needed, but also old models and algorithms should be revised. In recent years, a few pattern matching models have been introduced that can promise a new avenue for pattern matching research on extremely massive graphs. Moreover, several graph processing frameworks like Pregel have recently sought to harness shared nothing clusters for processing massive graphs through a vertex-centric, Bulk Synchronous Parallel (BSP) programming model. However, developing scalable and efficient BSP-based algorithms for pattern matching is very challenging on these platforms because this problem does not naturally align with a vertex-centric programming paradigm. This paper introduces a new pattern matching model, called tight simulation, which outperforms the previous models in its family in terms of scalability while preserving their important properties. It also presents a novel distributed algorithm based on the vertex-centric programming paradigm for this pattern matching model and several others in the family of graph simulation as well. Our algorithms are fine tuned to consider the challenges of pattern matching on massive data graphs. Furthermore, we present an extensive set of experiments involving massive graphs (millions of vertices and billions of edges) to study the effects of various parameters on the scalability and performance of the proposed algorithms.
Hormonal induction of transfected genes depends on DNA topology

Plasmids containing the hormone regulatory element of mouse mammary tumor virus linked to the thymidine kinase promoter of herpes simplex virus and the reporter gene chloramphenicol acetyltransferase of Escherichia coli respond to glucocorticoids and progestins when transfected into appropriate cells. In the human mammary tumor cell line T47D, the response to progestins, but not to glucocorticoids, is highly dependent on the topology of the transfected DNA. Although negatively supercoiled plasmids respond optimally to the synthetic progestin R5020, their linearized counterparts exhibit markedly reduced progestin inducibility. This is not due to changes in the efficiency of DNA transfection, since the amount of DNA incorporated into the cell nucleus is not significantly dependent on the initial topology of the plasmids. In contrast, cotransfection experiments with glucocorticoid receptor cDNA in the same cell line show no significant influence of DNA topology on induction by dexamethasone. A similar result was obtained with fibroblasts that contain endogenous glucocorticoid receptors. When the distance between receptor-binding sites or between the binding sites and the promoter was increased, the dependence of progestin induction on DNA topology was more pronounced. In contrast to the original plasmid, these constructs also revealed a similar topological dependence for induction by glucocorticoids. The differential influence of DNA topology is not due to differences in the affinity of the two hormone receptors for DNA of various topologies, but probably reflects an influence of DNA topology on the interaction between different DNA-bound receptor molecules and between receptors and other transcription factors.
Metacognition and Decision-Making Style in Clinical Narratives

Clinical decision-making has high-stakes outcomes for both physicians and patients, yet little research has attempted to model and automatically annotate such decision-making. The dual process model (Evans, 2008) posits two types of decision-making, which may be ordered on a continuum from intuitive to analytical (Hammond, 1981). Training clinicians to recognize decision-making style and select the most appropriate mode of reasoning for a particular context may help reduce diagnostic error (Norman, 2009). This study makes preliminary steps towards detection of decision style, based on an annotated dataset of image-based clinical reasoning in which speech data were collected from physicians as they inspected images of dermatological cases and moved towards diagnosis (Hochberg et al., 2014a). A classifier was developed based on lexical, speech, disfluency, physician demographic, cognitive, and diagnostic difficulty features to categorize diagnostic narratives as intuitive vs. analytical; the model improved on the baseline by over 30%. The introduced computational model provides construct validity for the dual process theory. Eventually, such modeling may be incorporated into instructional systems that teach clinicians to become more effective decision makers. In addition, metacognition, or self-assessment and self-management of cognitive processes, has been shown beneficial to decision-making (Batha & Carroll, 2007; Ewell-Kumar, 1999). This study measured physicians’ metacognitive awareness, an online component of metacognition, based on the confidence-accuracy relationship, and also exploited the corpus annotation of decision style to derive decision metrics. These metrics were used to examine the relationships between decision style, metacognitive awareness, expertise, case difficulty, and diagnostic accuracy. Based on statistical analyses, intuitive reasoning was associated with greater diagnostic accuracy, with an advantage for expert physicians. Case difficulty was associated with greater user of analytical decision-making, while metacognitive awareness was linked to decreased diagnostic accuracy. These results offer a springboard for further research on the interactions between decision style, metacognitive awareness, physician and case characteristics, and diagnostic accuracy.