nils-herrmann/clean_scirepeval huggingface dataset:
Multicausality : Confounding-Assignment solutions 1

1. a. Reserpine is a risk factor. Overall, the incidence of breast cancer is 10.47 per 100,000 women-years in reserpine users and 6.14 per 100,000 women-years in nonusers. Moreover, among the non-obese, the rate ratio is: 6.40/4.10 = 1.6. Looking in the non-obese women avoids potential confounding by obesity if it is a risk factor. b. Obesity is also a risk factor. The overall incidence rates for obese and non-obese women, respectively, are 8.72/100,000 women-years and 4.22/100,000 women-years. Among the women who did not take reserpine, the rate ratio is 8.30/4.10 = 2.0. Looking in the group of reserpine non-users avoids potential confounding by reserpine. c. Reserpine use is associated with obesity, though that fact cannot be deduced from the stratum-specific breast cancer rates alone. The direct approach is to remember that the crude rates are weighted averages of stratum-specific rates, with the weights consisting of the population prevalence of the risk factors. So: where P RO is the prevalence of reserpine use in obese subjects, and _ P RO is the prevalence of reserpine use in nonobese subjects. _ Solving these two equations gives P RO = 0.1 and P RO = 0.05, so reserpine use is more prevalent in obese women (presumably because they are more likely to have hypertension). The relative prevalence is 2.0; the odds ratio of association between obesity and reserpine use is [(.1)(.95)]/[(.9)(.05)] = 2.1. Such an association might be characterized as "moderate." Note that the above procedure involving weighted averages can be equally well carried out on the basis of the column rates, rather than the row rates. The odds ratio will be effectively the same. d. The association between reserpine and breast cancer is not attributable to obesity (in the data for this problem). The most relevant rates to demonstrate that the association is not
Detection of pathological biomarkers in human clinical samples via amplifying genetic switches and logic gates

Whole-cell biosensors have several advantages for the detection of biological substances and have proven to be useful analytical tools. However, several hurdles have limited whole-cell biosensor application in the clinic, primarily their unreliable operation in complex media and low signal-to-noise ratio. We report that bacterial biosensors with genetically encoded digital amplifying genetic switches can detect clinically relevant biomarkers in human urine and serum. These bactosensors perform signal digitization and amplification, multiplexed signal processing with the use of Boolean logic gates, and data storage. In addition, we provide a framework with which to quantify whole-cell biosensor robustness in clinical samples together with a method for easily reprogramming the sensor module for distinct medical detection agendas. Last, we demonstrate that bactosensors can be used to detect pathological glycosuria in urine from diabetic patients. These next-generation whole-cell biosensors with improved computing and amplification capacity could meet clinical requirements and should enable new approaches for medical diagnosis.
CAPITAL STRUCTURE AND FINANCIAL PERFORMANCE : EVIDENCE FROM SELECTED BUSINESS COMPANIES IN COLOMBO STOCK EXCHANGE SRI LANKA

Capital structure is most significant discipline of company’s operations. This researcher constitutes an attempt to identify the impact between Capital Structure and Companies Performance, taking into consideration the level of Companies Financial Performance. The analyze has been made the capital structure and its impact on Financial Performance capacity during 2005 to 2009 (05 years) financial year of Business companies in Sri Lanka. The results shown the relationship between the capital structure and financial performance is negative association at -0.114. Co-efficient of determination is 0.013. F and t values are 0.366, -0.605 respectively. It is reflect the insignificant level of the Business Companies in Sri Lanka. Hence Business companies mostly depend on the debt capital. Therefore, they have to pay interest expenses much.
Moving object tracking using optical flow and motion vector estimation

Moving object detection and tracking is an evolving research field due to its wide applications in traffic surveillance, 3D reconstruction, motion analysis (human and non-human), activity recognition, medical imaging etc. However real time object tracking is a challenging task due to dynamic tacking environment and different limiting parameters like view point, anthropometric variation, dimensions of an object, cluttered background, camera motions, occlusion etc. In this paper, we have developed new object detection and tracking algorithm which makes use of optical flow in conjunction with motion vector estimation for object detection and tracking in a sequence of frames. The optical flow gives valuable information about the object movement even if no quantitative parameters are computed. The motion vector estimation technique can provide an estimation of object position from consecutive frames which increases the accuracy of this algorithm and helps to provide robust result irrespective of image blur and cluttered background. The use of median filter with this algorithm makes it more robust in the presence of noise. The developed algorithm is applied to wide range of standard and real time datasets with different illumination (indoor and outdoor), object speed etc. The obtained results indicates that the developed algorithm over performs over conventional methods and state of art methods of object tracking.
Implantable RF Medical Devices: The Benefits of High-Speed Communication and Much Greater Communication Distances in Biomedical Applications

In the early ages of implantable devices, radio frequency (RF) technologies were not commonplace due to the challenges stemming from the inherent nature of biological tissue boundaries. As technology improved and our understanding matured, the benefit of RF in biomedical applications surpassed the implementation challenges and is thus becoming more widespread. The fundamental challenge is due to the significant electromagnetic (EM) effects of the body at high frequencies. The EM absorption and impedance boundaries of biological tissue result in significant reduction of power and signal integrity for transcutaneous propagation of RF fields. Furthermore, the dielectric properties of the body tissue surrounding the implant must be accounted for in the design of its RF components, such as antennas and inductors, and the tissue is often heterogeneous and the properties are highly variable. Additional challenges for implantable applications include the need for miniaturization, power minimization, and often accounting for a conductive casing due to biocompatibility and hermeticity requirements [1]?[3]. Today, wireless technologies are essentially a must have in most electrical implants due to the need to communicate with the device and even transfer usable energy to the implant [4], . Low-frequency wireless technologies face fewer challenges in this implantable setting than its higher frequency, or RF, counterpart, but are limited to much lower communication speeds and typically have a very limited operating distance. The benefits of high-speed communication and much greater communication distances in biomedical applications have spawned numerous wireless standards committees, and the U.S. Federal Communications Commission (FCC) has allocated numerous frequency bands for medical telemetry as well as those to specifically target implantable applications. The development of analytical models, advanced EM simulation software, and representative RF human phantom recipes has significantly facilitated design and optimization of RF components for implantable applications.
A survey on complex event definition languages in multimedia sensor networks

A multimedia sensor network is a sensor network that consists of at least one sensor outputting multimedia data (video, audio, etc.). This kind of sensor network is a fairly new research domain. Hence, techniques for processing complex events in the context of multimedia sensor networks remain underdeveloped as they have rarely been considered previously. In this paper, we identify the requirements of a suitable language for processing complex events in multimedia sensor networks. The requirements are illustrated here through a motivating scenario related to "Smart Home Automation" application. We thoroughly survey existing studies and discuss them with respect to the considered requirements. The conclusions show that no existing language can fully address all the necessary requirements. A discussion about the needed features for processing complex events in multimedia sensor networks is also given here.
Polymelia with unhealed navel in an Iranian indigenous young fowl

Developing supernumerary limbs is a rare congenital condition that only a few cases have been documented. Depending on the cause and developmental conditions, they may be single, multiple or complicated, and occur as a syndrome or associated with other anomalies. Polymelia is defined as the presence of extra limb(s) which have been reported in human, mouse, chicken, calf and lamb. It seems that the precise mechanism regulating this type of congenital malformations is not yet clearly understood. While hereditary trait of some limb anomalies was proven in human and the responsible genetic impairments were found, this has not been confirmed in the other animals especially the birds. Regarding the different susceptibilities of various vertebrate species to the environmental and genetic factors in embryonic period, the probable cause of an embryonic defect in one species cannot be generalized to the all other species class. The present study reports a case of polymelia in an Iranian indigenous young fowl and discusses its possible causes.
Incremental Stochastic Subgradient Algorithms for Convex Optimization

In this paper we study the effect of stochastic errors on two constrained incremental sub-gradient algorithms. We view the incremental sub-gradient algorithms as decentralized network optimization algorithms as applied to minimize a sum of functions, when each component function is known only to a particular agent of a distributed network. We first study the standard cyclic incremental sub-gradient algorithm in which the agents form a ring structure and pass the iterate in a cycle. We consider the method with stochastic errors in the sub-gradient evaluations and provide sufficient conditions on the moments of the stochastic errors that guarantee almost sure convergence when a diminishing step-size is used. We also obtain almost sure bounds on the algorithm’s performance when a constant step-size is used. We then consider the Markov randomized incremental subgradient method, which is a non-cyclic version of the incremental algorithm where the sequence of computing agents is modeled as a time non-homogeneous Markov chain. Such a model is appropriate for mobile networks, as the network topology changes across time in these networks. We establish the convergence results and error bounds for the Markov randomized method in the presence of stochastic errors for diminishing and constant step-sizes, respectively.
Implementation of K-means algorithm on FGGA

K-means algorithm is one of the clustering algorithms that increase in popularity day by day. The intensive mathematical operations and the continuous increase of the data size while clustering on large data using the K-means algorithm prevent the algorithm from operating at high performance. Therefore, the K-means algorithm that works on large data needs to be implemented on very fast hardware. FPGAs capable of parallel processing can be mathematically processed much faster than traditional processors. Therefore, realization of algorithms that require intensive mathematical computations such as K-means using FPGAs is of great importance for the performance of applications. In this study, an architecture is designed on the FPGA for the K-means algorithm and the accuracy and efficiency of the generated architecture are compared with the software applied in the standard processor and the performance is tested. When the results are examined, it is seen that the FPGA gives an average of 100X faster results than the standard processor.
Graph-based Event Extraction from Twitter

Detecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have mainly focused on detecting spikes in clusters around specific keywords or Named Entities (NE). However, one of the main drawbacks of such approaches is the difficulty in understanding when the same keywords describe different events. In this paper, we propose a novel approach that exploits NE mentions in tweets and their entity context to create a temporal event graph. Then, using simple graph theory techniques and a PageRank-like algorithm, we process the event graphs to detect clusters of tweets describing the same events. Experiments on two gold standard datasets show that our approach achieves state-of-the-art results both in terms of evaluation performances and the quality of the detected events.
Fault Detection Systems via a Novel hybrid Methodology for Fuzzy Logic Systems based on Individual base inference and Statistical Process Control

This paper presents a novel methodology to model fuzzy logic systems used as fault detection systems. This method is based in a hybrid model assembled via Individual Base Inference (IBI), Statistical Process Control (SPC) tool in the form of Control Charts (CC) and MacVicar-Whelan Methodologies. The novel method provides a simplification of the necessary calculus of inference engine module with the approaches SPC tool (CC: X-S and X-R). The inference engine is assembled via a novel methodology based in the concepts used to create CC and can be made with the specification for R charts or X-S charts. This methodology consists in the hybridization of Macvicar-Whelan method for the assignation of fuzzy labels to provide rules. The IBI is created with the wide spread of real historical data. This data forms the Universe of Discourse (UOD) and the SPC tool is used to provide the fuzzy rules and rule reduction. The aim of this paper is to test the novel method and to compare it with a classic method to prove that this method is an efficient form to rule deduction without lost reliability in the output approximation. The proposed method was tested in an electrical power system.
Adaptive methods for robust document image unterstanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current deficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexityand threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy.
Wi-Fi Tracking: Fingerprinting Attacks and Counter-Measures. (Traçage Wi-Fi: Attaques par Prise d'Empreinte et Contre-Mesures)

Wi-Fi Tracking: Fingerprinting Attacks and CounterMeasures The recent spread of everyday-carried Wi-Fi-enabled devices (smartphones, tablets and wearable devices) comes with a privacy threat to their owner, and to society as a whole. These devices continuously emit signals which can be captured by a passive attacker using cheap hardware and basic knowledge. These signals contain a unique identi er, called the MAC address. To mitigate the threat, device vendors are currently deploying a countermeasure on new devices: MAC address randomization. Unfortunately, we show that this mitigation, in its current state, is insu cient to prevent tracking. To do so, we introduce several attacks, based on the content and the timing of emitted signals. In complement, we study implementations of MAC address randomization in some recent devices, and nd a number of shortcomings limiting the e ciency of these implementations at preventing device tracking. At the same time, we perform two real-world studies. The rst one considers the development of actors exploiting this issue to install Wi-Fi tracking systems. We list some real-world installations and discuss their various aspects, including regulation, privacy implications, consent and public acceptance. The second one deals with the spread of MAC address randomization in the devices population. Finally, we present two tools: an experimental Wi-Fi tracking system for testing and public awareness raising purpose, and a tool estimating the uniqueness of a device based on the content of its emitted signals even if the identi er is randomized.
Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer.

Short-term preoperative radiotherapy and total mesorectal excision have each been shown to improve local control of disease in patients with resectable rectal cancer. We conducted a multicenter, randomized trial to determine whether the addition of preoperative radiotherapy increases the benefit of total mesorectal excision.



We randomly assigned 1861 patients with resectable rectal cancer either to preoperative radiotherapy (5 Gy on each of five days) followed by total mesorectal excision (924 patients) or to total mesorectal excision alone (937 patients). The trial was conducted with the use of standardization and quality-control measures to ensure the consistency of the radiotherapy, surgery, and pathological techniques.



Of the 1861 patients randomly assigned to one of the two treatment groups, 1805 were eligible to participate. The overall rate of survival at two years among the eligible patients was 82.0 percent in the group assigned to both radiotherapy and surgery and 81.8 percent in the group assigned to surgery alone (P=0.84). Among the 1748 patients who underwent a macroscopically complete local resection, the rate of local recurrence at two years was 5.3 percent. The rate of local recurrence at two years was 2.4 percent in the radiotherapy-plus-surgery group and 8.2 percent in the surgery-only group (P<0.001).



Short-term preoperative radiotherapy reduces the risk of local recurrence in patients with rectal cancer who undergo a standardized total mesorectal excision.
Forensic dentistry: A Pedodontist's perspective

Forensic dentistry delineates the overlap between the dental and the legal professions. Forensic identifi cations by their nature are multidisciplinary team eff orts. Odontologists can examine the structure of the teeth and jaws for clues that may support anthropological age estimates. Apart from dental identifi cation, forensic odontology is also applied in the investigation of crimes caused by dentition, such as bite marks. The importance of pedodontist in forensic odontology is to apply his expertise in various fi elds like child abuse and neglect, mass disaster, accidental and non-accidental oral trauma, age determination, and dental records. The aim of this paper is to discuss about the pedodontist perspective in forensic dentistry.
ROAD LANE AND TRAFFIC SIGN DETECTION & TRACKING FOR AUTONOMOUS URBAN DRIVING

ACKNOWLEDGEMENTS First, I would like to thank my supervisor Professor H. Levent Akın for his guidance. This thesis would not have been possible without his encouragement and enthusiastic support. I would also like to thank all the staff at the Artificial Intelligence Laboratory for their encouragement throughout the year. Their success in RoboCup is always a good motivation. Sharing their precious ideas during the weekly seminars have always guided me to the right direction. Finally I am deeply grateful to my family and to my wife Derya. They always give me endless love and support, which has helped me to overcome the various challenges along the way. Thank you for your patience... The field of Intelligent Transport Systems (ITS) is improving rapidly in the world. Ultimate aim of such systems is to realize fully autonomous vehicle. The researches in the field offer the potential for significant enhancements in safety and operational efficiency. Lane tracking is an important topic in autonomous navigation because the navigable region usually stands between the lanes, especially in urban environments. Several approaches have been proposed, but Hough transform seems to be the dominant among all. A robust lane tracking method is also required for reducing the effect of the noise and achieving the required processing time. In this study, we present a new lane tracking method which uses a partitioning technique for obtaining Multiresolution Hough Transform (MHT) of the acquired vision data. After the detection process, a Hidden Markov Model (HMM) based method is proposed for tracking the detected lanes. Traffic signs are important instruments to indicate the rules on roads. This makes them an essential part of the ITS researches. It is clear that leaving traffic signs out of concern will cause serious consequences. Although the car manufacturers have started to deploy intelligent sign detection systems on their latest models, the road conditions and variations of actual signs on the roads require much more robust and fast detection and tracking methods. Localization of such systems is also necessary because traffic signs differ slightly between countries. This study also presents a fast and robust sign detection and tracking method based on geometric transformation and genetic algorithms (GA). Detection is done by a genetic algorithm (GA) approach supported by a radial symmetry check so that false alerts are considerably reduced. Classification v is achieved by a combination of SURF features with NN or SVM classifiers. A heuristic …