nils-herrmann/clean_scirepeval huggingface dataset:
Lymphoepithelial Cyst of Tonsil

The tissue interactions between epithelium and mesenchyme are few of the events which result in pathological processes. One such interaction of epithelium and lymphoid tissue may result in lymphoepithelial lesions in the oral cavity. Lymphoepithelial cyst (LEC) is a rare developmental cystic lesion of the oral cavity with various histogenetic implications. Documented cases of LEC of the tonsil are low in number in the literature. We are hereby reporting a case of LEC of the tonsil in a male patient with review of literature.
A Study on Outlier Detection for Temporal Data

Data mining provides a way for finding hidden and useful knowledge from the large amount of data .usually we find any information by finding normal trends or distribution of data .But sometimes rare event or data object may provide information which is very interesting to us .Outlier detection is one of the task of data mining .It finds abnormal data point or sequence hidden in the dataset. Data stream is unbounded sequence of data with explicit or implicit temporal context .Data stream is uncertain and dynamic in nature. In the statistics community, outlier detection for time series data has been studied for decades. Recently, with advances in hardware and software technology, there has been a large body of work on temporal outlier detection from a computational perspective within the computer science community. Outlier detection alludes to task of distinguishing examples. They don’t accommodate set up normal conduct. Anomaly detection in high-dimensional information presents different difficulties coming about because of the "scourge of dimensionality". The present perspective is that separation fixation that is propensity of separations in high-dimensional information to end up in perceivable making separation based strategies mark all focuses as similarly great exceptions. This paper gives proof by exhibiting the separation based strategy can create additionally contrasting exception in high dimensional setting. The high dimensional can have an alternate effect, by re-evaluating the idea of opposite closest neighbors. In particular, advances in hardware technology have enabled the availability of various forms of temporal data collection mechanisms, and advances in software technology have enabled a variety of data management mechanisms. This has fuelled the growth of different kinds of data sets such as data streams, spatio-temporal data, distributed streams, temporal networks, and time series data, generated by a multitude of applications. There arises a need for an organized and detailed study of the work done in the area of outlier detection with respect to such temporal datasets. In this survey, we provide a comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel techniques, and application scenarios in which specific definitions and techniques have been widely used.
Stack-Captioning: Coarse-to-Fine Learning for Image Captioning

The existing image captioning approaches typically train a one-stage sentence decoder, which is difficult to generate rich fine-grained descriptions. On the other hand, multi-stage image caption model is hard to train due to the vanishing gradient problem. In this paper, we propose a coarse-to-fine multi-stage prediction framework for image captioning, composed of multiple decoders each of which operates on the output of the previous stage, producing increasingly refined image descriptions. Our proposed learning approach addresses the difficulty of vanishing gradients during training by providing a learning objective function that enforces intermediate supervisions. Particularly, we optimize our model with a reinforcement learning approach which utilizes the output of each intermediate decoder’s test-time inference algorithm as well as the output of its preceding decoder to normalize the rewards, which simultaneously solves the well-known exposure bias problem and the loss-evaluation mismatch problem. We extensively evaluate the proposed approach on MSCOCO and show that our approach can achieve the state-of-the-art performance.
A Survey on Multicasting in Software-Defined Networking

Existing surveys of research into software-defined networking (SDN) make only minimal mention of multicasting. We present a survey of existing multicast routing protocols in Internet Protocol (IP) multicast, and a survey of existing and proposed multicast routing algorithms in SDN multicast. Each of these protocols/algorithms is designed to optimize a slightly different metric, under specific constraints, so we contrast the approaches based on the optimization goal. We then present a survey of existing and proposed approaches to security for IP multicast and SDN multicast. This reveals a striking lack of breadth in the research in the topic of SDN multicast security.
Approximate capacity of a class of partially connected interference channels

We derive inner and outer bounds on the capacity region for a class of three-user partially connected interference channels. We focus on the impact of topology, interference alignment, and interplay between interference and noise. The representative channels we consider are the ones that have clear interference alignment gain. For these channels, Z-channel type outer bounds are tight to within a constant gap from capacity. We present near-optimal achievable schemes based on rate-splitting, lattice alignment, and successive decoding.
A Big Data Analysis Approach for Rail Failure Risk Assessment.

Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach.
Cholera Toxin: An Intracellular Journey into the Cytosol by Way of the Endoplasmic Reticulum

Cholera toxin (CT), an AB(5)-subunit toxin, enters host cells by binding the ganglioside GM1 at the plasma membrane (PM) and travels retrograde through the trans-Golgi Network into the endoplasmic reticulum (ER). In the ER, a portion of CT, the enzymatic A1-chain, is unfolded by protein disulfide isomerase and retro-translocated to the cytosol by hijacking components of the ER associated degradation pathway for misfolded proteins. After crossing the ER membrane, the A1-chain refolds in the cytosol and escapes rapid degradation by the proteasome to induce disease by ADP-ribosylating the large G-protein Gs and activating adenylyl cyclase. Here, we review the mechanisms of toxin trafficking by GM1 and retro-translocation of the A1-chain to the cytosol.
EVALUATING THE PREDICTIVE VALIDITY OF THE COMPAS RISK AND NEEDS ASSESSMENT SYSTEM

This study examines the statistical validation of a recently developed, fourth-generation (4G) risk–need assessment system (Correctional Offender Management Profiling for Alternative Sanctions; COMPAS) that incorporates a range of theoretically relevant criminogenic factors and key factors emerging from meta-analytic studies of recidivism. COMPAS’s automated scoring provides decision support for correctional agencies for placement decisions, offender management, and treatment planning. The article describes the basic features of COMPAS and then examines the predictive validity of the COMPAS risk scales by fitting Cox proportional hazards models to recidivism outcomes in a sample of presentence investigation and probation intake cases (N = 2,328). Results indicate that the predictive validities for the COMPAS recidivism risk model, as assessed by the area under the receiver operating characteristic curve (AUC), equal or exceed similar 4G instruments. The AUCs ranged from .66 to .80 for diverse offender subpopulations across three outcome criteria, with a majority of these exceeding .70.
A Customized Bolus Produced Using a 3-Dimensional Printer for Radiotherapy

Boluses are used in high-energy radiotherapy in order to overcome the skin sparing effect. In practice though, commonly used flat boluses fail to make a perfect contact with the irregular surface of the patient's skin, resulting in air gaps. Hence, we fabricated a customized bolus using a 3-dimensional (3D) printer and evaluated its feasibility for radiotherapy.



We designed two kinds of bolus for production on a 3D printer, one of which was the 3D printed flat bolus for the Blue water phantom and the other was a 3D printed customized bolus for the RANDO phantom. The 3D printed flat bolus was fabricated to verify its physical quality. The resulting 3D printed flat bolus was evaluated by assessing dosimetric parameters such as D1.5 cm, D5 cm, and D10 cm. The 3D printed customized bolus was then fabricated, and its quality and clinical feasibility were evaluated by visual inspection and by assessing dosimetric parameters such as Dmax, Dmin, Dmean, D90%, and V90%.



The dosimetric parameters of the resulting 3D printed flat bolus showed that it was a useful dose escalating material, equivalent to a commercially available flat bolus. Analysis of the dosimetric parameters of the 3D printed customized bolus demonstrated that it is provided good dose escalation and good contact with the irregular surface of the RANDO phantom.



A customized bolus produced using a 3D printer could potentially replace commercially available flat boluses.
Identification of incident stroke in Norway: hospital discharge data compared with a population-based stroke register.

The validity of hospital discharge diagnoses is essential in improving stroke surveillance and estimating healthcare costs of stroke. The aim of this study was to assess sensitivity, positive predictive value, and accuracy of discharge diagnoses compared with a stroke register.



A record linkage was made between a population-based stroke register and the discharge records of the hospital serving the population of the stroke register (n=70 000). The stroke register (including patients aged 15 and older and with no upper age limit), applied here as a "gold standard," was used to estimate sensitivity, positive predictive value, and accuracy of the discharge diagnoses classification. The length of stay in hospital by stroke patients was measured.



Identifying cerebrovascular diseases by hospital discharge diagnoses (International Classification of Diseases, 9th Revision [ICD-9], codes 430 to 438.9, first admission) lead to a substantial overestimation of stroke in the target population. Restricting the retrieval to acute stroke diagnoses (ICD-9 codes 430, 431, 434, and 436) gave an incidence estimate closer to the "true" incidence rate in the stroke register. Selecting ICD-9 codes 430 to 438 of cerebrovascular diseases gave the highest sensitivity (86%). The highest positive predictive value (68%) was achieved by selecting acute stroke diagnoses (ICD-9 codes 430, 431, 434, and 436), at the expense of a lower sensitivity (81%). Accuracy of ICD codes 430 to 438.9 (n=678) revealed the highest proportion of incident strokes identified by the acute stroke diagnoses (ICD-9 codes 430, 431, 434, and 436). Seventy-four percent of hospital discharge diagnoses classified as first-ever stroke kept the original diagnosis. Only 4.6% of the discharge diagnoses were classified as nonstroke diagnoses after validation. The estimation of length of stay in the hospital was improved by selection of acute stroke diagnoses from hospital discharge data (ICD-9 codes 430, 431, 434, and 436), which gave the same estimate of length of stay, a median of 8 days (2.5 percentile=0 and 97.5 percentile=56), compared with a median of 8 days (2.5 percentile=0 and 97.5 percentile=51) based on the stroke register.



Hospital discharge data may overestimate stroke incidence and underestimate the length of stay in the hospital, unless selection routines of hospital discharge diagnoses are restricted to acute stroke diagnoses (ICD-9 codes 430, 431, 434, and 436). If supplemented by a validation procedure, including estimates of sensitivity, positive predictive value, and accuracy, hospital discharge data may provide valid information on hospital-based stroke incidence and lead to better allocation of health resources. Distinguishing subtypes of stroke from hospital discharge diagnoses should not be performed unless coding practices are improved.
A Review of Offline Signature Verification Techniques

Among various biometric modalities, signature verification remains one of the most widely used methods to authenticate the identity of an individual. Signature verification, the most important component of behavioral biometrics, has attracted significant research attention over the last three decades. Despite extensive research, the problem still remains open to research due to the variety of challenges it offers. The high intra-class variations in signatures resulting from different physical or mental states of the signer, the differences that appear with aging and the visual similarity in case of skilled forgeries etc. are only a few of the challenges to name.  The pros and cons of notable recent contributions to this problem have also been presented along with a discussion of potential future research directions on this subject.
An experimental study of shared sensitivity to physical pain and social rejection

Recent evidence points to a possible overlap in the neural systems underlying the distressing experience that accompanies physical pain and social rejection (Eisenberger et al., 2003). The present study tested two hypotheses that stem from this suggested overlap, namely: (1) that baseline sensitivity to physical pain will predict sensitivity to social rejection and (2) that experiences that heighten social distress will heighten sensitivity to physical pain as well. In the current study, participants' baseline cutaneous heat pain unpleasantness thresholds were assessed prior to the completion of a task that manipulated feelings of social distress. During this task, participants played a virtual ball-tossing game, allegedly with two other individuals, in which they were either continuously included (social inclusion condition) or they were left out of the game by either never being included or by being overtly excluded (social rejection conditions). At the end of the game, three pain stimuli were delivered and participants rated the unpleasantness of each. Results indicated that greater baseline sensitivity to pain (lower pain unpleasantness thresholds) was associated with greater self-reported social distress in response to the social rejection conditions. Additionally, for those in the social rejection conditions, greater reports of social distress were associated with greater reports of pain unpleasantness to the thermal stimuli delivered at the end of the game. These results provide additional support for the hypothesis that pain distress and social distress share neurocognitive substrates. Implications for clinical populations are discussed.
Maintaining the Time in a Distributed System

To a client, one of the simplest services provided by a distributed system is a time service. A client simply requests the time from any set of servers, and uses any reply. The simplicity in this interaction, however, misrepresents the complexity of implementing such a service. An algorithm is needed that will keep a set of clocks synchronized, reasonably correct and accurate with rcspcct to a standard, and able to withstand errors such as communication failures and inaccurate clocks. This paper presents a partial solution to the problem by describing two algorithms which will keep clocks both correct and synchronized.
Control of TCF-4 Expression by VDR and Vitamin D in the Mouse Mammary Gland and Colorectal Cancer Cell Lines

The vitamin D receptor (VDR) pathway is important in the prevention and potentially in the treatment of many cancers. One important mechanism of VDR action is related to its interaction with the Wnt/beta-catenin pathway. Agonist-bound VDR inhibits the oncogenic Wnt/beta-catenin/TCF pathway by interacting directly with beta-catenin and in some cells by increasing cadherin expression which, in turn, recruits beta-catenin to the membrane. Here we identify TCF-4, a transcriptional regulator and beta-catenin binding partner as an indirect target of the VDR pathway.



In this work, we show that TCF-4 (gene name TCF7L2) is decreased in the mammary gland of the VDR knockout mouse as compared to the wild-type mouse. Furthermore, we show 1,25(OH)2D3 increases TCF-4 at the RNA and protein levels in several human colorectal cancer cell lines, the effect of which is completely dependent on the VDR. In silico analysis of the human and mouse TCF7L2 promoters identified several putative VDR binding elements. Although TCF7L2 promoter reporters responded to exogenous VDR, and 1,25(OH)2D3, mutation analysis and chromatin immunoprecipitation assays, showed that the increase in TCF7L2 did not require recruitment of the VDR to the identified elements and indicates that the regulation by VDR is indirect. This is further confirmed by the requirement of de novo protein synthesis for this up-regulation.



Although it is generally assumed that binding of beta-catenin to members of the TCF/LEF family is cancer-promoting, recent studies have indicated that TCF-4 functions instead as a transcriptional repressor that restricts breast and colorectal cancer cell growth. Consequently, we conclude that the 1,25(OH)2D3/VDR-mediated increase in TCF-4 may have a protective role in colon cancer as well as diabetes and Crohn's disease.
Social engineering in information assurance curricula

With the increasing use of security technology, technical attacks should become more difficult leading attackers to employ social engineering as a means to obtaining unauthorized access to information. Therefore, social engineering is a potentially dangerous threat to information security. Fortunately, a number of countermeasures have been proposed to defend against it. These countermeasures include implementing policy, providing end-user and key personnel education, and performing security audits. However, most current prominent information assurance curricula do not directly address social engineering and only indirectly address the countermeasures. Amending these curricula to include social engineering as a topic may help students be better prepared for encountering social engineering threats.
A new secure model for the use of cloud computing in big data analytics

When Big data and cloud computing join forces together, several domains like: healthcare, disaster prediction and decision making become easier and much more beneficial to users in term of information gathering, although cloud computing will reduce time and cost of analyzing information for big data, it may harm the confidentiality and integrity of the sensitive data, for instance, in healthcare, when analyzing disease's spreading area, the name of the infected people must remain secure, hence the obligation to adopt a secure model that protect sensitive data from malicious users. Several case studies on the integration of big data in cloud computing, urge on how easier it would be to analyze and manage big data in this complex envronement. Companies must consider outsourcing their sensitive data to the cloud to take advantage of its beneficial resources such as huge storage, fast calculation, and availability, yet cloud computing might harm the security of data stored and computed in it (confidentiality, integrity). Therefore, strict paradigm must be adopted by organization to obviate their outsourced data from being stolen, damaged or lost. In this paper, we compare between the existing models to secure big data implementation in the cloud computing. Then, we propose our own model to secure Big Data on the cloud computing environement, considering the lifecycle of data from uploading, storage, calculation to its destruction.
A Comparative Study of Crossover Operators for Genetic Algorithms to Solve the Job Shop Scheduling Problem

Genetic algorithms (GA) are wide class of global optimization methods. Many genetic algorithms have been applied to solve combinatorial optimization problems. One of the problems in using genetic algorithms is the choice of crossover operator. The aim of this paper is to show the influence of genetic crossover operators on the performance of a genetic algorithm. The GA is applied to the job shop scheduling problem (JSSP). To achieve this aim an experimental study of a set of crossover operators is presented. The experimental study is based on a decision support system (DSS). To compare the abilities of different crossover operators, the DSS was designed giving all the operators the same opportunities. The genetic crossover operators are tested on a set of standard instances taken from the literature. The makespan is the measure used to evaluate the genetic crossover operators. The main conclusion is that there is a crossover operator having the best average performance on a specific set of solved instances.  Scheduling, Genetic Algorithms, Crossover Operators, Optimization, Operations Research,
Information Systems Security Design Methods: Implications for Information Systems Development

The security of information systems is a serious issue because computer abuse is increasing. It is important, therefore, that systems analysts and designers develop expertise in methods for specifying information systems security. The characteristics found in three generations of general information system design methods provide a framework for comparing and understanding current security design methods. These methods include approaches that use checklists of controls, divide functional requirements into engineering partitions, and create abstract models of both the problem and the solution. Comparisons and contrasts reveal that advances in security methods lag behind advances in general systems development methods. This analysis also reveals that more general methods fail to consider security specifications rigorously.
The differential broadens. EUS FNA appearance and cytological findings of pancreatic angiomyolipoma.

Angiomyolipoma is a rare tumor characterized histologically by a mixture of spindle cells, adipose tissue, epithelioid cells, and vascular tissue. It usually involves the kidney followed by the liver whereby the majority of affected patients are female, and many cases arise in the setting of tuberous sclerosis.



We report a case of a 33-year-old female with an asymptomatic incidental right renal mass suggestive of an angiomyolipoma in conjunction with numerous pancreatic masses.



The utility of EUS in the differential diagnosis of pancreatic tumors is well established. This is the first known reported EUS detection and FNA confirmation of angiomyolipoma metastatic to the pancreas and should now be added to the already broad differential of metastatic pancreatic tumors.