nils-herrmann/clean_scirepeval huggingface dataset:
Phase-type distribution of customer relationship with Markovian response and marketing expenditure decision on the customer lifetime value

In recent years, more and more companies have adopted relationship marketing (RM). At the core of RM is the development and maintenance of long-term relationships with valuable customers. In RM, the customer lifetime value (CLV) is the discounted profit streams of a customer across the entire customer life cycle. The CLV plays a key role in customer acquisition and retention decisions. In this paper, we present a general mathematical framework for RM, and introduce a Markov chain model which is appropriate in modeling RM because of its flexibility and probabilistic nature. We also develop the life distribution of the customer relationship, which is a phase-type distribution since it is the distribution of the first arrival stopping state. And we obtain the expectation of the CLV, which is an important statistic for good decision-making. Finally, we illustrate how to find the optimal remarketing policy numerically. The framework developed for RM systems in this paper should be seen as a practical approach to RM where one can directly apply the results of phasetype distribution and expectation of CLV to marketing decisions.
Developing and delivering hands-on information assurance exercises: experiences with the cyber defense lab at UMBC

In summer 2003, we developed four new hands-on information assurance educational exercises for use in the UMBC undergraduate and graduate curricula. Exercise topics comprise buffer overflow attacks, vulnerability scanning, password security and policy, and flaws in the wired equivalent privacy (WEP) protocol. During each exercise, each student carries out structured activities using a laptop from a mobile cart that can be rolled into any classroom. These dedicated, isolated machines permit a student to make mistakes safely, even while acting as the system administrator, without adversely affecting any other user. Each exercise is organized in a modular fashion to facilitate varied use for different courses, levels, and available time. Our experiences delivering these exercises show that practical hands-on activities motivate students and enhance learning. In this paper we describe our exercises and share lessons learned, including the importance of careful planning, ethical considerations, the rapid obsolescence of tools, and the difficulty of including exercises in already busy courses.
Adaptation of human skin color in various populations

Skin color is a well-recognized adaptive trait and has been studied extensively in humans. Understanding the genetic basis of adaptation of skin color in various populations has many implications in human evolution and medicine. Impressive progress has been made recently to identify genes associated with skin color variation in a wide range of geographical and temporal populations. In this review, we discuss what is currently known about the genetics of skin color variation. We enumerated several cases of skin color adaptation in global modern humans and archaic hominins, and illustrated why, when, and how skin color adaptation occurred in different populations. Finally, we provided a summary of the candidate loci associated with pigmentation, which could be a valuable reference for further evolutionary and medical studies. Previous studies generally indicated a complex genetic mechanism underlying the skin color variation, expanding our understanding of the role of population demographic history and natural selection in shaping genetic and phenotypic diversity in humans. Future work is needed to dissect the genetic architecture of skin color adaptation in numerous ethnic minority groups around the world, which remains relatively obscure compared with that of major continental groups, and to unravel the exact genetic basis of skin color adaptation.
Theoretical mimicry of biomembranes.

The study of membrane proteins requires a proper consideration of the specific environment provided by the biomembrane. The compositional complexity of this environment poses great challenges to all experimental and theoretical approaches. In this article a rather simple theoretical concept is discussed for its ability to mimic the biomembrane. The biomembrane is approximated by three mimicry solvents forming individual continuum layers of characteristic physical properties. Several specific structural problems are studied with a focus on the biological significance of such an approach. Our results support the general perception that the biomembrane is crucial for correct positioning and embedding of its constituents. The described model provides a semi-quantitative tool of potential interest to many problems in structural membrane biology.
Error Entropy Minimization for LSTM Training

In this paper we present a new training algorithm for the Long Short-Term Memory (LSTM) recurrent neural network. This algorithm uses entropy instead of the usual mean squared error as the cost function for the weight update. More precisely we use the Error Entropy Minimization approach, were the entropy of the error is minimized after each symbol is present to the network. Our experiments show that this approach enables the convergence of the LSTM more frequently than with the traditional learning algorithm. This in turn relaxes the burden of parameter tuning since learning is achieved for a wider range of parameter values. The use of EEM also reduces, in some cases, the number of epochs needed for convergence.
ENDOCRINE TREATMENT OF GENDER-DYSPHORIC/GENDER-INCONGRUENT PERSONS: AN ENDOCRINE SOCIETY CLINICAL PRACTICE GUIDELINE.

Wylie C. Hembree, Peggy T. Cohen-Kettenis, Louis Gooren, Sabine E. Hannema, Walter J. Meyer, M. Hassan Murad, Stephen M. Rosenthal, Joshua D. Safer, Vin Tangpricha, and Guy G. T’Sjoen New York Presbyterian Hospital, Columbia University Medical Center, New York, New York 10032 (Retired); VU University Medical Center, 1007 MB Amsterdam, Netherlands (Retired); VU University Medical Center, 1007 MB Amsterdam, Netherlands (Retired); Leiden University Medical Center, 2300 RC Leiden, Netherlands; University of Texas Medical Branch, Galveston, Texas 77555; Mayo Clinic EvidenceBased Practice Center, Rochester, Minnesota 55905; University of California San Francisco, Benioff Children’s Hospital, San Francisco, California 94143; Boston University School of Medicine, Boston, Massachusetts 02118; Emory University School of Medicine and the Atlanta VA Medical Center,  and Ghent University Hospital, 9000 Ghent, Belgium
A Survey of Code Optimization Methods for Kalman Filter Extensions

Kalman filter extensions are commonly used algorithms for nonlinear state estimation in time series. The structure of the state and measurement models in the estimation problem can be exploited to reduce the computational demand of the algorithms. We review algorithms that use different forms of structure and show how they can be combined. We show also that the exploitation of the structure of the problem can lead to improved accuracy of the estimates while reducing the computational load.
ExpertEyes: open-source, high-definition eyetracking.

ExpertEyes is a low-cost, open-source package of hardware and software that is designed to provide portable high-definition eyetracking. The project involves several technological innovations, including portability, high-definition video recording, and multiplatform software support. It was designed for challenging recording environments, and all processing is done offline to allow for optimization of parameter estimation. The pupil and corneal reflection are estimated using a novel forward eye model that simultaneously fits both the pupil and the corneal reflection with full ellipses, addressing a common situation in which the corneal reflection sits at the edge of the pupil and therefore breaks the contour of the ellipse. The accuracy and precision of the system are comparable to or better than what is available in commercial eyetracking systems, with a typical accuracy of less than 0.4° and best accuracy below 0.3°, and with a typical precision (SD method) around 0.3° and best precision below 0.2°. Part of the success of the system comes from a high-resolution eye image. The high image quality results from uncasing common digital camcorders and recording directly to SD cards, which avoids the limitations of the analog NTSC format. The software is freely downloadable, and complete hardware plans are available, along with sources for custom parts.
Learning User Embeddings from Emails

Many important email-related tasks, such as email classification or search, highly rely on building quality document representations (e.g., bag-of-words or key phrases) to assist matching and understanding. Despite prior success on representing textual messages, creating quality user representations from emails was overlooked. In this paper, we propose to represent users using embeddings that are trained to reflect the email communication network. Our experiments on Enron dataset suggest that the resulting embeddings capture the semantic distance between users. To assess the quality of embeddings in a real-world application, we carry out auto-foldering task where the lexical representation of an email is enriched with user embedding features. Our results show that folder prediction accuracy is improved when embedding features are present across multiple settings.
The marginal fit of lithium disilicate crowns: Press vs. CAD/CAM.

This study aimed to compare the vertical marginal gap of teeth restored with lithium disilicate crowns fabricated using CAD/CAM or by pressed ceramic approach. Twenty mandibular third molar teeth were collected after surgical extractions and prepared to receive full veneer crowns. Teeth were optically scanned and lithium disilicate blocks were used to fabricate crowns using CAD/CAM technique. Polyvinyl siloxane impressions of the prepared teeth were made and monolithic pressed lithium disilicate crowns were fabricated. The marginal gap was measured using optical microscope at 200× magnification (Keyence VHX-5000, Japan). Statistical analysis was performed using Wilcoxon test. The lithium disilicate pressed crowns had significantly smaller (p = 0.006) marginal gaps (38 ± 12 μm) than the lithium disilicate CAD/CAM crowns (45 ± 12 μm). This research indicates that lithium disilicate crowns fabricated with the press technique have measurably smaller marginal gaps compared with those fabricated with CAD/CAM technique within in vitro environments. The marginal gaps achieved by the crowns across all groups were within a clinically acceptable range.
Giving Content to Investor Sentiment : The Role of Media in the Stock Market

I quantitatively measure the nature of the media’s interactions with the stock market using daily content from a popular Wall Street Journal column. I find that high media pessimism predicts downward pressure on market prices followed by a reversion to fundamentals, and unusually high or low pessimism predicts high market trading volume. These results and others are consistent with theoretical models of noise and liquidity traders. However, the evidence is inconsistent with theories of media content as a proxy for new information about fundamental asset values, as a proxy for market volatility, or as a sideshow with no relationship to asset markets. ∗Tetlock is at the McCombs School of Business, University of Texas at Austin. I am indebted to Robert Stambaugh (the editor), an anonymous associate editor and an anonymous referee for their suggestions. I am grateful to Aydogan Alti, John Campbell, Lorenzo Garlappi, Xavier Gabaix, Matthew Gentzkow, John Griffin, Seema Jayachandran, David Laibson, Terry Murray, Alvin Roth, Laura Starks, Jeremy Stein, Philip Tetlock, Sheridan Titman and Roberto Wessels for their comments. I thank Philip Stone for providing the General Inquirer software and Nathan Tefft for his technical expertise. I appreciate Robert O’Brien’s help in providing information about the Wall Street Journal. I also acknowledge the National Science Foundation, Harvard University and the University of Texas at Austin for their financial support. All mistakes in this article are my own.
A Better Model for Task Assignment in Server Farms: How Replication can Help

An age-old problem in the design of server farms is the choice of the task assignment policy. This is the algorithm that determines how to assign incoming jobs to servers. Popular policies include Round-Robin assignment, Join-the-Shortest-Queue, Join-Queue-with-Least-Work, and so on. While much research has studied assignment policies, little has taken into account server-side variability -- the fact that the server we choose might be temporarily and unpredictably slow. We show that when server-side variability dominates runtime, replication of jobs can be very beneficial. We introduce the Replication-d algorithm that replicates each arrival to d servers chosen at random, where the job is considered "done" as soon as the first replica completes. We provide an exact closed-form analysis of Replication-d.
 We next introduce a much more general model, one which takes both the inherent job size distribution and the server-side variability into account. This is a departure from traditional queueing models which only allow for one "size" distribution. We propose and analyze a new task assignment policy, Replicate-Idle-Queue (RIQ), which is designed to perform well given these dual sources of variability.
Hair testing of GHB: an everlasting issue in forensic toxicology.

In this paper, the authors present a critical review of different studies regarding hair testing of endogenous γ-hydroxybutyrate (GHB), concentrations in chronic users, and values measured after a single GHB exposure in drug facilitated sexual assault (DFSA) cases together with the role of a recently identified GHB metabolite, GHB-glucuronide.



The following databases (up to March 2017) PubMed, Scopus and Web of Science were used, searching the following key words: γ-hydroxybutyrate, GHB, GHB glucuronide, hair. The main key words "GHB" and "γ-hydroxybutyrate" were searched singularly and then associated individually to each of the other keywords.



Of the 2304 sources found, only 20 were considered appropriate for the purpose of this paper. Summing up all the studies investigating endogenous GHB concentration in hair, a very broad concentration range from 0 to 12 ng/mg was found. In order to detect a single GHB dose in hair it is necessary to commonly wait 1 month for collecting hair and a segmental analysis of 3 or 5 mm fragments and the calculation of a ratio between the targeted segment and the others represent a reliable method to detect a single GHB intake considering that the ratios presently proposed vary from 3 and 10. The only two studies so far performed, investigating GHB-Glucuronide in hair, show that the latter does not seem to provide any diagnostic information regarding GHB exposure.



A practical operative protocol is proposed to be applied in all suspected cases of GHB-facilitated sexual assault (GHB-FSA).
Visualizing SAT Instances and Runs of the DPLL Algorithm

SAT-solvers have turned into essential tools in many areas of applied logic like, for example, hardware verification or satisfiability checking modulo theories. However, although recent implementations are able to solve problems with hundreds of thousands of variables and millions of clauses, much smaller instances remain unsolved. What makes a particular instance hard or easy is at most partially understood – and is often attributed to the instance’s internal structure. By converting SAT instances into graphs and applying established graph layout techniques, this internal structure can be visualized and thus serve as the basis of subsequent analysis. Moreover, by providing tools that animate the structure during the run of a SAT algorithm, dynamic changes of the problem instance become observable. Thus, we expect both to gain new insights into the hardness of the SAT problem and to help in teaching SAT algorithms.
Small-Size Monopole Antenna with Dual Band-Stop Function for Ultra-Wideband Wireless Communications

In this letter, a different method to design an ultra-wideband (UWB) monopole antenna with dual frequency bandstop performance is presented. The proposed antenna consists of a rectangular-ring radiating patch with a meander-line strip as a resonator protruded inside the ring, and a ground plane. The measured results reveal that the presented dual band-notched monopole antenna design exhibits an operating bandwidth (VSWR<2) form 2.8 GHz to 13.8 GHz with two notched bands, covering the 5.2/5.8 GHz, 3.5/5.5 GHz, and 4 GHz to suppress any interference from the wireless local area network (WLAN), worldwide interoperability microwave access (WiMAX) and C-band systems, respectively. Good return loss, antenna gain and radiation pattern characteristics are obtained in the frequency band of interest. The proposed monopole antenna configuration is simple, easy to fabricate and can be integrated into any UWB system. The antenna has a small dimension of 10×17 mm 2 .
Interactive and Iterative Annotation for Biomedical Entity Recognition

In this paper, we demonstrate the impact of interactive machine learning for the development of a biomedical entity recognition dataset using a human-into-the-loop approach: during annotation, a machine learning model is built on previous annotations and used to propose labels for subsequent annotation. To demonstrate that such interactive and iterative annotation speeds up the development of quality dataset annotation, we conduct two experiments. In the first experiment, we carry out an iterative annotation experimental simulation and show that only a handful of medical abstracts need to be annotated to produce suggestions that increase annotation speed. In the second experiment, clinical doctors have conducted a case study in annotating medical terms documents relevant for their research. The experiments validate our method qualitatively and quantitatively, and give rise to a more personalized, responsive information extraction technology.
Retrieving Lexical Semantics from Multilingual Corpora

This paper presents a technique to build a lexical resource used for annotation of parallel corpora where the t ags can be seen as multilingual ‘synsets’. The approach can be extended to add relationships between these synsets that ar e akin to WordNet relationships of synonymy and hypernymy. The paper also discusses how the success of this approach can be measured. The reported results are for English, German, Fre nch, and Greek using the Europarl parallel corpus.
Integrating typed and untyped code in a scripting language

Many large software systems originate from untyped scripting language code. While good for initial development, the lack of static type annotations can impact code-quality and performance in the long run. We present an approach for integrating untyped code and typed code in the same system to allow an initial prototype to smoothly evolve into an efficient and robust program. We introduce like types , a novel intermediate point between dynamic and static typing. Occurrences of like types variables are checked statically within their scope but, as they may be bound to dynamic values, their usage is checked dynamically. Thus like types provide some of the benefits of static typing without decreasing the expressiveness of the language. We provide a formal account of like types in a core object calculus and evaluate their applicability in the context of a new scripting language.
Parallelization of Neural Network Training for NLP with Hogwild

Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.
Proposal Learning Semantics of WikiTables

Recent research has resulted in the creation of many fact extraction systems. To be able to utilize the extracted facts to their full potential, it is essential to understand their semantics. Placing these extracted facts in an ontology is an effective way to provide structure, which facilitates better understanding of semantics. Today there are many systems that extract facts and organize them in an ontology, namely DBpedia, NELL, YAGO etc. While such ontologies are used in a variety of applications, including IBM’s Jeopardy-winning Watson system, they demand significant effort in their creation. They are either manually curated, or built using semi-supervised machine learning techniques. As the effort in the creation of an ontology is significant, it is often hard to organize facts extracted from a corpus of documents that is different from the one used to build these ontologies in the first place. The goal of this work is to be able to automatically construct ontologies, for a given set of entities, properties and relations. One key source of this data is the Wikipedia tables dataset. Wikipedia tables are unique in that they are a large (1.4 million) and heterogeneous set of tables that can be extracted at very high levels of precision. Rather than augmenting an existing ontology, which is a very challenging research problem in itself, I propose to automatically construct a new ontology by utilizing representations of entities, their attributes and relations. These representations will be learnt using unsupervised machine learning techniques on facts extracted from Wikipedia tables. Thus, the end system will not only extract facts from Wikipedia tables, but also automatically organize them in an Ontology to understand the semantics of Wikipedia tables better.
Predicting Cancer Relapse with Clinical Data: A Survey of Current Techniques

While cancer treatments are constantly advancing, there is still a real risk of relapse after potentially curative treatments. At the risk of adverse side effects, certain adjuvant treatments can be given to patients that are at high risk of recurrence. The challenge, however, is in finding the best tradeoff between these two extremes. Patients that are given more potent treatments, such as chemotherapy, radiation, or systemic treatment, can suffer unnecessary consequences, especially if the cancer does not return. Predictive modeling of recurrence can help inform patients and practitioners on a case-by-case basis, personalized for each patient. For large-scale predictive models to be built, structured data must be captured for a wide range of diverse patients. This paper explores current methods for building cancer recurrence risk models using structured clinical patient data.
Extracting Relations with Integrated Information Using Kernel Methods

Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis. Each source of information is represented by kernel functions. Then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. We present an evaluation of these methods on the 2004 ACE relation detection task, using Support Vector Machines, and show that each level of syntactic processing contributes useful information for this task. When evaluated on the official test data, our approach produced very competitive ACE value scores. We also compare the SVM with KNN on different kernels.