nils-herrmann/clean_scirepeval huggingface dataset:
Unexpected oocyst shedding by cats fed Toxoplasma gondii tachyzoites: in vivo stage conversion and strain variation.

Tachyzoites, bradyzoites (in tissue cysts), and sporozoites (in oocysts) are the three infectious stages of Toxoplasma gondii. The prepatent period (time to shedding of oocysts after primary infection) varies with the stage of T. gondii ingested by the cat. The prepatent period (pp) after ingesting bradyzoites is short (3-10 days) while it is long (18 days or longer) after ingesting oocysts or tachyzoites, irrespective of the dose. The conversion of bradyzoites to tachyzoites and tachyzoites to bradyzoites is biologically important in the life cycle of T. gondii. In the present paper, the pp was used to study in vivo conversion of tachyzoites to bradyzoites using two isolates, VEG and TgCkAr23. T. gondii organisms were obtained from the peritoneal exudates (pex) of mice inoculated intraperitoneally (i.p.) with these isolates and administered to cats orally by pouring in the mouth or by a stomach tube. In total, 94 of 151 cats shed oocysts after ingesting pex. The pp after ingesting pex was short (5-10 days) in 50 cats, intermediate (11-17) in 30 cats, and long (18 or higher) in 14 cats. The strain of T. gondii (VEG, TgCKAr23) or the stage (bradyzoite, tachyzoite, and sporozoite) used to initiate infection in mice did not affect the results. In addition, six of eight cats fed mice infected 1-4 days earlier shed oocysts with a short pp; the mice had been inoculated i.p. with bradyzoites of the VEG strain and their whole carcasses were fed to cats 1, 2, 3, or 4 days post-infection. Results indicate that bradyzoites may be formed in the peritoneal cavities of mice inoculated intraperitoneally with T. gondii and some bradyzoites might give rise directly to bradyzoites without converting to tachyzoites.
Modeling Human-Robot Collaboration in a Simulated Environment

In this paper, we describe a project that explores an open-sourced enhanced robot simulator SIGVerse towards researching a social human-robot interaction. Research on high level social human-robot interaction systems that includes collaboration and emotional intercommunication between people and robots requires a big amount of data based on embodied interaction experiments. However, the cost of developing real robots and performing many experiments can be very high. On another hand, virtual robot simulators are very limited in terms of interaction between simulated robots and real people. Thus we propose using an enhanced human-robot interaction simulator SIGVerse that enables users to join the virtual world occupied by simulated robots through immersive user interface. In this paper,we describe a collaborative human-robot interaction task where a virtual human agent is controlled remotely by human subjects to interact with an automatic virtual robot with implemented artificial emotional reactions. Our project sets the first steps to explore the potential of using an enhanced human-robot interaction simulator to build socially interactive robots that can serve in educational, team building, and collaborative task solving applications.
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination

All existing image enhancement methods, such as HDR tone mapping, cannot recover A/D quantization losses due to insufficient or excessive lighting, (underflow and overflow problems). The loss of image details due to A/D quantization is complete and it cannot be recovered by traditional image processing methods, but the modern data-driven machine learning approach offers a much needed cure to the problem. In this work we propose a novel approach to restore and enhance images acquired in low and uneven lighting. First, the ill illumination is algorithmically compensated by emulating the effects of artificial supplementary lighting. Then a DCNN trained using only synthetic data recovers the missing detail caused by quantization.
Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior

We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks. The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks. Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped). Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.
AASRI Conference on Sports Engineering and Computer Science ( SECS 2014 ) Mining Tracks of Competitive Video Games

The development and professionalization of a video game requires tools for analyzing the practice of the players and teams, their tactics and strategies. These games are very popular and by nature numerical, they provide many tracks that we analyzed in terms of team play. We studied Defense of the Ancients (DotA), a Multiplayer Online Battle Arena (MOBA), where two teams battle in a game very similar to rugby or American football. Through topological measures – area of polygon described by the players, inertia, diameter, distance to the base – that are independent of the exact nature of the game, we show that the outcome of the match can be relevantly predicted. Mining e-sport’s tracks is opening interest in further application of these tools for analyzing real time sport.
Forecasting the presence and intensity of hostility on Instagram using linguistic and social features

Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. While prior work has proposed automated methods to identify hostile comments in online discussions, these methods work retrospectively on comments that have already been posted, making it difficult to intervene before an interaction escalates. In this paper we instead consider the problem of forecasting future hostilities in online discussions, which we decompose into two tasks: (1) given an initial sequence of non-hostile comments in a discussion, predict whether some future comment will contain hostility; and (2) given the first hostile comment in a discussion, predict whether this will lead to an escalation of hostility in subsequent comments. Thus, we aim to forecast both the presence and intensity of hostile comments based on linguistic and social features from earlier comments. To evaluate our approach, we introduce a corpus of over 30K annotated Instagram comments from over 1,100 posts. Our approach is able to predict the appearance of a hostile comment on an Instagram post ten or more hours in the future with an AUC of .82 (task 1), and can furthermore distinguish between high and low levels of future hostility with an AUC of .91 (task 2).
Interstitial-lymphatic mechanisms in the control of extracellular fluid volume.

While the study of the physiochemical composition and structure of the interstitium on a molecular level is a large and important field in itself, the present review centered mainly on the functional consequences for the control of extracellular fluid volume. As pointed out in section I, a biological monitoring system for the total extracellular volume seems very unlikely because a major part of that volume is made up of multiple, separate, and functionally heterogeneous interstitial compartments. Even less likely is a selective volume control of each of these compartments by the nervous system. Instead, as shown by many studies cited in this review, a local autoregulation of interstitial volume is provided by automatic adjustment of the transcapillary Starling forces and lymph flow. Local vascular control of capillary pressure and surface area, of special importance in orthostasis, has been discussed in several recent reviews and was mentioned only briefly in this article. The gel-like consistency of the interstitium is attributed to glycosaminoglycans, in soft connective tissues mainly hyaluronan. However, the concept of a gel phase and a free fluid phase now seems to be replaced by the quantitatively more well-defined distribution spaces for glycosaminoglycans and plasma protein, apparently in osmotic equilibrium with each other. The protein-excluded space, determined mainly by the content of glycosaminoglycans and collagen, has been measured in vivo in many tissues, and the effect of exclusion on the oncotic buffering has been clarified. The effect of protein charge on its excluded volume and on interstitial hydraulic conductivity has been studied only in lungs and is only partly clarified. Of unknown functional importance is also the recent finding of a free interstitial hyaluronan pool with relatively rapid removal by lymph. The postulated preferential channels from capillaries to lymphatics have received little direct support. Thus the variation of plasma-to-lymph passage times for proteins may probably be ascribed to heterogeneity with respect to path length, linear velocity, and distribution volumes. Techniques for measuring interstitial fluid pressure have been refined and reevaluated, approaching some concensus on slightly negative control pressures in soft connective tissues (0 to -4 mmHg), zero, or slightly positive pressure in other tissues. Interstitial pressure-volume curves have been recorded in several tissues, and progress has been made in clarifying the dependency of interstitial compliance on glycosaminoglycan-osmotic pressure, collagen, and microfibrils.(ABSTRACT TRUNCATED AT 400
Rehabilitation Program for Caregivers about Patients ’ Post Autologous Bone Marrow Transplantation

Autologous stem cell transplant can be a curative therapy to restore normal hematopoiesis after myeloablative treatments in patients with malignancies.  To evaluate the effect of rehabilitation program for caregivers about patients’ post autologous bone marrow transplantation Research Design: A quasi-experimental design was used.  The study was conducted in Sheikh Zayed Specialized Hospital at Oncology Outpatient Clinic of Bone Marrow Transplantation Unit.  A purposive sample comprised; a total number of 60 patients, their age ranged from 21 to 50 years, free from any other chronic disease and the caregivers are living with the patients in the same home.  Two tools were used for data collection. First tool: An interviewing autologous bone marrow transplantation questionnaire for the patients and their caregivers was divided into five parts; Including: Socio-demographic data, knowledge of caregivers regarding autologous bone marrow transplant and side effect of chemotherapy, family caregivers’ practices according to their providing care related to post bone marrow transplantation, signs and symptoms, activities of daily living for patients and home environmental sanitation for the patients. Second tool: deals with physical examination assessment of the patients from head to toe.  61.7% of patients aged 30˂40 years, and 68.3 % were female. Regarding the type of relationship with the patients, 48.3% were the mother, 58.3% of patients who underwent autologous bone marrow transplantation had a sanitary environment and there were highly statistically significant differences between caregivers’ knowledge and practices pre/post program.  There were highly statistically significant differences between family caregivers' total knowledge, their practices, as well as their total caregivers’ knowledge, practices and patients’ independency level pre/post rehabilitation program. .  Counseling for family caregivers of patients who underwent autologous bone marrow transplantation and carrying out rehabilitation program for the patients and their caregivers to be performed properly during the rehabilitation period at caner hospitals such as 57357 Hospital and The National Cancer Institute in Cairo.
Online scheduling of workflow applications in grid environments

Scheduling workflow applications in grid environments is a great challenge, because it is an NPcomplete problem. Many heuristic methods have been presented in the literature and most of them deal with a single workflow application at a time. In recent years, several heuristic methods have been proposed to deal with concurrent workflows or online workflows, but they do not work with workflows composed of data-parallel tasks. In this paper, we present an online scheduling approach for multiple mixed-parallel workflows in grid environments. The proposed approach was evaluated with a series of simulation experiments and the results show that the proposed approach delivers good performance and outperforms other methods under various workloads.
Pitfalls and Best Practices in Algorithm Configuration

Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in the AI community since they replace tedious, irreproducible and error-prone manual parameter tuning and can lead to new state-of-the-art performance. However, practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. We identify several common issues and propose best practices for avoiding them, including a tool called GenericWrapper4AC for preventing the many possible problems in measuring the performance of the algorithm being optimized by executing it in a standardized, controlled manner.
Store Location Selection via Mining Search Query Logs of Baidu Maps

Choosing a good location when opening a new store is crucial for the future success of a business. Traditional methods include offline manual survey, which is very time consuming, and analytic models based on census data, which are unable to adapt to the dynamic market. The rapid increase of the availability of big data from various types of mobile devices, such as online query data and offline positioning data, provides us with the possibility to develop automatic and accurate data-driven prediction models for business store placement. In this paper, we propose a Demand Distribution Driven Store Placement (D3SP) framework for business store placement by mining search query data from Baidu Maps. D3SP first detects the spatial-temporal distributions of customer demands on different business services via query data from Baidu Maps, the largest online map search engine in China, and detects the gaps between demand and supply. Then we determine candidate locations via clustering such gaps. In the final stage, we solve the location optimization problem by predicting and ranking the number of customers. We not only deploy supervised regression models to predict the number of customers, but also learn to rank models to directly rank the locations. We evaluate our framework on various types of businesses in real-world cases, and the experiments results demonstrate the effectiveness of our methods. D3SP as the core function for store placement has already been implemented as a core component of our business analytics platform and could be potentially used by chain store merchants on Baidu Nuomi.
Cvpr 2006 Submission #****. Confidential Review Copy. Do Not Distribute. L a T E X Author Guidelines for Cvpr Proceedings Anonymous Cvpr Submission

The ABSTRACT is to be in fully-justified italicized text, at the top of the left-hand column, below the author and affiliation information. Use the word " Abstract " as the title, in 12-point Times, boldface type, centered relative to the column, initially capitalized. The abstract is to be in 10-point, single-spaced type. Leave two blank lines after the Abstract, then begin the main text. Look at previous CVPR abstracts to get a feel for style and length.
Ethnobotany of Costanoan Indians, California, based on collections by John P. Harrington

A great variety of native and introduced plant species were used as foods, medicines and raw materials by the Rumsen and Mutsun Costanoan peoples of central California. The information presented here has been abstracted from original unpublished field notes recorded during the 1920s and 1930s by John Peabody Harrington, who also directed the collection of some 500 plant specimens. The nature of Harrington’s data and their significance for California ethnobotany are described, followed by a summary of information on the ethnographic uses of each plant.
Big Data: Challenges, Opportunities and Realities

With the advent of Internet of Things (IoT) and Web 2.0 technologies, there has been a tremendous growth in the amount of data generated. This chapter emphasizes on the need for big data, technological advancements, tools and techniques being used to process big data are discussed. Technological improvements and limitations of existing storage techniques are also presented. Since, the traditional technologies like Relational Database Management System (RDBMS) have their own limitations to handle big data, new technologies have been developed to handle them and to derive useful insights. This chapter presents an overview of big data analytics, its application, advantages, and limitations. Few research issues and future directions are presented in this chapter.
Automation of street light for smart city

Energy consumption in metropolitan cities is increasing day by day. In every city, considerable amount of electricity is being used for the purpose of street lighting system. Some areas of the city may have low frequency of passerby, but it's observed that the amount of energy the street lights consume in these areas is same as that of areas with high frequency passerby. As a result enormous amount of energy is wasted without being used. In the proposed system, high intensity discharge lamps are replaced by LED's which can alter its intensity based on the need. Movement of vehicles is sensed using LDR (Light Dependent Resistor) and the intensity of the street light is reduced when not in use. The system also detects fault in the system and indicates it to the base station using GSM (Global System for Mobile communication) technology by sending SMS (short message service).
Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions

Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents’ preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one’s own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent’s and one’s own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers.
Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets.



This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data.



This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes.



We are currently writing Auto-ML's design document. We intend to finish our study by around the year 2022.



Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes.
A Formal Security Analysis of ERTMS Train to Trackside Protocols

This paper presents a formal analysis of the train to trackside communication protocols used in the European Railway Tra c Management System (ERTMS) standard, and in particular the EuroRadio protocol. This protocol is used to secure important commands sent between train and trackside, such as movement authority and emergency stop messages. We perform our analysis using the applied pi-calculus and the ProVerif tool. This provides a powerful and expressive framework for protocol analysis and allows to check a wide range of security properties based on checking correspondence assertions. We show how it is possible to model the protocol’s counter-style timestamps in this framework. We define ProVerif assertions that allow us to check for secrecy of long and short term keys, authenticity of entities, message insertion, deletion, replay and reordering. We find that the protocol provides most of these security features, however it allows undetectable message deletion and the forging of emergency messages. We discuss the relevance of these results and make recommendations to further enhance the security of ERTMS.