nils-herrmann/clean_scirepeval huggingface dataset:
Biological subtypes of breast cancer: Prognostic and therapeutic implications.

Breast cancer is a heterogeneous complex of diseases, a spectrum of many subtypes with distinct biological features that lead to differences in response patterns to various treatment modalities and clinical outcomes. Traditional classification systems regarding biological characteristics may have limitations for patient-tailored treatment strategies. Tumors with similar clinical and pathological presentations may have different behaviors. Analyses of breast cancer with new molecular techniques now hold promise for the development of more accurate tests for the prediction of recurrence. Gene signatures have been developed as predictors of response to therapy and protein gene products that have direct roles in driving the biology and clinical behavior of cancer cells are potential targets for the development of novel therapeutics. The present review summarizes current knowledge in breast cancer molecular biology, focusing on novel prognostic and predictive factors.
The Virtuous Machine - Old Ethics for New Technology?

Modern AI and robotic systems are characterized by a high and ever-increasing level of autonomy. At the same time, their applications in fields such as autonomous driving, service robotics and digital personal assistants move closer to humans. From the combination of both developments emerges the field of AI ethics which recognizes that the actions of autonomous machines entail moral dimensions and tries to answer the question of how we can build moral machines. In this paper we argue for taking inspiration from Aristotelian virtue ethics by showing that it forms a suitable combination with modern AI due to its focus on learning from experience. We furthermore propose that imitation learning from moral exemplars, a central concept in virtue ethics, can solve the value alignment problem. Finally, we show that an intelligent system endowed with the virtues of temperance and friendship to humans would not pose a control problem as it would not have the desire for limitless
Intrarectal lidocaine is an effective treatment for abdominal pain associated with diarrhea-predominant irritable bowel syndrome.

Irritable bowel syndrome (IBS) is one of the most common disorders seen by gastroenterologists. Visceral hypersensitivity is now well recognized as a clinical marker for the disease. Intrarectal lidocaine has been previously shown to decrease pain report from rectal distension in patients with IBS without any significant serum lidocaine levels. We conducted a prospective, double-blind, crossover trial on 10 patients with IBS to evaluate the effects of 300 mg intrarectal lidocaine jelly on abdominal pain. Ten Caucasian premenopausal women who met the Rome II criteria for diarrhea-predominant IBS were recruited into the study. All of the patients that participated had intermittent left lower quadrant pain and diarrhea. Each patient participated in 2 sessions in which saline jelly (placebo) and lidocaine jelly was administered on a double-blind, crossover basis. Patients participated in these sessions at a time when their ongoing pain was at least 3 on a 0 to 10 visual analogue scale. In comparison to placebo saline jelly, lidocaine jelly significantly decreased abdominal pain (P < .02) for at least 4 hours. None of the patients experienced any side effects. Intrarectal lidocaine may be a potentially useful treatment for chronic abdominal pain in IBS.



The possible presence of abnormal sodium channels in the rectal and or colonic visceral afferents of patients with IBS might serve as a clue as to the effectiveness of rectal lidocaine. The dose of lidocaine used in this study may be of sufficient strength to normalize aberrant sodium channels that may be present in the colon of patients with IBS without affecting normal sodium channels of either IBS or control subjects.
Sequential Bayesian Optimisation for Spatial-Temporal Monitoring

Bayesian Optimisation has received considerable attention in recent years as a general methodology to find the maximum of costly-to-evaluate objective functions. Most existing BO work focuses on where to gather a set of samples without giving special consideration to the sampling sequence, or the costs or constraints associated with that sequence. However, in real-world sequential decision problems such as robotics, the order in which samples are gathered is paramount, especially when the robot needs to optimise a temporally non-stationary objective function. Additionally, the state of the environment and sensing platform determine the type and cost of samples that can be gathered. To address these issues, we formulate Sequential Bayesian Optimisation (SBO) with side-state information within a Partially Observed Markov Decision Process (POMDP) framework that can accommodate discrete and continuous observation spaces. We build on previous work using Monte-Carlo Tree Search (MCTS) and Upper Confidence bound for Trees (UCT) for POMDPs and extend it to work with continuous state and observation spaces. Through a series of experiments on monitoring a spatial-temporal process with a mobile robot, we show that our UCTbased SBO POMDP optimisation outperforms myopic and non-myopic alternatives.
Early Visually Evoked Electrophysiological Responses Over the Human Brain (P1, N170) Show Stable Patterns of Face-Sensitivity from 4 years to Adulthood

Whether the development of face recognition abilities truly reflects changes in how faces, specifically, are perceived, or rather can be attributed to more general perceptual or cognitive development, is debated. Event-related potential (ERP) recordings on the scalp offer promise for this issue because they allow brain responses to complex visual stimuli to be relatively well isolated from other sensory, cognitive and motor processes. ERP studies in 5- to 16-year-old children report large age-related changes in amplitude, latency (decreases) and topographical distribution of the early visual components, the P1 and the occipito-temporal N170. To test the face specificity of these effects, we recorded high-density ERPs to pictures of faces, cars, and their phase-scrambled versions from 72 children between the ages of 4 and 17, and a group of adults. We found that none of the previously reported age-dependent changes in amplitude, latency or topography of the P1 or N170 were specific to faces. Most importantly, when we controlled for age-related variations of the P1, the N170 appeared remarkably similar in amplitude and topography across development, with much smaller age-related decreases in latencies than previously reported. At all ages the N170 showed equivalent face-sensitivity: it had the same topography and right hemisphere dominance, it was absent for meaningless (scrambled) stimuli, and larger and earlier for faces than cars. The data also illustrate the large amount of inter-individual and inter-trial variance in young children's data, which causes the N170 to merge with a later component, the N250, in grand-averaged data. Based on our observations, we suggest that the previously reported "bi-fid" N170 of young children is in fact the N250. Overall, our data indicate that the electrophysiological markers of face-sensitive perceptual processes are present from 4 years of age and do not appear to change throughout development.
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function f . Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer simulation. We can use these approximations to eliminate low function value regions cheaply and use the expensive evaluations of f in a small but promising region and speedily identify the optimum. We formalise this task as a multi-fidelity bandit problem where the target function and its approximations are sampled from a Gaussian process. We develop MF-GP-UCB, a novel method based on upper confidence bound techniques. In our theoretical analysis we demonstrate that it exhibits precisely the above behaviour, and achieves better regret than strategies which ignore multi-fidelity information. MF-GP-UCB outperforms such naive strategies and other multi-fidelity methods on several synthetic and real experiments.
Nutrient Digestibility and Gas Production of Some Tropical Feeds Used in Ruminant Diets Estimated by the in vivo and in vitro Gas Production Techniques

Some feedstuffs which used in ruminants diet (corn grain, soybean meal, wheat bran and alfalfa) were analyzed for chemical composition, apparent in vivo nutrient digestibility, in vitro fermentation gas production and metabolizable energy. Chemical composition of test feeds differed in nutrient contents. Initially apparent in vivo digestibility of alfalfa nutrients were obtained then digestibility of nutrients for the other test feeds were determined by difference method, using 16 Ghezel mature rams (mean weight of 43.9±4 kg). In vivo DM, CP, NDF and OM apparent digestibility were different among the test feeds (p<0.05). Regarding to the results, corn grain had a high DM and OM digestibility between test feeds and soybean meal had a high CP and NDF digestibility between test feeds (p<0.05). Cumulative gas production was recorded at 2, 4, 8, 12, 16, 24, 36, 48, 72 and 96 h of incubation and the equation of p = A (1-e-ct) was used to describe the kinetics of gas production. Potential gas production (A) and rates of gas production (c) differed (p<0.01) among feeds. Corn grain showed higher potential gas production (A) (326.5 mL g DM) and wheat bran had higher rate of gas production (c) (0.097 h) than the other feeds, inverses alfalfa (257.6 mL g DM) and corn grain (0.048 h) had lower potential gas production and rate of gas production than the other test feeds, respectively. The metabolizable energy (MJ kg DM) content of feeds was calculated using in vivo organic matter digestibility and gas production data. According to in vivo organic matter digestibility data, the ME values ranged from 9.2 in alfalfa to 13.3 MJ kg DM in corn grain. It was concluded that regarding to different chemical composition of test feeds, the in vivo digestibility, in vitro gas production and ME of feeds showed different values.
A GPU-based index to support interactive spatio-temporal queries over historical data

There are increasing volumes of spatio-temporal data from various sources such as sensors, social networks and urban environments. Analysis of such data requires flexible exploration and visualizations, but queries that span multiple geographical regions over multiple time slices are expensive to compute, making it challenging to attain interactive speeds for large data sets. In this paper, we propose a new indexing scheme that makes use of modern GPUs to efficiently support spatio-temporal queries over point data. The index covers multiple dimensions, thus allowing simultaneous filtering of spatial and temporal attributes. It uses a block-based storage structure to speed up OLAP-type queries over historical data, and supports query processing over in-memory and disk-resident data. We present different query execution algorithms that we designed to allow the index to be used in different hardware configurations, including CPU-only, GPU-only, and a combination of CPU and GPU. To demonstrate the effectiveness of our techniques, we implemented them on top of MongoDB and performed an experimental evaluation using two real-world data sets: New York City's (NYC) taxi data - consisting of over 868 million taxi trips spanning a period of five years, and Twitter posts - over 1.1 billion tweets collected over a period of 14 months. Our results show that our GPU-based index obtains interactive, sub-second response times for queries over large data sets and leads to at least two orders of magnitude speedup over spatial indexes implemented in existing open-source and commercial database systems.
A reliable routing protocol for VANET communications

Vehicular Ad-hoc Network (VANET) is an emerging new technology to enable communications among vehicles and nearby roadside infrastructures to provide intelligent transportation applications. In order to provide stable connections between vehicles, a reliable routing protocol is needed. Currently, there are several routing protocols designed for MANETs could be applied to VANETs. However, due to the unique characteristics of VANETs, the results are not encouraging. In this paper, we propose a new routing protocol named AODV-VANET, which incorporates the vehicles' movement information into the route discovery process based on Ad hoc On-Demand Distance Vector (AODV). A Total Weight of the Route is introduced to choose the best route together with an expiration time estimation to minimize the link breakages. With these modifications, the proposed protocol is able to achieve better routing performances.
Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues

In recent years, autonomous driving algorithms using low-cost vehicle-mounted cameras have attracted increasing endeavors from both academia and industry. There are multiple fronts to these endeavors, including object detection on roads, 3-D reconstruction etc., but in this work we focus on a visionbased model that directly maps raw input images to steering angles using deep networks. This represents a nascent research topic in computer vision. The technical contributions of this work are three-fold. First, the model is learned and evaluated on real human driving videos that are time-synchronized with other vehicle sensors. This differs from many prior models trained from synthetic data in racing games. Second, state-of-the-art models, such as PilotNet, mostly predict the wheel angles independently on each video frame, which contradicts common understanding of driving as a stateful process. Instead, our proposed model strikes a combination of spatial and temporal cues, jointly investigating instantaneous monocular camera observations and vehicle’s historical states. This is in practice accomplished by inserting carefully-designed recurrent units (e.g., LSTM and Conv-LSTM) at proper network layers. Third, to facilitate the interpretability of the learned model, we utilize a visual back-propagation scheme for discovering and visualizing image regions crucially influencing the final steering prediction. Our experimental study is based on about 6 hours of human driving data provided by Udacity. Comprehensive quantitative evaluations demonstrate the effectiveness and robustness of our model, even under scenarios like drastic lighting changes and abrupt turning. The comparison with other state-of-the-art models clearly reveals its superior performance in predicting the due wheel angle for a self-driving car.
TeleSpiro: A low-cost mobile spirometer for resource-limited settings

Chronic obstructive pulmonary disease (COPD), a disabling combination of emphysema and chronic bronchitis, relies on spirometric lung function measurements for clinical diagnosis and treatment. Because spirometers are unavailable in most of the developing world, this project developed a low cost point of care spirometer prototype for the mobile phone called the “TeleSpiro.” The key contributions of this work are the design of a novel repeat-use, sterilisable, low cost, phone-powered prototype meeting developing world user requirements. A differential pressure sensor, dual humidity/pressure sensor, microcontroller and USB hardware were mounted on a printed circuit board for measurement of air flow in a custom machine-lathed respiratory air flow tube. The embedded circuit electronics were programmed to transmit data to and receive power directly from either a computer or Android smartphone without the use of batteries. Software was written to filter and extract respiratory cycles from the digitised data. Differential pressure signals from Telespiro showed robust, reproducible responses to the delivery of physiologic lung volumes. The designed device satisfied the stringent design criteria of resource-limited settings and makes substantial inroads in providing evidence-based chronic respiratory disease management.
Noble metal-free hydrazine fuel cell catalysts: EPOC effect in competing chemical and electrochemical reaction pathways.

We report the discovery of a highly active Ni-Co alloy electrocatalyst for the oxidation of hydrazine (N(2)H(4)) and provide evidence for competing electrochemical (faradaic) and chemical (nonfaradaic) reaction pathways. The electrochemical conversion of hydrazine on catalytic surfaces in fuel cells is of great scientific and technological interest, because it offers multiple redox states, complex reaction pathways, and significantly more favorable energy and power densities compared to hydrogen fuel. Structure-reactivity relations of a Ni(60)Co(40) alloy electrocatalyst are presented with a 6-fold increase in catalytic N(2)H(4) oxidation activity over today's benchmark catalysts. We further study the mechanistic pathways of the catalytic N(2)H(4) conversion as function of the applied electrode potential using differentially pumped electrochemical mass spectrometry (DEMS). At positive overpotentials, N(2)H(4) is electrooxidized into nitrogen consuming hydroxide ions, which is the fuel cell-relevant faradaic reaction pathway. In parallel, N(2)H(4) decomposes chemically into molecular nitrogen and hydrogen over a broad range of electrode potentials. The electroless chemical decomposition rate was controlled by the electrode potential, suggesting a rare example of a liquid-phase electrochemical promotion effect of a chemical catalytic reaction ("EPOC"). The coexisting electrocatalytic (faradaic) and heterogeneous catalytic (electroless, nonfaradaic) reaction pathways have important implications for the efficiency of hydrazine fuel cells.
Futura: design for collaborative learning and game play on a multi-touch digital tabletop

This paper introduces a collaborative learning game called Futura: The Sustainable Futures Game, which is implemented on a custom multi-touch digital tabletop platform. The goal of the game is to work with other players to support a growing population as time passes while minimizing negative impact on the environment. The design-oriented research goal of the project is to explore the novel design space of collaborative, multi-touch tabletop games for learning. Our focus is on identifying and understanding key design factors of importance in creating opportunities for learning. We use four theoretical perspectives as lenses through which we conceptualize our design intentions and inform our analysis. These perspectives are: experiential learning, constructivist learning, collaborative learning, and game theory. In this paper we discuss design features that enable collaborative learning, present the results from two observational studies, and compare our findings to other guidelines in order to contribute to the growing body of empirically derived design guidelines for tangible, embodied and embedded interaction.
Active Virtual Ground— Single-Phase Transformerless Grid-Connected Voltage Source Inverter Topology

An efficient single-phase Transformerless grid-connected voltage source inverter topology by using the proposed active virtual ground (AVG) technique is presented. With the AVG, the conventional output L filter can be reconfigured to LCL structure without adding additional inductor. High-frequency differential mode current ripple can be significantly suppressed comparing to the available single-phase grid-connected inverter topologies. Additionally, strong attenuation to the high-frequency common-mode current is achieved. It is particularly important for some applications such as photovoltaic and motor drives. High efficiency can be achieved due to fewer components involved in the conduction loss. Cost of the magnetic device can be reduced since the required inductance of the filter becomes smaller. Performance of the proposed inverter has been evaluated analytically. Experimental verification is performed on a 1-kW, 400-V input, and 110-V/60-Hz output prototype.
The era of Big Spatial Data

In this tutorial, we present the recent work in the database community for handling Big Spatial Data. This topic became very hot due to the recent explosion in the amount of spatial data generated by smartphones, satellites and medical devices, among others. This tutorial goes beyond the use of existing systems as-is (e.g., Hadoop, Spark or Impala), and digs deep into the core components of big systems (e.g., indexing and query processing) to describe how they are designed to handle big spatial data. During this 90-minute tutorial, we review the state-of-the-art work in the area of Big Spatial Data while classifying the existing research efforts according to the implementation approach, underlying architecture, and system components. In addition, we provide case studies of full-fledged systems and applications that handle Big Spatial Data which allows the audience to better comprehend the whole tutorial.
A proposed schema for classifying human research biobanks.

Human research biobanks have rapidly expanded in the past 20 years, in terms of both their complexity and utility. To date there exists no agreement upon classification schema for these biobanks. This is an important issue to address for several reasons: to ensure that the diversity of biobanks is appreciated, to assist researchers in understanding what type of biobank they need access to, and to help institutions/funding bodies appreciate the varying level of support required for different types of biobanks. To capture the degree of complexity, specialization, and diversity that exists among human research biobanks, we propose here a new classification schema achieved using a conceptual classification approach. This schema is based on 4 functional biobank "elements" (donor/participant, design, biospecimens, and brand), which we feel are most important to the major stakeholder groups (public/participants, members of the biobank community, health care professionals/researcher users, sponsors/funders, and oversight bodies), and multiple intrinsic features or "subelements" (eg, the element "biospecimens" could be further classified based on preservation method into fixed, frozen, fresh, live, and desiccated). We further propose that the subelements relating to design (scale, accrual, data format, and data content) and brand (user, leadership, and sponsor) should be specifically recognized by individual biobanks and included in their communications to the broad stakeholder audience.
G2o: A general framework for graph optimization

Many popular problems in robotics and computer vision including various types of simultaneous localization and mapping (SLAM) or bundle adjustment (BA) can be phrased as least squares optimization of an error function that can be represented by a graph. This paper describes the general structure of such problems and presents g2o, an open-source C++ framework for optimizing graph-based nonlinear error functions. Our system has been designed to be easily extensible to a wide range of problems and a new problem typically can be specified in a few lines of code. The current implementation provides solutions to several variants of SLAM and BA. We provide evaluations on a wide range of real-world and simulated datasets. The results demonstrate that while being general g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems.