nils-herrmann/clean_scirepeval huggingface dataset:
A GIS-Coupled Hydrological Model System for the Watershed Assessment of Agricultural Nonpoint and Point Sources of Pollution

This paper introduces AVSWAT, a GIS based hydrological system linking the Soil and Water Assessment Tool (SWAT) water quality model and ArcView Geographic Information System software. The ?main purpose of AVSWAT is the combined assessment of nonpoint and point pollUtion loading at the watershed scale. The GIS component of the system, in addition to the traditional functions of data acquisition, storage, organization and display, implements advanced analytical methods with enhanced flexibility to improve the hydrological characterization of a study watershed. Intuitive user friendly graphic interfaces, also part of the GIS component, have been developed to provide an efficient interaction with the model and the associated parameter databases, and ultimately to simplify water quality assessments, while maintaining and increasing their reliability. This is also supported by SWAT, the core of the system, a complex, conceptual, hydrologic, continuous model with spatially explicit parameterization, building upon the United State Department of Agriculture (USDA) modeling experience. A step-by-step example application for a watershed in Central Texas is also included to verify the capability and illustrate some of the characteristics of the system which has been adopted by many users around the world. Address for correspondence: Mauro Di Luzio, Texas Agricultural Experiment Station, Blackland Research Center, Texas A&M University System, 720 East Blackland Road, Temple, TX 76502, USA.  Garsington Road, Oxford 0X4 2DQ, UK and 350 Main Street, Maiden, MA 02148, USA. 114 M Di Luzio, R Srinivasan and I C Arnold
GPS-Free Positioning in Mobile ad-hoc Networks

In this paper we consider the problem of node positioning in ad-hoc networks. We propose a distributed, infrastructure-free positioning algorithm that does not rely on Global Positioning System (GPS). The algorithm uses the distances between the nodes to build a relative coordinate system in which the node positions are computed in two dimensions. The main contribution of this work is to define and compute relative positions of the nodes in an ad-hoc network without using GPS. We further explain how the proposed approach can be applied to wide area ad-hoc networks.
Sex differences in personality traits and gender-related occupational preferences across 53 nations: testing evolutionary and social-environmental theories.

Using data from over 200,000 participants from 53 nations, I examined the cross-cultural consistency of sex differences for four traits: extraversion, agreeableness, neuroticism, and male-versus-female-typical occupational preferences. Across nations, men and women differed significantly on all four traits (mean ds = -.15, -.56, -.41, and 1.40, respectively, with negative values indicating women scoring higher). The strongest evidence for sex differences in SDs was for extraversion (women more variable) and for agreeableness (men more variable). United Nations indices of gender equality and economic development were associated with larger sex differences in agreeableness, but not with sex differences in other traits. Gender equality and economic development were negatively associated with mean national levels of neuroticism, suggesting that economic stress was associated with higher neuroticism. Regression analyses explored the power of sex, gender equality, and their interaction to predict men's and women's 106 national trait means for each of the four traits. Only sex predicted means for all four traits, and sex predicted trait means much more strongly than did gender equality or the interaction between sex and gender equality. These results suggest that biological factors may contribute to sex differences in personality and that culture plays a negligible to small role in moderating sex differences in personality.
Ad click prediction: a view from the trenches

Predicting ad click-through rates (CTR) is a massive-scale learning problem that is central to the multi-billion dollar online advertising industry. We present a selection of case studies and topics drawn from recent experiments in the setting of a deployed CTR prediction system. These include improvements in the context of traditional supervised learning based on an FTRL-Proximal online learning algorithm (which has excellent sparsity and convergence properties) and the use of per-coordinate learning rates.
 We also explore some of the challenges that arise in a real-world system that may appear at first to be outside the domain of traditional machine learning research. These include useful tricks for memory savings, methods for assessing and visualizing performance, practical methods for providing confidence estimates for predicted probabilities, calibration methods, and methods for automated management of features. Finally, we also detail several directions that did not turn out to be beneficial for us, despite promising results elsewhere in the literature. The goal of this paper is to highlight the close relationship between theoretical advances and practical engineering in this industrial setting, and to show the depth of challenges that appear when applying traditional machine learning methods in a complex dynamic system.
Mining State-Based Models from Proof Corpora

Interactive theorem provers have been used extensively to reason about various software/hardware systems and mathematical theorems. The key challenge when using an interactive prover is finding a suitable sequence of proof steps that will lead to a successful proof requires a significant amount of human intervention. This paper presents an automated technique that takes as input examples of successful proofs and infers an Extended Finite State Machine as output. This can in turn be used to generate proofs of new conjectures. Our preliminary experiments show that the inferred models are generally accurate (contain few false-positive sequences) and that representing existing proofs in such a way can be very useful when guiding new ones.
Pattern recognition in a compartmental model of a CA1 pyramidal neuron.

Computer simulation of a CA1 hippocampal pyramidal neuron is used to estimate the effects of synaptic and spatio-temporal noise on such a cell's ability to accurately calculate the weighted sum of its inputs, presented in the form of transient patterns of activity. Comparison is made between the pattern recognition capability of the cell in the presence of this noise and that of a noise-free computing unit in an artificial neural network model of a heteroassociative memory. Spatio-temporal noise due to the spatial distribution of synaptic input and quantal variance at each synapse degrade the accuracy of signal integration and consequently reduce pattern recognition performance in the cell. It is shown here that a certain degree of asynchrony in action potential arrival at different synapses, however, can improve signal integration. Signal amplification by voltage-dependent conductances in the dendrites, provided by synaptic NMDA receptors, and sodium and calcium ion channels, also improves integration and pattern recognition. While the biological sources of noise are significant when few patterns are stored in the associative memory of which the cell is a part, when large numbers of patterns are stored the noise from the other stored patterns comes to dominate the pattern recognition process. In this situation, the pattern recognition performance of the pyramidal cell is within a factor of two of that of the computing unit in the artificial neural network model.
The mysterious optimality of Naive Bayes: Estimation of the probability in the system of “classifiers”

Bayes Classifiers are widely used currently for recognition, identification and knowledge discovery. The fields of application are, for example, image processing, medicine, chemistry (QSAR). However, by mysterious way the Naive Bayes Classifier usually gives a very nice and good presentation of recognition. More complex models of Bayes Classifier cannot improve it considerably. We demonstrate here a very nice and simple proof of the Naive Bayes Classifier optimality that can explain this interesting fact. The derivation in the current paper is based on a paper of the author written in 2002.
Lung organoids: current uses and future promise.

Lungs are composed of a system of highly branched tubes that bring air into the alveoli, where gas exchange takes place. The proximal and distal regions of the lung contain epithelial cells specialized for different functions: basal, secretory and ciliated cells in the conducting airways and type II and type I cells lining the alveoli. Basal, secretory and type II cells can be grown in three-dimensional culture, with or without supporting stromal cells, and under these conditions they give rise to self-organizing structures known as organoids. This Review summarizes the different methods for generating organoids from cells isolated from human and mouse lungs, and compares their final structure and cellular composition with that of the airways or alveoli of the adult lung. We also discuss the potential and limitations of organoids for addressing outstanding questions in lung biology and for developing new drugs for disorders such as cystic fibrosis and asthma.
Learning mid-level features for recognition

Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be broken down into two steps: (1) a coding step, which performs a pointwise transformation of the descriptors into a representation better adapted to the task, and (2) a pooling step, which summarizes the coded features over larger neighborhoods. Several combinations of coding and pooling schemes have been proposed in the literature. The goal of this paper is threefold. We seek to establish the relative importance of each step of mid-level feature extraction through a comprehensive cross evaluation of several types of coding modules (hard and soft vector quantization, sparse coding) and pooling schemes (by taking the average, or the maximum), which obtains state-of-the-art performance or better on several recognition benchmarks. We show how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding. We provide theoretical and empirical insight into the remarkable performance of max pooling. By teasing apart components shared by modern mid-level feature extractors, our approach aims to facilitate the design of better recognition architectures.
Upper lip swelling caused by a large dentigerous cyst

Swelling of the upper lip can result from various diseases such as salivary tumors, infectious and inflammatory diseases and cysts. Among the latter, dentigerous cysts, typically involving unerupted teeth, are sometimes associated with supernumerary teeth in the maxillary anterior incisors region called the mesiodens. We report an unusual case of a large dentigerous cyst associated with an impacted mesiodens in a 42-year-old male who presented with a slow-growing swelling in the upper lip.
Pulling levers focused deterrence strategies and the prevention of gun homicide

A number of jurisdictions have been experimenting with new problem-oriented policing frameworks to understand and respond to gun violence among gang-involved offenders. These interventions are based on the “pulling levers” deterrence strategy that focuses criminal justice and social service attention on a small number of chronically offending gang members responsible for the bulk of urban gun violence problems. Unfortunately, there is relatively little rigorous evaluation evidence on the effectiveness of these approaches to violence prevention. In Stockton, California, an interagency task force implemented a pulling levers strategy to prevent gun homicide among gang-involved offenders. A U.S. Department of Justice-sponsored impact evaluation suggests that the pulling levers strategy was associated with a statistically significant decrease in the monthly number of gun homicide incidents in Stockton. A comparative analysis of gun homicide trends in Stockton relative to other midsize California cities also supports a unique program effect associated with the pulling levers intervention.
Energy-Efficient Architecture for FPGA-based Deep Convolutional Neural Networks with Binary Weights

This paper presents an energy-efficient, deep parallel Convolutional Neural Network (CNN) accelerator. By adopting a recently proposed binary weight method, the CNN computations are converted into multiplication-free processing. To allow parallel accessing and storing of data, we use two RAM banks, where each bank is composed of NRAM blocks corresponding to N-parallel processing. We also design a reconfigurable CNN computing unit in a divide-and-reuse to support a variable-size convolutional filter. Compared with full-precision computing on the MNIST and CIFAR-10 classification tasks, the inference Top-1 accuracy of the binary weight CNN has dropped by 1.21% and 1.34%, respectively. The hardware implementation results show that the proposed design can achieve 2100 GOPs with a 4.6 millisecond processing latency. The deep parallel accelerator exhibits 3X energy efficiency compared to a GPU-based design.
Inside-Outside and Forward-Backward Algorithms Are Just Backprop (tutorial paper)

A probabilistic or weighted grammar implies a posterior probability distribution over possible parses of a given input sentence. One often needs to extract information from this distribution, by computing the expected counts (in the unknown parse) of various grammar rules, constituents, transitions, or states. This requires an algorithm such as inside-outside or forward-backward that is tailored to the grammar formalism. Conveniently, each such algorithm can be obtained by automatically differentiating an “inside” algorithm that merely computes the log-probability of the evidence (the sentence). This mechanical procedure produces correct and efficient code. As for any other instance of back-propagation, it can be carried out manually or by software. This pedagogical paper carefully spells out the construction and relates it to traditional and nontraditional views of these algorithms.
Infinity-Norm Support Vector Machines Against Adversarial Label Contamination

Nowadays machine-learning algorithms are increasingly being applied in security-related applications like spam and malware detection, aiming to detect never-before-seen attacks and novel threats. However, such techniques may expose specific vulnerabilities that may be exploited by carefully-crafted attacks. Support Vector Machines (SVMs) are a well-known and widely-used learning algorithm. They make their decisions based on a subset of the training samples, known as support vectors. We first show that this behaviour poses risks to system security, if the labels of a subset of the training samples can be manipulated by an intelligent and adaptive attacker. We then propose a countermeasure that can be applied to mitigate this issue, based on infinity-norm regularization. The underlying rationale is to increase the number of support vectors and balance more equally their contribution to the decision function, to decrease the impact of the contaminating samples during training. Finally, we empirically show that the proposed defence strategy, referred to as Infinity-norm SVM, can significantly improve classifier security under malicious label contamination in a real-world classification task involving malware detection.
Towards a hybrid approach to Netflix Challenge

Today Recommendation systems  have become indispensible because of the sheer bulk of information made available to a user from web-services(Netflix, IMDB, Amazon and many others) and the need for personalized suggestions. Recommendation systems are a well studied research area. In the following work, we present our study on the Netflix Challenge . The Neflix Challenge can be summarized in the following way: ”Given a movie, predict the rating of a particular user based on the user’s prior ratings”. The performance of all such approaches is measured using the RMSE (root mean-squared error) of the submitted ratings from the actual ratings. Currently, the best system has an RMSE of 0.8616 . We obtained ratings from the following approaches:
Stabilizing Training of Generative Adversarial Networks through Regularization

Deep generative models based on Generative Adversarial Networks (GANs) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture, parameter initialization, and selection of hyper-parameters. This fragility is in part due to a dimensional mismatch or non-overlapping support between the model distribution and the data distribution, causing their density ratio and the associated f -divergence to be undefined. We overcome this fundamental limitation and propose a new regularization approach with low computational cost that yields a stable GAN training procedure. We demonstrate the effectiveness of this regularizer accross several architectures trained on common benchmark image generation tasks. Our regularization turns GAN models into reliable building blocks for deep learning.
Text message corpus: Applying natural language processing to mobile device forensics

The average mobile device user sends a large quantity of text and other short messages. These text message data are of great value to law enforcement investigators who may be analyzing a suspect's mobile device or social media profile for evidence of criminal activity. Current tools and methodologies for analyzing text and other short message data generally only allow for simple keyword searches, which is often a time-consuming task for law enforcement investigators. In addition, there are limited corpora available containing text message data. An initial corpus of text message data for experimental purposes has been developed and made available to the research community. A simple methodology is proposed for feature extraction. The format of the data is given as well as basic statistics, suggestions for possible use, and future work.
It's the writing on the wall: Whiteboards improve inpatient satisfaction with provider communication.

Although keeping patients informed is a part of quality hospital care, inpatients often report they are not well informed. The authors placed whiteboards in each patient room on medicine wards in their hospital and asked nurses and physicians to use them to improve communication with inpatients. The authors then examined the effect of these whiteboards by comparing satisfaction with communication of patients discharged from medical wards before and after whiteboards were placed to satisfaction with communication of patients from surgical wards that did not have whiteboards. Patient satisfaction scores (0-100 scale) with communication improved significantly on medicine wards: nurse communication (+6.4, P < .001), physician communication (+4.0, P = .04), and involvement in decision making (+6.3, P = .002). Patient satisfaction scores did not change significantly on surgical wards. There was no secular trend, and the authors excluded a trend in overall patient satisfaction. Whiteboards could be a simple and effective tool to increase inpatient satisfaction with communication.
How important is importance for prospective memory? A review

Forgetting to carry out an intention as planned can have serious consequences in everyday life. People sometimes even forget intentions that they consider as very important. Here, we review the literature on the impact of importance on prospective memory performance. We highlight different methods used to manipulate the importance of a prospective memory task such as providing rewards, importance relative to other ongoing activities, absolute importance, and providing social motives. Moreover, we address the relationship between importance and other factors known to affect prospective memory and ongoing task performance such as type of prospective memory task (time-, event-, or activity-based), cognitive loads, and processing overlaps. Finally, we provide a connection to motivation, we summarize the effects of task importance and we identify important venues for future research.
Lauric acid-rich medium-chain triglycerides can substitute for other oils in cooking applications and may have limited pathogenicity

Recently, medium-chain triglycerides (MCTs) containing a large fraction of lauric acid (LA) (C12)-about 30%-have been introduced commercially for use in salad oils and in cooking applications. As compared to the long-chain fatty acids found in other cooking oils, the medium-chain fats in MCTs are far less likely to be stored in adipose tissue, do not give rise to 'ectopic fat' metabolites that promote insulin resistance and inflammation, and may be less likely to activate macrophages. When ingested, medium-chain fatty acids are rapidly oxidised in hepatic mitochondria; the resulting glut of acetyl-coenzyme A drives ketone body production and also provokes a thermogenic response. Hence, studies in animals and humans indicate that MCT ingestion is less obesogenic than comparable intakes of longer chain oils. Although LA tends to raise serum cholesterol, it has a more substantial impact on high density lipoprotein (HDL) than low density lipoprotein (LDL) in this regard, such that the ratio of total cholesterol to HDL cholesterol decreases. LA constitutes about 50% of the fatty acid content of coconut oil; south Asian and Oceanic societies which use coconut oil as their primary source of dietary fat tend to be at low cardiovascular risk. Since ketone bodies can exert neuroprotective effects, the moderate ketosis induced by regular MCT ingestion may have neuroprotective potential. As compared to traditional MCTs featuring C6-C10, laurate-rich MCTs are more feasible for use in moderate-temperature frying and tend to produce a lower but more sustained pattern of blood ketone elevation owing to the more gradual hepatic oxidation of ingested laurate.
A System for Speed and Torque Control of DC Motors with Application to Small Snake Robots

This paper describes a custom Low Power Motor Controller (LoPoMoCo) that was developed for a 34-axis robot system currently being designed for Minimally Invasive Surgery (MIS) of the upper airways. The robot system will includes three robot arms equipped with small snake-like mechanisms, which challenge the controller design due to their requirement for precise sensing and control of low motor currents. The controller hardware also provides accurate velocity estimate from incremental encoder feedback and can selectively be operated in speed or torque control mode. The experimental results demonstrate that the controller can measure applied loads with a resolution of , even though the transmission is nonbackdriveable. Although the controller was designed for this particular robot, it is applicable to other systems requiring torque monitoring capabilities.