nils-herrmann/clean_scirepeval huggingface dataset:
Detection of Composite Communities in Multiplex Biological Networks

The detection of community structure is a widely accepted means of investigating the principles governing biological systems. Recent efforts are exploring ways in which multiple data sources can be integrated to generate a more comprehensive model of cellular interactions, leading to the detection of more biologically relevant communities. In this work, we propose a mathematical programming model to cluster multiplex biological networks, i.e. multiple network slices, each with a different interaction type, to determine a single representative partition of composite communities. Our method, known as SimMod, is evaluated through its application to yeast networks of physical, genetic and co-expression interactions. A comparative analysis involving partitions of the individual networks, partitions of aggregated networks and partitions generated by similar methods from the literature highlights the ability of SimMod to identify functionally enriched modules. It is further shown that SimMod offers enhanced results when compared to existing approaches without the need to train on known cellular interactions.
Algorithms for the multi-armed bandit problem

The stochastic multi-armed bandit problem is an important model for studying the explorationexploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms. Three important observations can be made from our results. Firstly, simple heuristics such as -greedy and Boltzmann exploration outperform theoretically sound algorithms on most settings by a significant margin. Secondly, the performance of most algorithms varies dramatically with the parameters of the bandit problem. Our study identifies for each algorithm the settings where it performs well, and the settings where it performs poorly. These properties are not described by current theory, even though they can be exploited in practice in the design of heuristics. Thirdly, the algorithms’ performance relative each to other is affected only by the number of bandit arms and the variance of the rewards. This finding may guide the design of subsequent empirical evaluations. In the second part of the paper, we turn our attention to an important area of application of bandit algorithms: clinical trials. Although the design of clinical trials has been one of the principal practical problems motivating research on multi-armed bandits, bandit algorithms have never been evaluated as potential treatment allocation strategies. Using data from a real study, we simulate the outcome that a 2001-2002 clinical trial would have had if bandit algorithms had been used to allocate patients to treatments. We find that an adaptive trial would have successfully treated at least 50% more patients, while significantly reducing the number of adverse effects and increasing patient retention. At the end of the trial, the best treatment could have still been identified with a high level of statistical confidence. Our findings demonstrate that bandit algorithms are attractive alternatives to current adaptive treatment allocation strategies.
Improving Breast Cancer Surgery: A Classification and Quadrant per Quadrant Atlas for Oncoplastic Surgery

Oncoplastic surgery (OPS) has emerged as a new approach for extending breast conserving surgery (BCS) possibilities, reducing both mastectomy and re-excision rates, while avoiding breast deformities. OPS is based upon the integration of plastic surgery techniques for immediate reshaping after wide excision for breast cancer. A simple guide for choosing the appropriate OPS procedure is not available. To develop an Atlas and guideline for oncoplastic surgery (OPS) to help in patient selection and choice of optimal surgical procedure for breast cancer patients undergoing BCS. We stratify OPS into two levels based on excision volume and the complexity of the reshaping technique. For resections less than 20% of the breast volume (level I OPS), a step-by-step approach allows easy reshaping of the breast. For larger resections (level II OPS), a mammoplasty technique is required. We identified three elements that can be used for patient selection and for determination of the appropriate OPS technique: excision volume, tumor location, and glandular density. For level II techniques, we defined a quadrant per quadrant Atlas that offers a different mammoplasty for each quadrant of the breast. OPS is the “third pathway” between standard BCS and mastectomy. The OPS classification and Atlas improves patient selection and allows a uniform approach for surgeons. It proposes a specific solution for different scenarios and helps improve breast conservation outcomes.
Sales Promotion Preferences : A Demographic Analysis

It would be a significant understatement to say that sales promotion is enjoying a dominant role in the promotional mixes of most consumer goods companies. The 1998 Cox Direct 20th Annual Survey of Promotional Practices suggests that many companies spend as much as 75% of their total promotional budgets on sales promotion and only 25% on advertising. This is up from 57% spent on sales promotions in 1981 (Landler and DeGeorge). The reasons for this unprecedented growth have been welldocumented. Paramount among these is the desire on the part of many organizations for a quick bolstering of sales. The obvious corollary to this is the desire among consumer groups for increased value in the products they buy. Value can be defined as the ratio of perceived benefits to price, and is linked to performance and meeting consumers' expectations (Zeithaml 1988). In today's value-conscious environment, marketers must stress the overall value of their products (Blackwell, Miniard and Engel 2001). Consumers have reported that coupons, price promotions and good value influence 75 80% of their brand choice decisions (Cox 1998). Today, "many Americans, brought up on a steady diet of commercials, view advertising with cynicism or indifference. With less money to shop, they're far more apt to buy on price" (Landler and DeGeorge 1991, 68).
A Convergence Theory for Deep Learning via Over-Parameterization

Deep neural networks (DNNs) have demonstrated dominating performance in many fields; since AlexNet, the neural networks used in practice are going wider and deeper. On the theoretical side, a long line of works have been focusing on why we can train neural networks when there is only one hidden layer. The theory of multi-layer networks remains somewhat unsettled. In this work, we prove why simple algorithms such as stochastic gradient descent (SGD) can find global minima on the training objective of DNNs in polynomial time. We only make two assumptions: the inputs do not degenerate and the network is over-parameterized. The latter means the number of hidden neurons is sufficiently large: polynomial in L, the number of DNN layers and in n, the number of training samples. As concrete examples, on the training set and starting from randomly initialized weights, we show that SGD attains 100% accuracy in classification tasks, or minimizes regression loss in linear convergence speed ε ∝ e−Ω(T , with a number of iterations that only scales polynomial in n and L. Our theory applies to the widely-used but non-smooth ReLU activation, and to any smooth and possibly non-convex loss functions. In terms of network architectures, our theory at least applies to fully-connected neural networks, convolutional neural networks (CNN), and residual neural networks (ResNet). ∗V1 appears on arXiv on this date and no new result is added since then. V2 adds citations and V3/V4 polish writing. This work was done when Yuanzhi Li and Zhao Song were 2018 summer interns at Microsoft Research Redmond. When this work was performed, Yuanzhi Li was also affiliated with Princeton, and Zhao Song was also affiliated with UW and Harvard. We would like to specially thank Greg Yang for many enlightening discussions, thank Ofer Dekel, Sebastien Bubeck, and Harry Shum for very helpful conversations, and thank Jincheng Mei for carefully checking the proofs of this paper. ar X iv :1 81 1. 03 96 2v 4 [ cs .L G ] 4 F eb 2 01 9
FlowMon: Detecting Malicious Switches in Software-Defined Networks

Software-Defined Networking (SDN) introduces a new communication network management paradigm and has gained much attention recently. In SDN, a network controller overlooks and manages the entire network by configuring routing mechanisms for underlying switches. The switches report their status to the controller periodically, such as port statistics and flow statistics, according to their communication protocol. However, switches may contain vulnerabilities that can be exploited by attackers. A compromised switch may not only lose its normal functionality, but it may also maliciously paralyze the network by creating network congestions or packet loss. Therefore, it is important for the system to be able to detect and isolate malicious switches. In this work, we investigate a methodology for an SDN controller to detect compromised switches through real-time analysis of the periodically collected reports. Two types of malicious behavior of compromised switches are investigated: packet dropping and packet swapping. We proposed two anomaly detection algorithms to detect packet droppers and packet swappers. Our simulation results show that our proposed methods can effectively detect packet droppers and swappers. To the best of our knowledge, our work is the first to address malicious switches detection using statistics reports in SDN.
Hyperspectral Image Classification with Convolutional Neural Networks

Hyperspectral image (HSI) classification is one of the most widely used methods for scene analysis from hyperspectral imagery. In the past, many different engineered features have been proposed for the HSI classification problem. In this paper, however, we propose a feature learning approach for hyperspectral image classification based on convolutional neural networks (CNNs). The proposed CNN model is able to learn structured features, roughly resembling different spectral band-pass filters, directly from the hyperspectral input data. Our experimental results, conducted on a commonly-used remote sensing hyperspectral dataset, show that the proposed method provides classification results that are among the state-of-the-art, without using any prior knowledge or engineered features.
Acquiring Knowledge of Affective Events from Blogs Using Label Propagation

Many common events in our daily life affect us in positive and negative ways. For example, going on vacation is typically an enjoyable event, while being rushed to the hospital is an undesirable event. In narrative stories and personal conversations, recognizing that some events have a strong affective polarity is essential to understand the discourse and the emotional states of the affected people. However, current NLP systems mainly depend on sentiment analysis tools, which fail to recognize many events that are implicitly affective based on human knowledge about the event itself and cultural norms. Our goal is to automatically acquire knowledge of stereotypically positive and negative events from personal blogs. Our research creates an event context graph from a large collection of blog posts and uses a sentiment classifier and semi-supervised label propagation algorithm to discover affective events. We explore several graph configurations that propagate affective polarity across edges using local context, discourse proximity, and event-event co-occurrence. We then harvest highly affective events from the graph and evaluate the agreement of the polarities with human judgements.
Basic Concepts on AUTOSAR Development

AUTOSAR(AUTomotiveOpen System ARchitecture) is an open and standardized automotive software architecture, jointly developed by automobile manufacturers, suppliers and tool developers. The AUTOSAR standard will serve as a platform upon which future vehicle applications will be implemented and will also serve to minimize the current barriers between functional domains. To improve cost-efficiency and reusability, AUTOSAR separates application software from the associated hardware. This stage, AUTOSAR is still in the promotion phase, and many countries are committed to its applications, including Japan and South Korea. It will widespread in automotive industry. This paper showed the basic concepts of AUTOSAR and comparisons of some confusing concepts for future in-depth study.
Design of Hough transform hardware accelerator for lane detection

The Hough transform is a well-known straight line detection algorithm and it has been widely used for many lane detection algorithms. However, its real-time operation is not guaranteed due to its high computational complexity. In this paper, we designed a Hough transform hardware accelerator on FPGA to process it in real time. Its FPGA logic area usage was reduced by limiting the angles of the lines to (-20, 20) degrees which are enough for lane detection applications, and its arithmetic computations were performed in parallel to speed up the processing time. As a result of FPGA synthesis using Xilinx Vertex-5 XC5VLX330 device, it occupies 4,521 slices and 25.6Kbyte block memory giving performance of 10,000fps in VGA images(5000 edge points). The proposed hardware on FPGA (0.1ms) is 450 times faster than the software implementation on ARM Cortex-A9 1.4GHz (45ms). Our Hough transform hardware was verified by applying it to the newly developed LDWS (lane departure warning system).
Enhanced ethanol formation by Clostridium thermocellum via pyruvate decarboxylase

Pyruvate decarboxylase (PDC) is a well-known pathway for ethanol production, but has not been demonstrated for high titer ethanol production at temperatures above 50 °C.



Here we examined the thermostability of eight PDCs. The purified bacterial enzymes retained 20% of activity after incubation for 30 min at 55 °C. Expression of these PDC genes, except the one from Zymomonas mobilis, improved ethanol production by Clostridium thermocellum. Ethanol production was further improved by expression of the heterologous alcohol dehydrogenase gene adhA from Thermoanaerobacterium saccharolyticum.



The best PDC enzyme was from Acetobactor pasteurianus. A strain of C. thermocellum expressing the pdc gene from A. pasteurianus and the adhA gene from T. saccharolyticum was able to produce 21.3 g/L ethanol from 60 g/L cellulose, which is 70% of the theoretical maximum yield.
Anomaly Detection in User Daily Patterns in Smart-Home Environment

— The paper presents a technique for anomaly detection in user behavior in a smart-home environment. Presented technique can be used for a service that learns daily patterns of the user and proactively detects unusual situations. We have identified several drawbacks of previously presented models such as: just one type of anomaly-inactivity, intricate activity classification into hierarchy, detection only on a daily basis. Our novelty approach desists these weaknesses, provides additional information if the activity is unusually short/long, at unusual location. It is based on a semi-supervised clustering model that utilizes the neural network Self-Organizing Maps. The input to the system represents data primarily from presence sensors, however also other sensors with binary output may be used. The experimental study is realized on both synthetic data and areal database collected in our own smart-home installation for the period of two months.
Robust inverse kinematics by configuration control for redundant manipulators with seven DoF

This paper presents an optimized, robust inverse kinematics solution in a closed form for redundant manipulators with seven degrees of freedom and zero link offsets like the KUKA LBR iiwa lightweight robot. The computation allows for full range elbow self-motion manifold control, defined by an intuitive angle parameter. Furthermore, by using common configuration parameters, all possible solutions for a single effector pose are taken into account and full arm configuration control is realized. The algorithm was evaluated looking at configuration reliability and continuity for an exemplary, challenging path. The results show that commanded arm configurations are not changing spontaneously inside a trajectory. Even for varying elbow self-motion manifold angles, the joint trajectories stay continuous and the presented algorithm provides consistent solutions. In a second evaluation, the algorithm was implemented in three programming languages and analyzed with respect to computing times. The tests demonstrate short runtimes and overall real-time capability.
Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing

In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling.
Visual Relationship Detection Based on Guided Proposals and Semantic Knowledge Distillation

A thorough comprehension of image content demands a complex grasp of the interactions that may occur in the natural world. One of the key issues is to describe the visual relationships between objects. When dealing with real world data, capturing these very diverse interactions is a difficult problem. It can be alleviated by incorporating common sense in a network. For this, we propose a framework that makes use of semantic knowledge and estimates the relevance of object pairs during both training and test phases. Extracted from precomputed models and training annotations, this information is distilled into the neural network dedicated to this task. Using this approach, we observe a significant improvement on all classes of Visual Genome, a challenging visual relationship dataset. A 68.5 % relative gain on the recall at 100 is directly related to the relevance estimate and a 32.7% gain to the knowledge distillation.
A Scalable Method for Time Series Clustering

Time series clustering has become an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics. Unfortunately, existing methods for time series clustering that rely on the actual time series point values can become impractical since the methods do not scale well for longer time series, and many clustering algorithms do not easily handle high dimensional data. In this paper we propose a scalable method for time series clustering that replaces the time series point values with some global measures of the characteristics of the time series. These global measures are then clustered using a selforganising map, which performs additional dimension reduction. The proposed approach has been tested using some benchmark time series previously reported for time series clustering, and is shown to yield useful and robust clustering. The resulting clusters are similar to those produced by other methods, with some interesting variations that can be intuitively explained with knowledge of the global characteristics of the time series.
PREDICTION OF JUMPING DISTANCE USING A SHORT APPROACH MODEL

Recent evidence has suggested that relationship between approach speed and distance jumped may not be linear. The aims of this study were (1) to test the hypothesis that using a short approach (6-8-10-12 strides) of increasing length, performance variables will be non-linearly related to distance jumped, (2) to investigate the nature of these relationships for a group of long jumpers and individuals within the group, and (3) to use the regression analysis to determine the optimum number of run-up strides and predict the jumping distance that would be achieved with an optimum length run-up for an individual jumper. Eight male long jumpers with different skill levels (body mass: 75.2±2.2 kg and body height: 188.0±4.2 cm) performed a series of short-approach maximal jumps and the full-length approach in a competition. Kinematic data were collected from video analysis. The relationship between the number of approach strides and velocity, and distance jumped were shown to be best represented by second order polynomial equations. When applied on an individual basis, the predicted jump distances (6.95±.61 m) agreed very well with those found in actual competition (6.96±.58m). As a result, these individual relationships were used to comment on individual optimal approach lengths and to evaluate an individual’s potential for performance in the long jump event. It was concluded that the short approach model of performance is a valuable paradigm for investigating long jump behaviour and the performance potential of individuals. The findings also supported the simplified mathematical model proposed in the literature for the study of long jump performance.
Distinguishing specific sexual and general emotional effects in fMRI—Subcortical and cortical arousal during erotic picture viewing

Sexual activity involves excitement with high arousal and pleasure as typical features of emotions. Brain activations specifically related to erotic feelings and those related to general emotional processing are therefore hard to disentangle. Using fMRI in 21 healthy subjects (11 males and 10 females), we investigated regions that show activations specifically related to the viewing of sexually intense pictures while controlling for general emotional arousal (GEA) or pleasure. Activations in the ventral striatum and hypothalamus were found to be modulated by the stimulus' specific sexual intensity (SSI) while activations in the anterior cingulate cortex were associated with an interaction between sexual intensity and emotional valence. In contrast, activation in other regions like the dorsomedial prefrontal cortex, the mediodorsal thalamus and the amygdala was associated only with a general emotional component during sexual arousal. No differences were found in these effects when comparing females and males. Our findings demonstrate for the first time neural differentiation between emotional and sexual components in the neural network underlying sexual arousal.