nils-herrmann/clean_scirepeval huggingface dataset:
An Exponential Lower Bound for Cut Sparsifiers in Planar Graphs

Given an edge-weighted graph G with a set $$Q$$ Q of k terminals, a mimicking network is a graph with the same set of terminals that exactly preserves the size of minimum cut between any partition of the terminals. A natural question in the area of graph compression is to provide as small mimicking networks as possible for input graph G being either an arbitrary graph or coming from a specific graph class. We show an exponential lower bound for cut mimicking networks in planar graphs: there are edge-weighted planar graphs with k terminals that require $$2^{k-2}$$ 2k-2 edges in any mimicking network. This nearly matches an upper bound of $$\mathcal {O}(k 2^{2k})$$ O(k22k) of Krauthgamer and Rika (in: Khanna (ed) Proceedings of the twenty-fourth annual ACM-SIAM symposium on discrete algorithms, SODA 2013, New Orleans, 2013) and is in sharp contrast with the upper bounds of $$\mathcal {O}(k^2)$$ O(k2) and $$\mathcal {O}(k^4)$$ O(k4) under the assumption that all terminals lie on a single face (Goranci et al., in: Pruhs and Sohler (eds) 25th Annual European symposium on algorithms (ESA 2017), 2017, ; Krauthgamer and Rika in Refined vertex sparsifiers of planar graphs, 2017, ). As a side result we show a tight example for double-exponential upper bounds given by Hagerup et al. ( 1998), Khan and Raghavendra ( 2014), and Chambers and Eppstein ( 2013).
Joint auto-encoders: a flexible multi-task learning framework

The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest. Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a datadriven fashion. We develop a framework for learning multiple tasks simultaneously, based on sharing features that are common to all tasks, achieved through the use of a modular deep feedforward neural network consisting of shared branches, dealing with the common features of all tasks, and private branches, learning the specific unique aspects of each task. Once an appropriate weight sharing architecture has been established, learning takes place through standard algorithms for feedforward networks, e.g., stochastic gradient descent and its variations. The method deals with domain adaptation and multi-task learning in a unified fashion, and can easily deal with data arising from different types of sources. Numerical experiments demonstrate the effectiveness of learning in domain adaptation and transfer learning setups, and provide evidence for the flexible and task-oriented representations arising in the network.
Converter Topologies and Power Semiconductors for Industrial Medium Voltage Converters

Today there are numerous different converter topologies and power semiconductor devices used in medium- voltage drive systems. This paper provides a general overview of the common converter topologies available on the market and their corresponding major characteristics. The different topologies are compared and evaluated with respect to their semiconductor effort. Due to the available power semiconductor devices with maximum blocking voltages of 6.5 kV, the drive market with power ratings up to 25 MW is dominated by voltage source inverters in IGBT as well as in IGCT technology. For higher power demands and special applications, thyristor converters are still frequently used.
Chemical and biological aspects of Amaryllidaceae alkaloids

The Amaryllidaceae alkaloids represent a large (over 300 alkaloids have been isolated) and still expanding group of biogenetically related isoquinoline alkaloids that are found exclusively in plants belonging to this family. In spite of their great variety of pharmacological and/or biological properties, only galanthamine is used therapeutically. First isolated from Galanthus species, this alkaloid is a long-acting, selective, reversible and competitive inhibitor of acetylcholinesterase, and is used for the treatment of Alzheimer’s disease. Other Amaryllidaceae alkaloids of pharmacological interest will also be described in this chapter.
The Robo-havioral Methodology : Developing Neuroscience Theories with FOES

In systems neuroscience there is a big gap between what theorists postulate (i.e., grand unifying theories about the general principles underlying cortical processes such as the predictive processing account) and what empiricists measure (i.e., reaction times, pupil dilations, blood-oxygenated level dependent signal in brain areas, magnetic pulses). It is becoming increasingly difficult for the theorists to come up with empirically testable hypotheses and for the empiricists to use their findings to confirm or refute a theory. We propose a research methodology based on robot simulations that may help bridge that gap. The methodology is summarized by four keywords: Formalize verbal theories into computational models; Operationalize this computational model into a working robot implementation; Explore the consequences of various design choices and parameter settings to generate empirically testable hypotheses; and finally Study these hypotheses in behavioral or imaging experiments. We lay out a research program that aims at investigating various open issues in predictive processing and exemplify our approach in a simple case study.
Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach

Recent studies show that the popularity of the pairs trading strategy has been growing and it may pose a problem as the opportunities to trade become much smaller. Therefore, the optimization of pairs trading strategy has gained widespread attention among high-frequency traders. In this paper, using reinforcement learning, we examine the optimum level of pairs trading specifications over time.More specifically, the reinforcement learning agent chooses the optimum level of parameters of pairs trading to maximize the objective function. Results are obtained by applying a combination of the reinforcement learning method and cointegration approach. We find that boosting pairs trading specifications by using the proposed approach significantly overperform the previous methods. Empirical results based on the comprehensive intraday data which are obtained from S&P500 constituent stocks confirm the efficiently of our proposed method. Communicated by V. Loia. B Hasan Hakimian hasan.hakimian@ut.ac.ir Saeid Fallahpour falahpor@ut.ac.ir Khalil Taheri k.taheri@ut.ac.ir Ehsan Ramezanifar e.ramezanifar@maastrichtuniversity.nl 1 Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran 2 Advanced Robotics and Intelligent Systems Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran 3 Department of Finance, School of Business and Economics, Maastricht, The Netherlands
Evolution of Payments Bank and Impact From M-PESA: A Case of Mobile Banking Services in India

A large proportion of the Indian population is excluded from basic banking services. Just one in two Indians has access to a savings bank account and just one in seven Indians has access to bank credit (Business Standard, June 28 2013). There are merely 684 million savings bank accounts in the country with a population of 1.2 billion. Branch per 100,000 adult ratio in India stands at 747 compared to 1,065 for Brazil and 2,063 for Malaysia (. As more people, especially the poor, gain access to financial services, they will be able to save better and get access to funding in a more structured manner. This will reduce income inequality, help the poor up the ladder, and contribute to economic development. There is a need for transactions and savings accounts for the under-served in the population. Mobile banking has been evolved in last couple of years with the help of Mobile penetration, which has shown phenomenal growth in rural areas of India. The rural subscription increased from 398.68 million at the end of December 2014 to 404.16 million at the end of January 2015, said in a statement by the Telecom Regulatory Authority of India. Banks in India are already investing in mobile technology and security from last couple of years. They are adding value in services such as developing smartphone apps, mobile wallets and educating consumers about the benefits of using the mobile banking resulting in adoption of mobile banking faster among consumers as compared to internet banking.
 The objective of this study is:
 1. To understand the scope of mobile banking to reach unbanked population in India.
 2. To analyze the learnings of M-PESA and Payments Bank Opportunity.
 3. To evaluate the upcoming challenges for the payments bank success in India.
Design space exploration of FPGA-based Deep Convolutional Neural Networks

Deep Convolutional Neural Networks (DCNN) have proven to be very effective in many pattern recognition applications, such as image classification and speech recognition. Due to their computational complexity, DCNNs demand implementations that utilize custom hardware accelerators to meet performance and energy-efficiency constraints. In this paper we propose an FPGA-based accelerator architecture which leverages all sources of parallelism in DCNNs. We develop analytical feasibility and performance estimation models that take into account various design and platform parameters. We also present a design space exploration algorithm for obtaining the implementation with the highest performance on a given platform. Simulation results with a real-life DCNN demonstrate that our accelerator outperforms other competing approaches, which disregard some sources of parallelism in the application. Most notably, our accelerator runs 1.9× faster than the state-of-the-art DCNN accelerator on the same FPGA device.
A Comparative Study of Multilayer Neural Network and C 4 . 5 Decision Tree Models for Predicting the Risk of Breast Cancer

Diagnosing breast cancer at an early stage can have a great impact on cancer mortality. One of the fundamental problems in cancer treatment is the lack of a proper method for early detection, which may lead to diagnostic errors. Using data analysis techniques can significantly help in early diagnosis of the disease. The purpose of this study was to evaluate and compare the efficacy of two data mining techniques, i.e., multilayer neural network and C4.5, in early diagnosis of breast cancer.  A data set from Motamed Cancer Institute's breast cancer research clinic, Tehran, containing 2860 records related to breast cancer risk factors were used. Of the records, 1141 (40%) were related to malignant changes and breast cancer and 1719 (60%) to benign tumors. The data set was analyzed using perceptron neural network and decision tree algorithms, and was split into two a training data set (70%) and a testing data set (30%) using Rapid Miner 5.2.  For neural networks, accuracy was 80.52%, precision 88.91%, and sensitivity 90.88%; and for decision tree, accuracy was 80.98%, precision 80.97%, and sensitivity 89.32%. Results indicated that both algorithms have acceptable capabilities for analyzing breast cancer data.  Although both models provided good results, neural network showed more reliable diagnosis for positive cases. Data set type and analysis method affect results. On the other hand, information about more powerful risk factors of breast cancer, such as genetic mutations, can provide models with high coverage.  19 January 2018  26 January 2018
Adversarial Dropout Regularization

We present a method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to align features across domains by fooling a special domain critic network. However, a drawback of this approach is that the critic simply labels the generated features as in-domain or not, without considering the boundaries between classes. This can lead to ambiguous features being generated near class boundaries, reducing target classification accuracy. We propose a novel approach, Adversarial Dropout Regularization (ADR), to encourage the generator to output more discriminative features for the target domain. Our key idea is to replace the critic with one that detects non-discriminative features, using dropout on the classifier network. The generator then learns to avoid these areas of the feature space and thus creates better features. We apply our ADR approach to the problem of unsupervised domain adaptation for image classification and semantic segmentation tasks, and demonstrate significant improvement over the state of the art. We also show that our approach can be used to train Generative Adversarial Networks for semi-supervised learning.
Unusual Biosynthesis and Structure of Locillomycins from Bacillus subtilis 916.

Three families of Bacillus cyclic lipopeptides--surfactins, iturins, and fengycins--have well-recognized potential uses in biotechnology and biopharmaceutical applications. This study outlines the isolation and characterization of locillomycins, a novel family of cyclic lipopeptides produced by Bacillus subtilis 916. Elucidation of the locillomycin structure revealed several molecular features not observed in other Bacillus lipopeptides, including a unique nonapeptide sequence and macrocyclization. Locillomycins are active against bacteria and viruses. Biochemical analysis and gene deletion studies have supported the assignment of a 38-kb gene cluster as the locillomycin biosynthetic gene cluster. Interestingly, this gene cluster encodes 4 proteins (LocA, LocB, LocC, and LocD) that form a hexamodular nonribosomal peptide synthetase to biosynthesize cyclic nonapeptides. Genome analysis and the chemical structures of the end products indicated that the biosynthetic pathway exhibits two distinct features: (i) a nonlinear hexamodular assembly line, with three modules in the middle utilized twice and the first and last two modules used only once and (ii) several domains that are skipped or optionally selected.
Position Tracking and Control of Lightweight Flexible Joint Manipulator Robolink® Using Kinect Sensor

In this article, a method of feedback data acquisition and target recognition using Kinect sensor is proposed, in order to perform position tracking and control of Robolink® articulated arm. Robolink is a lightweight flexible joint robotic manipulator, the rotational joints of which are driven by step motors through Dyneema wires. The goal of the presented experimental work was to investigate the possibility to use Robolink in fruit picking systems and other similar works. At an early stage, Lidar sensors were also used for Robolink indoor behavior experimentation. Feedback data management and control law, based on the kinematics of this lightweight flexible robotic arm are also analyzed and presented from the efficiency point of view.
Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach

This paper develops an integrated approach, combining quality function deployment (QFD), fuzzy set theory, and analytic hierarchy process (AHP) approach, to evaluate and select the optimal third-party logistics service providers (3PLs). In the approach, multiple evaluating criteria are derived from the requirements of company stakeholders using a series of house of quality (HOQ). The importance of evaluating criteria is prioritized with respect to the degree of achieving the stakeholder requirements using fuzzy AHP. Based on the ranked criteria, alternative 3PLs are evaluated and compared with each other using fuzzy AHP again to make an optimal selection. The effectiveness of proposed approach is demonstrated by applying it to a Hong Kong based enterprise that supplies hard disk components. The proposed integrated approach outperforms the existing approaches because the outsourcing strategy and 3PLs selection are derived from the corporate/business strategy.
Binary Similarity Detection Using Machine Learning

Finding similar procedures in stripped binaries has various use cases in the domains of cyber security and intellectual property. Previous works have attended this problem and came up with approaches that either trade throughput for accuracy or address a more relaxed problem.
 In this paper, we present a cross-compiler-and-architecture approach for detecting similarity between binary procedures, which achieves both high accuracy and peerless throughput. For this purpose, we employ machine learning alongside similarity by composition: we decompose the code into smaller comparable fragments, transform these fragments to vectors, and build machine learning-based predictors for detecting similarity between vectors that originate from similar procedures.
 We implement our approach in a tool called Zeek and evaluate it by searching similarities in open source projects that we crawl from the world-wide-web. Our results show that we perform 250X faster than state-of-the-art tools without harming accuracy.
Intelligent parking Cloud services based on IoT using MQTT protocol

This paper presents the concept of vehicular cloud service network using IoT and Cloud together. Both these technologies (IoT and Cloud Computing) are able to solve real time problems faced by population. The tremendous growth of Internet of Thing(IoT) and Cloud Computing together have provided great solution to the increasing transportation issues. In this paper we propose, creating vehicular cloud service network using MQTT protocol. The main objective of this paper is to design a cloud vehicular service for parking purpose based on the basic communication principle of MQTT protocol. We propose an intelligent parking space services to make IoT more suitable for both small-sized and large-scale information retrieval by cloud. This paper briefs the most emerging paradigm of IoT in parking cloud services.
Using regional saliency for speech emotion recognition

In this paper, we show that convolutional neural networks can be directly applied to temporal low-level acoustic features to identify emotionally salient regions without the need for defining or applying utterance-level statistics. We show how a convolutional neural network can be applied to minimally hand-engineered features to obtain competitive results on the IEMOCAP and MSP-IMPROV datasets. In addition, we demonstrate that, despite their common use across most categories of acoustic features, utterance-level statistics may obfuscate emotional information. Our results suggest that convolutional neural networks with Mel Filterbanks (MFBs) can be used as a replacement for classifiers that rely on features obtained from applying utterance-level statistics.
A gentle introduction to quantile regression for ecologists

www.frontiersinecology.org  Quantile regression (Koenker and Bassett 1978) is a method for estimating functional relations between variables for all portions of a probability distribution. Although it has begun to be used in ecology and biology (Table 1), many ecologists remain unaware of it, as it was developed relatively recently and is rarely taught in statistics courses at many universities. We present this introduction both to encourage additional applications in ecology and to educate those who are already contemplating using the method. Typically, a response variable y is some function of predictor variables X, so that y = f(X). Most regression applications in the ecological sciences focus on estimating rates of change in the mean of the response variable distribution as some function of a set of predictor variables; in other words, the function is defined for the expected value of y conditional on X, E(y|X). Mosteller and Tukey (1977) noted that it was possible to fit regression curves to other parts of the distribution of the response variable, but that this was rarely done, and therefore most regression analyses gave an incomplete picture of the relationships between variables. This is especially problematic for regression models with heterogeneous variances, which are common in ecology. A regression model with heterogeneous variances implies that there is not a single rate of change that characterizes changes in the probability distributions. Focusing exclusively on changes in the means may underestimate, overestimate, or fail to distinguish real nonzero changes in heterogeneous distributions (Terrell et al. 1996; Cade et al. 1999). The Dunham et al. (2002) analyses relating the abundance of Lahontan cutthroat trout (Oncorhynchus clarki henshawi) to the ratio of stream width to depth illustrates the value of the additional information provided by quantile regression . The ratio was used as a predictor variable because it was an easily obtained measure of channel morphology that was thought to be related to the integrity of habitat in small streams like those typically inhabited by cutthroat trout. Quantile regression estimates indicated a nonlinear, negative relationship with the upper 30% (≥ 70th percentiles, P ≤ 0.10 for H0: 1 = 0) of cutthroat densities across 13 streams and 7 years. A weighted least squares regression estimated zero change (90% confidence intervals of –0.014 to 0.012, P = 0.901 for H0: 1 = 0) in mean densities with stream width to depth. If the authors REVIEWS REVIEWS REVIEWS
Radioiodine therapy dosimetry in benign thyroid disease and differentiated thyroid carcinoma

Traditionally, nuclear medicine has been regarded mainly as a diagnostic specialty in which the administration of radioactive substances yields information whose importance far outweighs any potential risk to normal tissue. Now, however, therapeutic applications of radionuclide-based approaches are at last gaining the prominence they deserve. In this field, individual patient dosimetry is essential both for optimising the administered activity through the establishment of minimum effective and maximum tolerated absorbed doses and for determining a dose-response relationship as a basis for predicting tumour response . Unfortunately, the lack of comprehensive clinical trials designed to bring out the value of radionuclide dosimetry in predicting therapy outcome has fed a general belief that dosimetry methods are daunting, prone to a large degree of uncertainty, and liable to generate increased costs, and thus held back their progress. This is, indeed, why Flux and colleagues remarked “radionuclide therapy remains the Cinderella of cancer treatment modalities, under-utilised with respect to more conventional treatments” . Now, however, the availability of new technologies (e.g. PET/CT) and radioisotope pairs for diagnosis and therapy (e.g. I and I or Y and Y), supported by a new generation of personal computer software for internal dose assessment, is at last leading to an increase in the use of dosimetry in clinical therapeutic applications. To show the appreciable progress now being made in this field we here take a look at a sample of the most recent literature on dosimetry for radioiodine treatment of benign thyroid disease and differentiated thyroid carcinoma.
3 D Virtual Prototyping Traces New Avenues for Fashion Design and Product Development : A Qualitative Study

The fashion industry faces the increasing complexity of its activities such as the globalization of the market, the proliferation of information, the reduced time to market, the increasing distance between industrial partners and pressures related to costs. Digital prototype in the textile and clothing industry enables technologies in the process of product development where various operators are involved in the different stages, with various skills and competencies, and different necessity of formalizing and defining in a deterministic way the result of their activities. Taking into account the recent trends in the industry, the product development cycle and the use of new digital technologies cannot be restricted in the “typical cycle” but additional tools and skills are required to be integrated taking into account these developments .