nils-herrmann/clean_scirepeval huggingface dataset:
Information Theoretic Learning

Learning systems depend on three interrelated components: topologies, cost/performance functions, and learning algorithms. Topologies provide the constraints for the mapping, and the learning algorithms offer the means to find an optimal solution; but the solution is optimal with respect to what? Optimality is characterized by the criterion and in neural network literature, this is the least addressed component, yet it has a decisive influence in generalization performance. Certainly, the assumptions behind the selection of a criterion should be better understood and investigated. Traditionally, least squares has been the benchmark criterion for regression problems; considering classification as a regression problem towards estimating class posterior probabilities, least squares has been employed to train neural network and other classifier topologies to approximate correct labels. The main motivation to utilize least squares in regression simply comes from the intellectual comfort this criterion provides due to its success in traditional linear least squares regression applications – which can be reduced to solving a system of linear equations. For nonlinear regression, the assumption of Gaussianity for the measurement error combined with the maximum likelihood principle could be emphasized to promote this criterion. In nonparametric regression, least squares principle leads to the conditional expectation solution, which is intuitively appealing. Although these are good reasons to use the mean squared error as the cost, it is inherently linked to the assumptions and habits stated above. Consequently, there is information in the error signal that is not captured during the training of nonlinear adaptive systems under non-Gaussian distribution conditions when one insists on second-order statistical criteria. This argument extends to other linear-second-order techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and canonical correlation analysis (CCA). Recent work tries to generalize these techniques to nonlinear scenarios by utilizing kernel techniques or other heuristics. This begs the question: what other alternative cost functions could be used to train adaptive systems and how could we establish rigorous techniques for extending useful concepts from linear and second-order statistical techniques to nonlinear and higher-order statistical learning methodologies?
Effective advertising and its influence on consumer buying behavior

Advertising is a form of communication intended to convince an audience (viewers, readers or listeners) to purchase or take some action upon products, information, or services etc. This paper investigates the relationship between independent variables which are environmental response and emotional response with attitudinal and behavioral aspect of consumer buying behavior, by tapping the responses of 200 respondents using telecommunication services from Rawalpindi, Islamabad, and Lahore (cities of Pakistan). The major findings of the study demonstrate an overall normal association between the variables but in-depth analysis found that emotional response of consumer purchase behavior is the variable that results into strong association with the consumer buying behavior. It is true that people purchase those brands with which they are emotionally attached. This study reports new evidences in the field of consumer buying behavior of consumers pertaining to the local markets of these cities of Pakistan.
Investigating Robustness and Interpretability of Link Prediction via Adversarial Attacks

Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on improving ranking metrics and ignore other aspects of knowledge base representations, such as robustness, interpretability, and ability to detect errors. In this paper, we propose adversarial attacks on link prediction models (AALP): identifying the fact to add into or remove from the knowledge graph that changes the prediction of a target fact. Using these attacks, we are able to identify the most influential related fact for a predicted link and investigate the sensitivity of the model to additional made-up facts. We introduce an efficient approach to estimate the effect of making a change by approximating the change in the embeddings upon altering the knowledge graph. In order to avoid the combinatorial search over all possible facts, we introduce an inverter function and gradient-based search to identify the adversary in a continuous space. We demonstrate that our models effectively attack the link prediction models by reducing their accuracy between 6− 45% for different metrics. Further, we study patterns in the most influential neighboring facts, as identified by the adversarial attacks. Finally, we use the proposed approach to detect incorrect facts in the knowledge base, achieving up to 55% accuracy in identifying errors.
Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.
The algorithm design manual

Due to the fact that no NP-complete problem can be solved in polynomial time (unless P=NP), many approximability results (both positive and negative) of NP-hard optimization problems have appeared in the technical literature. In this compendium, we collect a large number of these results. ● Introduction ❍ NPO Problems: Definitions and Preliminaries ❍ Approximate Algorithms and Approximation Classes ❍ Completeness in Approximation Classes ❍ A list of NPO problems ❍ Improving the compendium ● Graph Theory ❍ Covering and Partitioning ❍ Subgraphs and Supergraphs ❍ Vertex Ordering file:///E|/COMPEND/COMPED19/COMPENDI.HTM (1 of 2) [19/1/2003 1:36:58] A compendium of NP optimization problems ❍ Isoand Other Morphisms ❍ Miscellaneous ● Network Design ❍ Spanning Trees ❍ Cuts and Connectivity ❍ Routing Problems ❍ Flow Problems ❍ Miscellaneous ● Sets and Partitions ❍ Covering, Hitting, and Splitting ❍ Weighted Set Problems ● Storage and Retrieval ❍ Data Storage ❍ Compression and Representation ❍ Miscellaneous ● Sequencing and Scheduling ❍ Sequencing on One Processor ❍ Multiprocessor Scheduling ❍ Shop Scheduling ❍ Miscellaneous ● Mathematical Programming ● Algebra and Number Theory ● Games and Puzzles ● Logic ● Program Optimization ● Miscellaneous ● References ● Index ● About this document ... Viggo Kann Mon Apr 21 13:07:14 MET DST 1997 file:///E|/COMPEND/COMPED19/COMPENDI.HTM (2 of 2) [19/1/2003 1:36:58]
Fingerprint-Quality Index Using Gradient Components

Fingerprint image-quality checking is one of the most important issues in fingerprint recognition because recognition is largely affected by the quality of fingerprint images. In the past, many related fingerprint-quality checking methods have typically considered the condition of input images. However, when using the preprocessing algorithm, ridge orientation may sometimes be extracted incorrectly. Unwanted false minutiae can be generated or some true minutiae may be ignored, which can also affect recognition performance directly. Therefore, in this paper, we propose a novel quality-checking algorithm which considers the condition of the input fingerprints and orientation estimation errors. In the experiments, the 2-D gradients of the fingerprint images were first separated into two sets of 1-D gradients. Then, the shapes of the probability density functions of these gradients were measured in order to determine fingerprint quality. We used the FVC2002 database and synthetic fingerprint images to evaluate the proposed method in three ways: 1) estimation ability of quality; 2) separability between good and bad regions; and 3) verification performance. Experimental results showed that the proposed method yielded a reasonable quality index in terms of the degree of quality degradation. Also, the proposed method proved superior to existing methods in terms of separability and verification performance.
From archaeon to eukaryote: the evolutionary dark ages of the eukaryotic cell.

The evolutionary origin of the eukaryotic cell represents an enigmatic, yet largely incomplete, puzzle. Several mutually incompatible scenarios have been proposed to explain how the eukaryotic domain of life could have emerged. To date, convincing evidence for these scenarios in the form of intermediate stages of the proposed eukaryogenesis trajectories is lacking, presenting the emergence of the complex features of the eukaryotic cell as an evolutionary deus ex machina. However, recent advances in the field of phylogenomics have started to lend support for a model that places a cellular fusion event at the basis of the origin of eukaryotes (symbiogenesis), involving the merger of an as yet unknown archaeal lineage that most probably belongs to the recently proposed 'TACK superphylum' (comprising Thaumarchaeota, Aigarchaeota, Crenarchaeota and Korarchaeota) with an alphaproteobacterium (the protomitochondrion). Interestingly, an increasing number of so-called ESPs (eukaryotic signature proteins) is being discovered in recently sequenced archaeal genomes, indicating that the archaeal ancestor of the eukaryotic cell might have been more eukaryotic in nature than presumed previously, and might, for example, have comprised primitive phagocytotic capabilities. In the present paper, we review the evolutionary transition from archaeon to eukaryote, and propose a new model for the emergence of the eukaryotic cell, the 'PhAT (phagocytosing archaeon theory)', which explains the emergence of the cellular and genomic features of eukaryotes in the light of a transiently complex phagocytosing archaeon.
A Numerical Investigation of the Incompressible Flow through a Butterfly Valve Using CFD

The results of a numerical investigation of a butterfly valve under incompressible flow conditions are reported. The commercial CFD code Fluent was used for the study, and the standard K-ε turbulence model was selected. The simulations provided insight into the flow field characteristics of the valve. The valve performance factors such as the flow coefficient and hydrodynamic flow coefficient were computed for different valve opening angles. The results were compared with experimental data and good agreement was found. The results demonstrated the importance of CFD as a valuable tool in visualizing the complex structures in the flow field of the valve. The pressure drop across the valve and the valve flow coefficient were found to be directly dependent on the opening angle of the valve disk. The hydrodynamic torque coefficient curve was computed at different valve opening angles, and the angle at which maximum torque coefficient occurs was identified.  Butterfly Valve, Control Valve, Computational Fluid Dynamics, CFD, Numerical Analysis,
Adversarial Examples Target Topological Holes in Deep Networks

It is currently unclear why adversarial examples are easy to construct for deep networks that are otherwise successful with respect to their training domain. However, it is suspected that these adversarial examples lie within some small perturbation from the network’s decision boundaries or exist in low-density regions with respect to the training distribution. Using persistent homology, we find that deep networks effectively have “holes” in their activation graphs, making them blind to regions of the input space that can be exploited by adversarial examples. These holes are effectively dense in the input space, making it easy to find a perturbed image that can be misclassified. By studying the topology of network activation, we find global patterns in the form of activation subgraphs which can both reliably determine whether an example is adversarial and can recover the true category of the example well above chance, implying that semantic information about the input is embedded globally via the activation pattern in deep networks.
SegNetRes-CRF: A Deep Convolutional Encoder-Decoder Architecture for Semantic Image Segmentation

Semantic segmentation is an essential task in computer vision that aims to label each image pixel. Several of the actual best approaches in this context are based on deep neural networks. For example, SegNet is a deep encoder-decoder architecture approach whose results were disruptive because it is fast and performs well. However, this architecture fails to finedelineating the edges between the objects of interest in the image. We propose some modifications in the SegNet-Basic architecture by using a post-processing segmentation layer (using Conditional Random Fields) and by transferring high resolution features combined to the decoder network. The proposed method was evaluated in the dataset CamVid. Moreover, it was compared with important variants of SegNet and showed to be able to improve the overall accuracy of SegNet-Basic by up to 17.5%.
Author disambiguation by hierarchical agglomerative clustering with adaptive stopping criterion

Entity disambiguation is an important step in many information retrieval applications. This paper proposes new research for entity disambiguation with the focus of name disambiguation in digital libraries. In particular, pairwise similarity is first learned for publications that share the same author name string (ANS) and then a novel Hierarchical Agglomerative Clustering approach with Adaptive Stopping Criterion (HACASC) is proposed to adaptively cluster a set of publications that share a same ANS to individual clusters of publications with different author identities. The HACASC approach utilizes a mixture of kernel ridge regressions to intelligently determine the threshold in clustering. This obtains more appropriate clustering granularity than non-adaptive stopping criterion. We conduct a large scale empirical study with a dataset of more than 2 million publication record pairs to demonstrate the advantage of the proposed HACASC approach.
Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables.

Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.



We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA1c, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations.



We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes.



We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.


 Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhälsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.
Corporate Governance , Board Diversity , and Firm Value

This study examines the relationship between board diversity and firm value for Fortune 1000 firms. Board diversity is defined as the percentage of women, African Americans, Asians, and Hispanics on the board of directors. This research is important because it presents the first empirical evidence examining whether board diversity is associated with improved financial value. After controlling for size, industry, and other corporate governance measures, we find significant positive relationships between the fraction of women or minorities on the board and firm value. We also find that the proportion of women and minorities on boards increases with firm size and board size, but decreases as the number of insiders increases.
Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices.

Neuroanatomy places critical constraints on the functional connectivity of the cerebral cortex. To analyze these constraints we have examined the relationship between structural features of networks (expressed as graphs) and the patterns of functional connectivity to which they give rise when implemented as dynamical systems. We selected among structurally varying graphs using as selective criteria a number of global information-theoretical measures that characterize functional connectivity. We selected graphs separately for increases in measures of entropy (capturing statistical independence of graph elements), integration (capturing their statistical dependence) and complexity (capturing the interplay between their functional segregation and integration). We found that dynamics with high complexity were supported by graphs whose units were organized into densely linked groups that were sparsely and reciprocally interconnected. Connection matrices based on actual neuroanatomical data describing areas and pathways of the macaque visual cortex and the cat cortex showed structural characteristics that coincided best with those of such complex graphs, revealing the presence of distinct but interconnected anatomical groupings of areas. Moreover, when implemented as dynamical systems, these cortical connection matrices generated functional connectivity with high complexity, characterized by the presence of highly coherent functional clusters. We also found that selection of graphs as they responded to input or produced output led to increases in the complexity of their dynamics. We hypothesize that adaptation to rich sensory environments and motor demands requires complex dynamics and that these dynamics are supported by neuroanatomical motifs that are characteristic of the cerebral cortex.
Birth Preparedness and Complication Readiness among Pregnant Women in Southern Ethiopia

Birth preparedness and complication preparedness (BPACR) is a key component of globally accepted safe motherhood programs, which helps ensure women to reach professional delivery care when labor begins and to reduce delays that occur when mothers in labor experience obstetric complications.



This study was conducted to assess practice and factors associated with BPACR among pregnant women in Aleta Wondo district in Sidama Zone, South Ethiopia.



A community based cross sectional study was conducted in 2007, on a sample of 812 pregnant women. Data were collected using pre-tested and structured questionnaire. The collected data were analyzed by SPSS for windows version 12.0.1. The women were asked whether they followed the desired five steps while pregnant: identified a trained birth attendant, identified a health facility, arranged for transport, identified blood donor and saved money for emergency. Taking at least two steps was considered being well-prepared.



Among 743 pregnant women only a quarter (20.5%) of pregnant women identified skilled provider. Only 8.1% identified health facility for delivery and/or for obstetric emergencies. Preparedness for transportation was found to be very low (7.7%). Considerable (34.5%) number of families saved money for incurred costs of delivery and emergency if needed. Only few (2.3%) identified potential blood donor in case of emergency. Majority (87.9%) of the respondents reported that they intended to deliver at home, and only 60(8%) planned to deliver at health facilities. Overall only 17% of pregnant women were well prepared. The adjusted multivariate model showed that significant predictors for being well-prepared were maternal availing of antenatal services (OR = 1.91 95% CI; 1.21-3.01) and being pregnant for the first time (OR = 6.82, 95% CI; 1.27-36.55).



BPACR practice in the study area was found to be low. Effort to increase BPACR should focus on availing antenatal care services.
Deep Learning towards Mobile Applications

Recent years have witnessed an explosive growth of mobile devices. Mobile devices are permeating every aspect of our daily lives. With the increasing usage of mobile devices and intelligent applications, there is a soaring demand for mobile applications with machine learning services. Inspired by the tremendous success achieved by deep learning in many machine learning tasks, it becomes a natural trend to push deep learning towards mobile applications. However, there exist many challenges to realize deep learning in mobile applications, including the contradiction between the miniature nature of mobile devices and the resource requirement of deep neural networks, the privacy and security concerns about individuals' data, and so on. To resolve these challenges, during the past few years, great leaps have been made in this area. In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning. The former two aspects cover the primary tasks of deep learning. Then, we go through our two recent applications that apply the data collected by mobile devices to inferring mood disturbance and user identification. Finally, we conclude this paper with the discussion of the future of this area.