Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Spectral Learning Approaches for Structured Prediction

Latent variable models(LVM) are prevalent for dealing with problems pertaining to Structured Prediciton as they offer flexibility in modelling observed data by introducing conditional independence between observed variables. Generally, algorithms for such models employ local search heuristics like EM algorithm, which have shortcomings like slow convergence, susceptibility to get stuck in local minima etc. Spectral methods offer a new perspective, which is significantly different from the optimization perspective, to view a LVM and provide fast and local minima free algorithms to estimate the objective. A lot of work has been done on development of spectral algorithms for latent variable models where the objective is to calculate marginal or conditional probabilities of observed variables, and estimation of model parameters is not necessary. However, some spectral algorithms have been developed for parameter estimation too. Embedding probability distributions in Hilbert spaces to model observations drawn from continuous distributions lead to nonparametric kernel based estimation algorithms. This survey focusses on spectral algorithms that have been developed for various models prevalent in structured prediction problems like Hidden Markov Models, Latent PCFG trees, Weighted Automata etc. Bulk of this survey covers the models where the observations are drawn from discrete distributions, however, an algorithm for HMMs having contiuous observed data will also be discussed.
Customer Relationship Management Scale for the Business-to-Consumer Market: Validation in the United States and comparison to Brazilian Models

Given the strategic relevance of Customer Relationship Management (CRM) for organizations nowadays and the lack of instruments customized for the business-toconsumer (B2C) market in general, the main objective of this study is to develop and validate a reliable and valid CRM scale for the B2C market. Besides, we compared Brazilian and American models for the proposed scale. Three studies have been conducted with different samples for the development and validation of the Customer Relationship Management Scale (CRMS). This research is a starting point to provide a comprehensive measure of CRM based on customers’ perspectives to help managers establish profitable relationships.
The relationship between the attachment styles of the adults with and without depression and the parenting attitudes of their parents

Objectives: In this study, it is aimed to determine the relationship between attachment styles of adults with and without depression and child rearing attitudes of their parents. Methods: One hundred eighteen patients who are admitted to outpatient clinic of Department of Psychiatry of the Uludag University School of Medicine, who are diagnosed with depression according to DSMIV-R and DSM V and put on regular medication, and 130 healthy individuals of similar demographic characteristics without any psychopathologic diagnostic, are included in this study. Within the scope of this research "Experiences in Close Relationships Inventory-II", "Child-Rearing Attitudes Scale", "Beck Depression Inventory" and Sociodemographic Information Form were used. Results: It is found that there is a significant difference between the attachment styles of the individuals with and without depression and their parents' attitudes towards child rearing. Maternal and paternal acceptance / interest scores of the patient group were lower than those of the healthy group, and the parents' supervision / control scores were higher. The anxiety and avoidance attachment scores of the married individuals of the patient group are higher than the scores of married individuals of the healthy group. Conclusions: High anxiety and avoidance related to attachment and controlling parental attitudes were found to be risk factors for depression.
∆NN: Power-eicient Neural Network Acceleration using Dierential Weights

The enormous and ever-increasing complexity of state-of-the-art neural networks has impeded the deployment of deep learning on resource-limited embedded and mobile devices. To reduce the complexity of neural networks, this paper presents ∆NN, a power-eicient architecture that leverages a combination of the approximate value locality of neuron weights and algorithmic structure of neural networks. ∆NN keeps each weight as its dierence (∆) to the nearest smaller weight: each weight reuses the calculations of the smaller weight, followed by a calculation on the ∆ value to make up the dierence. We also round up/down the ∆ to the closest power of two number to further reduce complexity. The experimental results show that ∆NN boosts the average performance by 14-37% and reduces the average power consumption by 17-49% over some state-of-the-art neural
Diagnostic strategy for growth hormone deficiency: relevance of IGF-1 determination as a screening test

Background. – Adult growth hormone (GH) deficiency must be diagnosed before prescribing therapeutic recombinant human GH. We studied the clinical relevance of a diagnostic strategy for growth hormone deficiency (GHD) using IGF-1 determination as a first step. Methods. – In 2000 and 2001, we tested 142 adult patients with hypothalamo-pituitary disorders for somatotropic function using Insulin Tolerance Test (ITT), the reference test for the diagnosis of GHD, with concomitant Insulin-like growth factor–1 (IGF-1) determination, a marker of somatotropic function. Patients were classified as GHD (peak GH concentration < 3 ng/ml with the ITT) or normal. Setting. – Monocenter prospective study in a tertiary referral center. Results. – GHD was diagnosed in 61 subjects. Using a ROC curve, a threshold IGF–1 concentration of 175 ng/ml yielded a negative predictive value of 89 ± 5%. A diagnostic strategy with IGF-1 determination as the first step followed by ITT for patients with an IGF-1 concentration below 175 ng/ml missed five of the 61 GHD patients, avoided 46/142 ITT and reduced the cost of diagnosis by 15%. g author. ss: s.hadjadj@chu-poitiers.fr (S. Hadjadj). e front matter © 2007 Elsevier Masson SAS. All rights reserved. o.2007. 08.004 . Tous droits réservés. Document téléchargé le 15/02/2020 Il est interdit et illégal de diffuser ce document. S. Hadjadj et al. / Annales d’Endocrinologie 68 (2007) 449–455 450 © 2020 Conclusion. – We propose the use of a strategy consisting of IGF-1 determination followed, if below 175 ng/ml by confirmatory ITT to diagnose GHD in adults. © 2007 Elsevier Masson SAS. All rights reserved. Mots clés : IGF-1 ; GH ; Insuffisance somatotrope de l’adulte ; Stratégie diagnostique ; Courbe ROC
GRAPH FEATURE NETWORKS

Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of understanding on what they are learning and how sophisticated the learned graph functions are. In this work, we propose Graph Feature Network (GFN), a simple lightweight neural net defined on a set of graph augmented features. GFN can be used to approximate GNNs with improved efficiency, and serve as a tool to access and understand the graph functions learned. To our surprise, we find that, despite its simplicity, GFN could match or exceed the best accuracies produced by recently proposed GNNs on common graph classification benchmarks. Our results seemingly suggest that (1) GFN may serve as an efficient and effective alternative to GNNs, and (2) existing GNNs may not have learned more sophisticated graph functions on these benchmarks. Furthermore, we observe that when treating images as pixels defined in graphs/grids, the same type of GNNs can outperform GFN, indicating GNNs may have learned more complex graph functions in that case.1
THE SOLVENCY II PROCESS: OVERVIEW

As early as the 1970s, European Union (EU) member countries implemented rules to coordinate insurance markets and regulation. However, with the more recent movement toward a general single EU market, financial services regulation has taken on new meaning and priority. Solvency I regulations went into effect for member nations by January 2004. The creation of risk-based capital standards, the main focus of Solvency II, now appears likely sometime after 2007. The purpose of the discussion presented here is to outline the specifics of Solvency II as they currently stand and suggest important areas of future research.
Comparative Analysis on Mitochondrial Control Region DNA Diversity of Three Far Eastern catfish (Siluyus asotus) Populations

To investigate genetic diversity of the far eastern catfish (Silurus asotus) populations in the Yellow River, the Songhua River, and the Dayanghe River, mitochondrial control region (D-loop) DNA was analyzed for the samples collected in Zhengzhou (ZZ in the Yellow River), Haerbin (HEB in the Songhua River), and Donggang (DG in the Dayanghe River). There were totally twenty-one haplotypes identified in the three groups of samples. Haplotype networks by using neighbor-joining and median-joining clustering methods both indicated that the haplotypes 3, 8, and 19 constituted a single branch and the other 18 haplotypes constituted another branch. The highest genetic differentiation coefficients were observed between the ZZ and HEB samples (0.264), and the lowest was between the ZZ and DG (0.101). The average gene flow among the three populations was 3.063. The Fu's Fs and Tajima's D test values of the three populations were both negative, and indicated no significant difference between three populations (p=0.08 and 0.06> 0.05). Our results suggest that the far eastern catfish populations have not undergone significant population expansion and remained stable in the Yellow River, the Songhua River, and the Dayanghe River.
Examining the Impact Records Keeping, Information Sharing and Partner Relations on Procurement Performance in Firms

This study examines the extent to which information sharing, records keeping and partner relations influence firm performance via the creation of an efficient procurement system. A survey of 142 firms in the Ashanti region of Ghana, selected using the convenience sampling technique is used in the study. The study adopted a quantitative approach, with a structured questionnaire as the primary data collection instrument. An examination of the relationship among the variables reveal that procurement efficiency partially mediates the relationship between each of the three procurement related practices and firm performance. The implication of these findings for practice is that a focus on collaboration, not competition must be the aim of procurement. In modern times, firms must move beyond adversarial relations and focus on creative collaborative, long term relationship with selected suppliers. The study proposes that records keeping and audit of the process must be accorded as much importance as tender evaluation and contract management. The findings further imply that Suppliers are an extension of the focal firm and must be viewed as such. Excessive use of power leaves the weaker party aggrieved and willing to resort to unethical practices, which may include supply of low quality materials or creation of artificial shortages.
Using Machine Learning to Analyze Merger Activity

An unprecedented amount of access to data, “big data (or high dimensional data),” cloud computing, and innovative technology have increased applications of artificial intelligence in finance and numerous other industries. Machine learning is used in process automation, security, underwriting and credit scoring, algorithmic trading and robo-advisory. In fact, machine learning AI applications are purported to save banks an estimated $447 billion by 2023. Given the advantages that AI brings to finance, we focused on applying supervised machine learning to an investment problem. 10-K SEC filings are routinely used by investors to determine the worth and status of a company–Warren Buffett is frequently cited to read a 10-K a day. We sought to answer–“Can machine learning analyze more than thousands of companies and spot patterns? Can machine learning automate the process of human analysis in predicting whether a company is fit to merge? Can machine learning spot something that humans cannot? ” In the advent of rising antitrust discussion of growing market concentrations and the concern for decrease in competition, we analyzed merger activity using text as a data set. Merger activity has been traditionally hard to predict in the past. We took advantage of the large amount of publicly available filings through the Securities Exchange Commission that give a comprehensive summary of a company, and used text, and an innovative way to analyze a company. In order to verify existing theory and measure harder to observe variables, we look to use a text document and examined a firm’s 10-K SEC filing. To minimize over-fitting, the L2 LASSO regularization technique is used. We came up with a model that has 85% accuracy compared to a 35% accuracy using the “bag-of-words” method to predict a company’s likelihood of merging from words alone on the same period’s test data set. These steps are the beginnings of tackling more complicated questions, such as “Which section or topic of words is the most predictive? ” and “What is the difference between being acquired and acquiring? ” Using product descriptions to characterize mergers further into horizontal and vertical mergers could eventually assist with the causal estimates that are of interest to economists. More importantly, using language and words to categorize companies could be useful in predicting counterfactual scenarios and answering policy questions, and could have different applications ranging from detecting fraud to better trading.
Research on Optimization Method of VR Task Scenario Resources Driven by User Cognitive Needs

Research was performed in order to improve the efficiency of a user’s access to information and the interactive experience of task selection in a virtual reality (VR) system, reduce the level of a user’s cognitive load, and improve the efficiency of designers in building a VR system. On the basis of user behavior cognition-system resource mapping, a task scenario resource optimization method for VR system based on quality function deployment-convolution neural network (QFD-CNN) was proposed. Firstly, under the guidance of user behavior cognition, the characteristics of multi-channel information resources in a VR system were analyzed, and the correlation matrix of the VR system scenario resource characteristics was constructed based on the design criteria of human–computer interaction, cognition, and low-load demand. Secondly, analytic hierarchy process (AHP)-QFD combined with evaluation matrix is used to output the priority ranking of VR system resource characteristics. Then, the VR system task scenario cognitive load experiment is carried out on users, and the CNN input set and output set data are collected through the experiment, in order to build a CNN system and predict the user cognitive load and satisfaction in the human–computer interaction in the VR system. Finally, combined with the task information interface of a VR system in a smart city, the application research of the system resource feature optimization method under multi-channel cognition is carried out. The results show that the test coefficient CR value of the AHP-QFD model based on cognitive load is less than 0.1, and the MSE of CNN prediction model network is 0.004247, which proves the effectiveness of this model. According to the requirements of the same design task in a VR system, by comparing the scheme formed by the traditional design process with the scheme optimized by the method in this paper, the results show that the user has a lower cognitive load and better task operation experience when interacting with the latter scheme, so the optimization method studied in this paper can provide a reference for the system construction of virtual reality.
Computing the Rao’s distance between negative binomial distributions. Application to Exploratory Data Analysis

The statistical analysis of counts of living organisms brings information about the collective behavior of species (schooling, habitat preference, etc), possibly depending on their biological characteristics (growth rate, reproductive power, survival rate, etc). This task can be implemented in a non-parametric setting, but parametric distributions, such as the negative binomial (NB) distributions studied here, are also very useful for modeling populations abundance. Never-theless, the parametric approach is ill-suited from an exploratory point of view, because the visual distance between parameters is irrelevant. On the contrary, considering the Riemannian manifold NB ( D R ) of NB distributions equipped with the Rao metrics D R , one can compute intrinsic distances between species which can be considered as absolute. Unfortunately, computing this distance requires solving a second-order nonlinear di(cid:27)erential equation, whose solution cannot be always found in an acceptable length of time with enough precision. While MantØ and KidØ [1] proposed numerical remedies to these problem, we propose a geometrical one, based on Poisson approximation. It consists in su-perseding A and/or B by "equivalent" better-suited distribution(s) before computing the distance, insofar as possible. The proposed method is illustrated by displaying distributions of counts of marine species: these counts having been (cid:28)tted by NB distributions, we compute the distance table ∆ between species and represent ∆ through multidimensional scaling (MDS).
Pilot CEOs and Corporate Cash Holdings

We examine the effect of situation awareness developed from aviation training and experiences on corporate cash policies. We find that firms led by pilot CEOs are more likely to have higher cash holdings and higher market value of cash holdings. The findings suggest that pilot CEOs have greater situation awareness, and that these CEOs are more likely to plan ahead to cope with future liquidity needs. In additional tests, we find the level of pilot certification is associated with corporate cash holdings and the value of cash holdings. These findings provide further evidence that situation awareness can be developed through training and aviation experience. We also find that the effect of pilot CEOs on corporate cash holdings is more pronounced for firms with high growth opportunities and firms with financial constraints. These results indicate that pilot CEOs tend to hold more cash when they are aware of future opportunities and risks.
Afro-pessimism, Fugitivity, and the Border to Social Death

Flight generally entails borders. Whether prison walls, plot boundaries, or borders between states, being fugitive implies that borders have been and/or are still to be overcome. One might assume that flight ends when the borders that stood between the captive and their freedom have been successfully crossed. Enslaved African Americans frequently fled their enslavers and legal owners in North America to gain freedom by, for instance, crossing the demarcating lines between slave plantation and the wilderness or the Mason-Dixon Line, the Ohio River, and the borders to Canada and Mexico into ‘free’ territory. However, with legislation such as the Fugitive Slave Acts, a fugitive slave remained retrievable property even in the supposedly ‘Free North’ so that freedom for a fugitive slave in nineteenth century North America was only a constrained form of freedom, if the term applies at all. But what if the ‘social death’ (Patterson 1982) that enslavement brought over ‘people racialised as Black’ (Coleman 2014: n.p.) has been never-ending as the Afro-pessimist Frank B. Wilderson III (2010) has suggested? And if so, how can we conceptualize Black social life that has undoubtedly endured despite social death in such a framework?
Designing A Framework for Categorizing Health- Subject Posts in Social Media

Posts with health content are shared on social networking sites by government agencies, fan clubs or social marketers on a daily basis. These posts play an important role in informing people and linking them on social networks in order to share their ideas, change their behavior, or contribute to their diseases. Despite the prominence of the role of social networks in social marketing, so far this field has not been formally analyzed in the literature. Therefore, the purpose of this study is to provide a formal analysis of posts with health content and to propose a framework for categorizing them based on their message content. With this in mind, the study performed qualitative content analysis involving three interrelated coding procedures. First, the study reviewed earlier works in the social marketing literature and more recent analysis of the function of social networks to improve health to identify initial coding categories (deductive coding). Second, the study drew together systematic inferences from a purposive sample of health-subject posts (n = 342) to derive initial coding categories (inductive coding). Finally, the study implemented a double-coding procedure on a probabilistic sample of health-subject posts (n = 264) to validate the initial coding categories (validation coding). Collectively, the three coding procedures produced 7 exhaustive and mutually exclusive categories of health-subject posts. The proposed classification provides a comprehensive framework for thinking about posts with health content. For social marketers, it provides guidance to create the stream of content necessary to stimulate daily interactions in social media channels. For researchers, it offers a solid conceptual foundation to categorize and measure health-subject posts.
Pay Attention to Features, Transfer Learn Faster CNNs

Deep convolutional neural networks are now widely deployed in vision applications, but the size of training data can bottleneck their performance. Transfer learning offers the chance for CNNs to learn with limited data samples by transferring knowledge from weights pretrained on large datasets. On the other hand, blindly transferring all learned features from the source dataset brings unnecessary computation to CNNs on the target task. In this paper, we propose attentive feature distillation and selection (AFDS) that not only adjusts the strength of regularization introduced by transfer learning but also dynamically determines which are the important features to transfer. When deploying AFDS on ResNet-101, we achieve state-of-the-art computation reduction at the same accuracy budget, outperforming all existing transfer learning methods. On a 10x MACs reduction budget, transfer learned from ImageNet to Stanford Dogs 120, AFDS achieves an accuracy that is 12.51% higher than its best competitor.
Path Planning for Ellipsoidal Robots via Generalized Closed-form Minkowski Operations

Path planning has long been one of the major research areas in robotics, with PRM and RRT being two of the most effective path planners. Though they are generally very efficient, these two samplebased planners can become computationally expensive in the important special case of narrow passage problems. This paper develops a path planning paradigm which uses ellipsoids and superquadrics to respectively encapsulate the rigid parts of the robot and obstacles. The main benefit in doing this is that configuration-space obstacles can be parameterized in closed form, thereby allowing prior knowledge to be used to avoid sampling infeasible configurations, in order to solve the narrow passage problem efficiently. Benchmark results for single-body robots show that, remarkably, the proposed method outperforms the sample-based planners in terms of the computational time in searching for a path through narrow corridors. Feasible extensions that integrate with sample-based planners to further solve the high dimensional multi-body problems are discussed, which will require substantial additional theoretical development in the future.