Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic Marketing in Business-to-Business Markets - User Related Benefits of Sales Configurators

Electronic marketing is becoming an integral part of the sales process in business-to-business (B2B) markets. In line with that, sales configurators are emerging as novel applications that help companies engage customer and drive sales. This research investigates the feature related benefits of sales configurator. Our goal is to categorize the benefits and to identify the important ones. In order to reach the goal, personal interviews were conducted and data was gathered through an online questionnaire. Responses from 152 business-to-business customers were analyzed and a factorial model of the feature related benefits of sales configurators was developed. The results show a model with five factors: versatility, configurability, user experience, security, and customizability. Of these, user experience and security were found to be the most important. In light of the this, we suggest that companies emphasize the aspects of user experience in addition to core functionalities when developing sales force automation systems.
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network

Accurate estimates of rotation are crucial to vision-based motion estimation in augmented reality and robotics. In this work, we present a method to extract probabilistic estimates of rotation from deep regression models. First, we build on prior work and argue that a multi-headed network structure we name HydraNet provides better calibrated uncertainty estimates than methods that rely on stochastic forward passes. Second, we extend HydraNet to targets that belong to the rotation group, SO(3), by regressing unit quaternions and using the tools of rotation averaging and uncertainty injection onto the manifold to produce three-dimensional covariances. Finally, we present results and analysis on a synthetic dataset, learn consistent orientation estimates on the 7-Scenes dataset, and show how we can use our learned covariances to fuse deep estimates of relative orientation with classical stereo visual odometry to improve localization on the KITTI dataset.
Tests of life or life of tests?: similarities and differences in parents’ and teachers’ prioritisation of character, academic attainment, the virtues and moral theories

ABSTRACT Although the area of parental involvement in education is well researched, much less is known about how parents and teachers might work together to cultivate desirable character virtues in their children/pupils. This article considers three potential barriers to parents/teachers forming such partnerships: i) differing views on the importance of character compared to academic attainment; ii) their prioritisation of moral, performance, civic and intellectual virtues; and iii) their prioritisation of different moral theories in ethical decision making. The article describes the findings from a quantitative study conducted with 376 parents and 137 teachers. The study found that both parents and teachers prioritise character over academic attainment but perceive the opposite to be true of their counterpart. Further, both parents and teachers rank moral virtues, such as honesty, as the most important, followed by performance virtues, such as resilience. The findings are significant as they illuminate a possible gap between parents and teachers in England, which, if addressed, will ensure children and young people are more likely to develop character qualities that contribute to individual and societal flourishing. Given that several countries are (re)introducing character education into the curriculum, the results of the study also have international significance.
Probiotic Lactobacilli in Fermented Dairy Products: Selective Detection, Enumeration and Identification Scheme

A selection of 36 commercial probiotic fermented dairy products from UK and Europe markets were evaluated for the numbers, types, and viability of Lactobacillus strains against the stated information on their packages. A comparative study was carried out on selectivity of MRS-Clindamycin, MRS-Sorbitol, and MRS-IM Maltose, to select the right medium for enumeration of probiotic Lactobacillus. Based on selectivity of medium for recovery of the targeted lactobacilli, and also simplicity of preparation, MRS-Clindamycin was chosen as the best medium for enumeration of probiotic Lactobacillus in fermented milks. The results of enumeration of lactobacilli showed that 22 out of a total 36 tested products contained more than 106 colony-forming units/g at the end of their shelf life, which comply with the recommended minimum therapeutic level for probiotics. Rep-PCR using primer GTG-5 was applied for initial discrimination of isolated strains, and isolates, which presented different band profile, were placed in different groups. The isolated Lactobacillus spp. were identified mainly as Lactobacillus acidophilus, Lactobacillus casei, and Lactobacillus paracasei by analysis of partial sequences of the 16S ribosomal RNA and rpoA genes.
Junta Correlation is Testable

The problem of tolerant junta testing is a natural and challenging problem which asks if the property of a function having some specified correlation with a k-Junta is testable. In this paper we give an affirmative answer to this question: There is an algorithm which given distance parameters c, d, and oracle access to a Boolean function f on the hypercube, has query complexity exp(k). poly(1/(c-d)) and distinguishes between the following cases: 1. The distance of f from any k-junta is at least c; 2. There is a k-junta g which has distance at most d from f. This is the first non-trivial tester (i.e., query complexity is independent of the ambient dimension n) which works for all c and d (bounded by 0.5). The best previously known results by Blais et~al., required c to be at least 16d. In fact, with the same query complexity, we accomplish the stronger goal of identifying the most correlated k-junta, up to permutations of the coordinates. We can further improve the query complexity to poly(k/(c-d)) for the (weaker) task of distinguishing between the following cases: 1. The distance of f from any k'-junta is at least c. 2. There is a k-junta g which is at a distance at most d from f. Here k'=poly(k/(c-d)). Our main tools are Fourier analysis based algorithms that simulate oracle access to influential coordinates of functions.
Deep Learning for Improved Agricultural Risk Management

Deep learning provides many benefits, including automation, speed, accuracy, and intelligence, and it is delivering competitive performance now across a wide range of real-world operational applications – from credit card fraud detection to recommender systems and customer segmentation. Its potential in actuarial sciences and agricultural insurance/risk management, however, remains largely untapped. In this pilot study, we investigate deep learning in predicting agricultural yield in time and space under weather/climate uncertainty. We evaluate the predictive power of deep learning, benchmarking its performance against more conventional approaches alongside both weather station and climate. Our findings reveal that deep learning offers the highest predictive accuracy, outperforming all the other approaches. We infer that it also has great potential to reduce underwriting inefficiencies and insurance coverage costs associated with using more imprecise yield-based metrics of real risk exposure. Future work aims to further evaluate its performance, from municipal area-yield, to finer-scale crop-specific producer-scale yield.
Characterizing the Local Relation between Star Formation Rate and Gas-phase Metallicity in MaNGA Spiral Galaxies

The role of gas accretion in galaxy evolution is still a matter of debate. The presence of inflows of metal-poor gas that trigger star formation bursts of low metallicity has been proposed as an explanation for the local anticorrelation between star formation rate (SFR) and gas-phase metallicity (Zg) found in the literature. In the present study, we show how the anticorrelation is also present as part of a diversified range of behaviors for a sample of more than 700 nearby spiral galaxies from the SDSS-IV MaNGA survey. We have characterized the local relation between SFR and Zg after subtracting the azimuthally averaged radial profiles of both quantities. Of the analyzed galaxies, 60% display an SFR–Zg anticorrelation, with the remaining 40% showing no correlation (19%) or positive correlation (21%). Applying a random forest machine-learning algorithm, we find that the slope of the correlation is mainly determined by the average gas-phase metallicity of the galaxy. Galaxy mass, g − r colors, stellar age, and mass density seem to play a less significant role. This result is supported by the performed second-order polynomial regression analysis. Thus, the local SFR–Zg slope varies with the average metallicity, with the more metal-poor galaxies presenting the lowest slopes (i.e., the strongest SFR–Zg anticorrelations), and reversing the relation for more metal-rich systems. Our results suggest that external gas accretion fuels star formation in metal-poor galaxies, whereas in metal-rich systems, the gas comes from previous star formation episodes.
Iterative Normalization: Beyond Standardization Towards Efficient Whitening

Batch Normalization (BN) is ubiquitously employed for accelerating neural network training and improving the generalization capability by performing standardization within mini-batches. Decorrelated Batch Normalization (DBN) further boosts the above effectiveness by whitening. However, DBN relies heavily on either a large batch size, or eigen-decomposition that suffers from poor efficiency on GPUs. We propose Iterative Normalization (IterNorm), which employs Newton’s iterations for much more efficient whitening, while simultaneously avoiding the eigen-decomposition. Furthermore, we develop a comprehensive study to show IterNorm has better trade-off between optimization and generalization, with theoretical and experimental support. To this end, we exclusively introduce Stochastic Normalization Disturbance (SND), which measures the inherent stochastic uncertainty of samples when applied to normalization operations. With the support of SND, we provide natural explanations to several phenomena from the perspective of optimization, e.g., why group-wise whitening of DBN generally outperforms full-whitening and why the accuracy of BN degenerates with reduced batch sizes. We demonstrate the consistently improved performance of IterNorm with extensive experiments on CIFAR-10 and ImageNet over BN and DBN.
Gender Bias in Contextualized Word Embeddings

In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) training data for ELMo contains significantly more male than female entities, (2) the trained ELMo embeddings systematically encode gender information and (3) ELMo unequally encodes gender information about male and female entities. Then, we show that a state-of-the-art coreference system that depends on ELMo inherits its bias and demonstrates significant bias on the WinoBias probing corpus. Finally, we explore two methods to mitigate such gender bias and show that the bias demonstrated on WinoBias can be eliminated.
Decoding hand kinematics from population responses in sensorimotor cortex during grasping

Objective. The hand—a complex effector comprising dozens of degrees of freedom of movement—endows us with the ability to flexibly, precisely, and effortlessly interact with objects. The neural signals associated with dexterous hand movements in primary motor cortex (M1) and somatosensory cortex (SC) have received comparatively less attention than have those associated with proximal upper limb control. Approach. To fill this gap, we trained two monkeys to grasp objects varying in size and shape while tracking their hand postures and recording single-unit activity from M1 and SC. We then decoded their hand kinematics across tens of joints from population activity in these areas. Main results. We found that we could accurately decode kinematics with a small number of neural signals and that different cortical fields carry different amounts of information about hand kinematics. In particular, neural signals in rostral M1 led to better performance than did signals in caudal M1, whereas Brodmann’s area 3a outperformed areas 1 and 2 in SC. Moreover, decoding performance was higher for joint angles than joint angular velocities, in contrast to what has been found with proximal limb decoders. Significance. We conclude that cortical signals can be used for dexterous hand control in brain machine interface applications and that postural representations in SC may be exploited via intracortical stimulation to close the sensorimotor loop.
Enabling High Value Care with A Point of Care Solution: the Australian Experience

Adopting patient-centric technology solutions is considered a critical enabler to enhance superior, high value healthcare delivery. Evidence from the literature underscores the simultaneous benefits of such an approach for enhancing the quality of care, increasing value and reducing associated costs. This study contributes to the current void in the literature by providing data from an implementation of a patient-centric solution that serves to deliver and support value based-care. Specifically, the presented study highlights how a point of care system can deliver high value patient-centric care across a healthcare group in Australia. The results from this qualitative study show that the examined point of care system supports patient-centric care by facilitating a high level of patient engagement and supporting key safety and quality care outcomes, as well as building a cultural shift towards patient-centric care as part of standard practice. The study has far reaching implications for both theory and practice.
What Doctors Wish They Knew: Treatment Compliance in an Online Health Community for Chronic Patients

Treatment compliance for patients with chronic health problems is important for the management of their illness due to the long-term nature of their conditions. In this study, we examine how evaluations of different types of treatments provided by members of an online health community are associated with treatment evaluations and compliance. We use selfreported data on evaluation and compliance of over 270 different treatments from over 20,000 patients in a prominent online health community. We find that other community members’ treatment evaluation valence is positively associated with patient treatment evaluation and treatment compliance. Similarly, other community members’ treatment compliance is positively associated with patient treatment compliance. We also find these relations are moderated by community size and ratings variance. We discuss the theoretical implications of these results for the online health communities’ literature, as well as the practical implications for patients, healthcare providers, and policy makers.
Log-barrier constrained CNNs

This study investigates imposing inequality constraints on the outputs of CNNs for weakly supervised segmentation. In the general context of deep networks, constraints are commonly handled with penalty approaches for their simplicity, and despite their well-known limitations. Lagrangian optimization has well-established theoretical and practical advantages over penalty methods, but has been largely avoided for deep CNNs, mainly due to computational complexity and stability/convergence issues caused by alternating stochastic optimization and dual updates. Several recent studies showed that, in the context of deep CNNs, the theoretical advantages of Lagrangian optimization over simple penalties do not materialize in practice, with performances that are, surprisingly, worse. We leverage well-established concepts in interior-point methods, which approximate Lagrangian optimization with a sequence of unconstrained problems, while completely avoiding dual steps/projections. Specifically, we propose a sequence of unconstrained {\em log-barrier-extension} losses for approximating inequality-constrained CNN problems. The proposed extension has a duality-gap bound, which yields sub-optimality certificates for feasible solutions in the case of convex losses. While sub-optimality is not guaranteed for non-convex problems, the result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs. Our approach addresses the well-known limitations of penalty methods and, at the same time, removes the explicit dual steps of Lagrangian optimization. We report comprehensive experiments showing that our formulation outperforms a recent penalty-based constrained CNN method, both in terms of accuracy and training stability.
Multi-interacting dark matter in the Boltzmann code CLASS

Despite its remarkable success, the standard ΛCDM paradigm has been challenged lately by significant tensions between different datasets. This has boosted interest in non-minimal dark sectors, which are theoretically well-motivated and inspire new search strategies for dark matter (DM). With this in mind, we have developed a new and efficient version of the Boltzmann code class that allows for one DM species to have simultaneous interactions with photons, baryons, and dark radiation. As a proof-of-principle of this code, we have reassessed existing cosmological bounds on the various interaction coefficients in multi-interacting DM scenarios, showing that these constraints are robust to the underlying cosmological model. We have also investigated the ability of these models to alleviate the H0 and S8 tensions, showing that the combination of DM–photons and DM – dark radiation interactions can alleviate both tensions. The upcoming public release of our code will pave the way for the study of various rich dark sectors.
Attack-Resilient Supervisory Control of Discrete-Event Systems

In this work, we study the problem of supervisory control of discrete-event systems (DES) in the presence of attacks that tamper with inputs and outputs of the plant. We consider a very general system setup as we focus on both deterministic and nondeterministic plants that we model as finite state transducers (FSTs); this also covers the conventional approach to modeling DES as deterministic finite automata. Furthermore, we cover a wide class of attacks that can nondeterministically add, remove, or rewrite a sensing and/or actuation word to any word from predefined regular languages, and show how such attacks can be modeled by nondeterministic FSTs; we also present how the use of FSTs facilitates modeling realistic (and very complex) attacks, as well as provides the foundation for design of attack-resilient supervisory controllers. Specifically, we first consider the supervisory control problem for deterministic plants with attacks (i) only on their sensors, (ii) only on their actuators, and (iii) both on their sensors and actuators. For each case, we develop new conditions for controllability in the presence of attacks, as well as synthesizing algorithms to obtain FST-based description of such attack-resilient supervisors. A derived resilient controller provides a set of all safe control words that can keep the plant work desirably even in the presence of corrupted observation and/or if the control words are subjected to actuation attacks. Then, we extend the controllability theorems and the supervisor synthesizing algorithms to nondeterministic plants that satisfy a nonblocking condition. Finally, we illustrate applicability of our methodology on several examples and numerical case-studies.
Cross-Channel Integration and Customer Retention in Omnichannel Retailing: The Role of Retailer Image and Alternative Attractiveness

Cross-channel integration (CCI) is increasingly considered as an important driver of customer retention in omnichannel retailing. However, the existing findings about the relationship between CCI and customer retention are contradictory, wherein both positive and non-significant findings exist. This study aims to explore the contingency role of retailer image and alternative attractiveness for the above relationship. Specifically, both two-way and three-way interaction effects of retailer image and alternative attractiveness were tested. Our survey finding confirmed the positive relationship between CCI and customer retention. We also found that the positive relationship was negatively moderated by retailer image, while positively moderated by alternative attractiveness. This study further uncovered that alternative attractiveness can weaken the negative moderating effect of retailer image. Implications and limitations of the study are discussed.
Social Roles, Interactions and Community Sustainability in Social Q&A Sites: A Resource-based Perspective

Online tech support communities have become valuable channels for users to seek and provide solutions to specific problems. From the resource exchange perspective, the sustainability of a social system is contingent upon the size of its members as well as their communication activities. To further extend the resource-based model, the current research identifies a variety of social roles in a large tech support Q&A forum and examines longitudinal changes in the community’s structure based on the identification. Moreover, this study also investigates the relationship between the community’s functionality and its traffic. Results suggest that the proportion of unsolved questions negatively impacts the number of future incoming questions and the outcome of a given question is not only dependent on users’ interactions within the discussion, but also on the community activities preceding the question. These observations can help community managers to improve system design and task allocation.
Weakly Supervised Semantic Segmentation of Satellite Images

When one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works with aerial or satellite images, which are usually very large, it is even worse. With that in mind, we investigate how to use image-level annotations in order to perform semantic segmentation. Image-level annotations are much less expensive to acquire than pixel-level annotations, but we lose a lot of information for the training of the model. From the annotations of the images, the model must find by itself how to classify the different regions of the image. In this work, we use the method proposed by Anh and Kwak [1] to produce pixel-level annotation from image level annotation.We compare the overall quality of our generated dataset with the original dataset.In addition, we propose an adaptation of the AffinityNet that allows us to directly perform a semantic segmentation.Our results show that the generated labels lead to the same performances for the training of several segmentation networks. Also, the quality of semantic segmentation performed directly by the AffinityNet and the Random Walk is close to the one of the best fully-supervised approaches.