Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploration and Exploitation: Driving Organizational Capability and Organizational Change Toward Competitive Advantage

Abstract There are two important aspects that usually influence the organizational competitive advantage, i.e. organizational capabilities and organizational change. This study aimed to frame the role of organizational capabilities and organizational change based on exploration and exploitation in the form of balancing and ambidexterity toward organizational competitive advantage, and justify that the organizational capabilities and organizational change are becoming important in organizational competitiveness, and those are becoming more effective if there are complementary relationships between exploration and exploitation. For this purpose, a systematic thematic approach is applied by collecting, synthesizing, reviewing, and understanding previous related scientific articles. Sustainable competitive advantage of firms may lay in the firm’s ability to simultaneously balancing exploration and exploitation. Organizations that focus exclusively either on exploration or exploitation, as well as neither on exploration nor exploitation, may become obsolete. Balancing both exploitation and exploration activities creates an ambidextrous organization and allows this organization to be creative and adaptable at the same time, and it can drive organizational capabilities and organizational change in generating the realization of superior competitive organizations.
An Updated Meta-analysis: Similar Clinical Efficacy of Anterior and Posterior Approaches in Peroral Endoscopic Myotomy (POEM) for Achalasia

Introduction Currently, there are few studies on the efficacy of peroral endoscopic myotomy (POEM) in the anterior or posterior approach; however, limited studies have shown contradictory findings. Thus, the goal was to obtain more quantitative and objective outcomes and further compare the clinical efficacy of these two approaches in this meta-analysis. Methods A comprehensive search of PubMed, Embase, Cochrane Library, and Web of Science was conducted to find studies relevant to POEM. The retrieval time was from database inception to September 2021. Studies reporting the effects of POEM according to the anterior or posterior approach were included. STATA 16.0 was used to perform statistical analysis, mainly comparing the quantitative objective indicators (lower esophageal sphincter (LES) pressure and Eckardt scores, etc.) in anterior and posterior approaches by meta-analysis. Result A total of 19 studies with 1261 patients were finally included. Except for shorter procedure time in the posterior approach, other factors (pooled difference of LES pressure, Eckardt scores, clinical success, length of total myotomy, hospital stays, gastroesophageal reflux (GERD), and adverse event) were compared, and all above confirmed that there is no difference between anterior and posterior approaches, and the safety of POEM is ensured. In addition, both anterior and posterior myotomy can improve LES pressure and Eckardt scores, and the difference in anterior and posterior myotomy was unconspicuous. Conclusion The terms of the pooled difference in LES pressure, Eckardt scores, and other factors (clinical success, length of total myotomy, hospital stays, GERD, adverse events, and procedure time) seemed to be similar for the anterior and posterior approaches. However, the further prognosis after POEM via anterior and posterior approaches needs to be answered in the future.
EFFECT OF MINDFULNESS AND RESILIENCE ON THE EMOTIONAL COMPETENCE OF YOUTH

This paper assesses the effect of mindfulness and resilience on emotional competence of youth. For this purpose, 112 Youth (56 males and 56 females) took Five facet mindfulness scale(Baer,2006) Brief Resilience scale (Smith, 2008) and Emotional competence scale (Sharma & Bhardwaj,2008). Mindfulness is the basic human ability to be fully aware of their actions and their present which helps in reducing stress, gaining insight and attention. Resilience is the ability to bounce back from adverse situations where as according to Boyatzis et al (1999) emotional competence is how a person demonstrates the competencies that comprises of self-awareness, self-management, social awareness, and social skills at appropriate times. The results indicate a significant positive relationship between mindfulness, resilience and emotional competence. Further mindfulness and resilience positively predicts emotional competence of Youth. No significant gender difference was found with respect to the level of mindfulness, resilience and emotional competence
Assessment of the Necessity of Osteoporosis Treatment for Patients with Low Bone Density in Diffuse Idiopathic Skeletal Hyperostosis

Introduction Although patients with diffuse idiopathic skeletal hyperostosis (DISH) do not have low bone density, it is a risk factor for spine fractures associated with DISH. We investigated the characteristics and bone metabolism markers of patients with DISH having low bone density to assess whether osteoporosis medication is necessary to prevent fractures. Methods A cross-sectional study was conducted between April 1, 2008, and March 31, 2019. The 86 patients included were divided into two groups according to their T-scores―one group had low bone density and DISH, and the other group did not. Group A (T-score≤−1) and B (T-score>−1) data were adjusted for confounding factors and compared for differences in age, body weight, maximum number of vertebral bodies with bony bridges between adjacent vertebrae (max VB), and previous history (hypertension, malignant tumors, diabetes mellitus, cardiac diseases, chronic renal failure, and spinal fractures). In Group A, multiple linear regression was used to investigate relationships among max VB, femur bone mineral density (BMD), total type I procollagen N-terminal propeptide (P1NP), and tartrate-resistant acid phosphatase 5b (TRACP-5b). Results Group A had 36, and Group B had 50 male patients with DISH. Patients in Group B were heavier than those in Group A. The mean femur BMD in Group A was age-appropriate, and that in Group B was higher than the age-appropriate femur BMD. The mean values of P1NP and TRACP-5b were within the normal range. Max VB was positively correlated with total P1NP in Group A. Total P1NP was significantly and positively correlated with TRACP-5b. Conclusions The DISH group with a T-score of ≤−1 was age-appropriate. The group with a T-score of >−1 had higher BMD because of their higher body weight. The group with a T-score of ≤−1 had good bone metabolism and did not require aggressive osteoporosis treatment.
Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks

Deep neural networks achieve remarkable performance in multiple fields. However, after proper training they suffer from an inherent vulnerability against adversarial examples (AEs). In this work we shed light on inner representations of the AEs by analysing their activations on the hidden layers. We test various types of AEs, each crafted using a specific norm constraint, which affects their visual appearance and eventually their behavior in the trained networks. Our results in image classification tasks (MNIST and CIFAR-10) reveal qualitative differences between the individual types of AEs, when comparing their proximity to the class-specific manifolds on the inner representations. We propose two methods that can be used to compare the distances to class-specific manifolds, regardless of the changing dimensions throughout the network. Using these methods, we consistently confirm that some of the adversarials do not necessarily leave the proximity of the manifold of the correct class, not even in the last hidden layer of the neural network. Next, using UMAP visualisation technique, we project the class activations to 2D space. The results indicate that the activations of the individual AEs are entangled with the activations of the test set. This, however, does not hold for a group of crafted inputs called the rubbish class. We also confirm the entanglement of adversarials with the test set numerically using the soft nearest neighbour loss.
Learning to Express in Knowledge-Grounded Conversation

Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studies focus on how to synthesize a response with proper knowledge, yet neglect that the same knowledge could be expressed differently by speakers even under the same context. In this work, we mainly consider two aspects of knowledge expression, namely the structure of the response and style of the content in each part. We therefore introduce two sequential latent variables to represent the structure and the content style respectively. We propose a segmentation-based generation model and optimize the model by a variational approach to discover the underlying pattern of knowledge expression in a response. Evaluation results on two benchmarks indicate that our model can learn the structure style defined by a few examples and generate responses in desired content style.
Fermionic approach to variational quantum simulation of Kitaev spin models

We use the variational quantum eigensolver (VQE) to simulate Kitaev spin models with and without integrability breaking perturbations, focusing in particular on the honeycomb and square-octagon lattices. These models are well known for being exactly solvable in a certain parameter regime via a mapping to free fermions. We use classical simulations to explore a novel variational ansatz that takes advantage of this fermionic representation and is capable of expressing the exact ground state in the solvable limit. We also demonstrate that this ansatz can be extended beyond this limit to provide excellent accuracy when compared to other VQE approaches. In certain cases, this fermionic representation is advantageous because it reduces by a factor of two the number of qubits required to perform the simulation. We also comment on the implications of our results for simulating non-Abelian anyons on quantum computers.
The drag exerted by weakly dissipative trapped lee waves on the atmosphere: Application to Scorer's two‐layer model

Although it is known that trapped lee waves propagating at low levels in a stratified atmosphere exert a drag on the mountains that generate them, the distribution of the corresponding reaction force exerted on the atmospheric mean circulation, defined by the wave momentum flux profiles, has not been established, because for inviscid trapped lee waves these profiles oscillate indefinitely downstream. A framework is developed here for the unambiguous calculation of momentum flux profiles produced by trapped lee waves, which circumvents the difficulties plaguing the inviscid trapped lee wave theory. Using linear theory, and taking Scorer's two‐layer atmosphere as an example, the waves are assumed to be subject to a small dissipation, expressed as a Rayleigh damping. The resulting wave pattern decays downstream, so the momentum flux profile integrated over the area occupied by the waves converges to a well‐defined form. Remarkably, for weak dissipation, this form is independent of the value of Rayleigh damping coefficient, and the inviscid drag, determined in previous studies, is recovered as the momentum flux at the surface. The divergence of this momentum flux profile accounts for the areally integrated drag exerted by the waves on the atmosphere. The application of this framework to this and other types of trapped lee waves potentially enables the development of physically based parametrizations of the effects of trapped lee waves on the atmosphere.
Variational Heteroscedastic Volatility Model

We propose Variational Heteroscedastic Volatility Model (VHVM) -- an end-to-end neural network architecture capable of modelling heteroscedastic behaviour in multivariate financial time series. VHVM leverages recent advances in several areas of deep learning, namely sequential modelling and representation learning, to model complex temporal dynamics between different asset returns. At its core, VHVM consists of a variational autoencoder to capture relationships between assets, and a recurrent neural network to model the time-evolution of these dependencies. The outputs of VHVM are time-varying conditional volatilities in the form of covariance matrices. We demonstrate the effectiveness of VHVM against existing methods such as Generalised AutoRegressive Conditional Heteroscedasticity (GARCH) and Stochastic Volatility (SV) models on a wide range of multivariate foreign currency (FX) datasets.
Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback

For summarization, human preference is critical to tame outputs of the summarizer in favor of human interests, as ground-truth summaries are scarce and ambiguous. Practical settings require dynamic exchanges between human and AI agent wherein feedback is provided in an online manner, a few at a time. In this paper, we introduce a new framework to train summarization models with preference feedback interactively. By properly leveraging offline data and a novel reward model, we improve the performance regarding ROUGE scores and sample-efficiency. Our experiments on three various datasets confirm the benefit of the proposed framework in active, few-shot and online settings of preference learning.
Perception of Mechanical Properties via Wrist Haptics: Effects of Feedback Congruence

Despite non-co-location, haptic stimulation at the wrist can potentially provide feedback regarding interactions at the fingertips without encumbering the user's hand. Here we investigate how two types of skin deformation at the wrist (normal and shear) relate to the perception of the mechanical properties of virtual objects. We hypothesized that a congruent mapping (i.e. when the most relevant interaction forces during a virtual interaction spatially match the haptic feedback at the wrist) would result in better perception than other mappings. We performed an experiment where haptic devices at the wrist rendered either normal or shear feedback during manipulation of virtual objects with varying stiffness, mass, or friction properties. Perception of mechanical properties was more accurate with congruent skin stimulation than noncongruent. In addition, discrimination performance and subjective reports were positively influenced by congruence. This study demonstrates that users can perceive mechanical properties via haptic feedback provided at the wrist with a consistent mapping between haptic feedback and interaction forces at the fingertips, regardless of congruence.
Text-Driven Separation of Arbitrary Sounds

We propose a method of separating a desired sound source from a single-channel mixture, based on either a textual description or a short audio sample of the target source. This is achieved by combining two distinct models. The ﬁrst model, SoundWords , is trained to jointly embed both an audio clip and its textual description to the same embedding in a shared representation. The second model, SoundFilter , takes a mixed source audio clip as an input and separates it based on a conditioning vector from the shared text-audio representation deﬁned by SoundWords, making the model agnostic to the conditioning modality. Evaluating on multiple datasets, we show that our approach can achieve an SI-SDR of 9.1 dB for mixtures of two arbitrary sounds when conditioned on text and 10.1 dB on the easier task of conditioned on audio from a different part of the same audio clip. We also show that SoundWords is effective at learning co-embeddings and that our multi-modal training approach improves the performance of SoundFilter.
Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid

For fast timescales or long prediction horizons, the AC optimal power flow (OPF) problem becomes a computational challenge for large-scale, realistic AC networks. To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network. The method takes as inputs the full AC network and a pre-computed library of AC load flow data and uses the graph Laplacian to constraint nodal reductions to only be feasible for neighbors of non-reduced nodes. This results in a highly effective MILP formulation which is embedded within an iterative scheme to successively improve the Kron-based network reduction until convergence. The resulting optimal network reduction is, thus, grounded in the physics of the full network. The accuracy of the network reduction methodology is then explored for a 100+ node medium-voltage radial distribution feeder example across a wide range of operating conditions. It is finally shown that a network reduction of 25-85% can be achieved within seconds and with worst-case voltage magnitude deviation errors within any super node cluster of less than 0.01pu. These results illustrate that the proposed optimization-based approach to Kron reduction of networks is viable for larger networks and suitable for use within various power system applications.
Towards Polyglot Data Stores - Overview and Open Research Questions

Nowadays, data-intensive applications face the problem of handling heterogeneous data with sometimes mutually exclusive use cases and soft non-functional goals such as consistency and availability. Since no single platform copes everything, various stores (RDBMS, NewSQL, NoSQL) for different workloads and use-cases have been developed. However, since each store is only a specialization, this motivates progress in polyglot data management emerged new systems called Mult- and Polystores. They are trying to access different stores transparently and combine their capabilities to achieve one or multiple given use-cases. This paper describes representative real-world use cases for data-intensive applications (OLTP and OLAP). It derives a set of requirements for polyglot data stores. Subsequently, we discuss the properties of selected Multi- and Polystores and evaluate them based on given needs illustrated by three common application use cases. We classify them into functional features, query processing technique, architecture and adaptivity and reveal a lack of capabilities, especially in changing conditions tightly integration. Finally, we outline the benefits and drawbacks of the surveyed systems and propose future research directions and current challenges in this area.
Detection of long range dependence in the time domain for (in)finite-variance time series

Empirical detection of long range dependence (LRD) of a time series often consists of deciding whether an estimate of the memory parameter $d$ corresponds to LRD. Surprisingly, the literature offers numerous spectral domain estimators for $d$ but there are only a few estimators in the time domain. Moreover, the latter estimators are criticized for relying on visual inspection to determine an observation window $[n_1, n_2]$ for a linear regression to run on. Theoretically motivated choices of $n_1$ and $n_2$ are often missing for many time series models. In this paper, we take the well-known variance plot estimator and provide rigorous asymptotic conditions on $[n_1, n_2]$ to ensure the estimator's consistency under LRD. We establish these conditions for a large class of square-integrable time series models. This large class enables one to use the variance plot estimator to detect LRD for infinite-variance time series (after suitable transformation). Thus, detection of LRD for infinite-variance time series is another novelty of our paper. A simulation study indicates that the variance plot estimator can detect LRD better than the popular spectral domain GPH estimator.
Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets

Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of this research study, we aimed to predict the future stock movement of shares using the historical prices aided with availability of sentiment data. Two models were used as part of the exercise, LSTM was the first model with historical prices as the independent variable. Sentiment Analysis captured using Intensity Analyzer was used as the major parameter for Random Forest Model used for the second part, some macro parameters like Gold, Oil prices, USD exchange rate and Indian Govt. Securities yields were also added to the model for improved accuracy of the model. As the end product, prices of 4 stocks viz. Reliance, HDFC Bank, TCS and SBI were predicted using the aforementioned two models. The results were evaluated using RMSE metric.
Not always about you: Prioritizing community needs when developing endangered language technology

Languages are classified as low-resource when they lack the quantity of data necessary for training statistical and machine learning tools and models. Causes of resource scarcity vary but can include poor access to technology for developing these resources, a relatively small population of speakers, or a lack of urgency for collecting such resources in bilingual populations where the second language is high-resource. As a result, the languages described as low-resource in the literature are as different as Finnish on the one hand, with millions of speakers using it in every imaginable domain, and Seneca, with only a small-handful of fluent speakers using the language primarily in a restricted domain. While issues stemming from the lack of resources necessary to train models unite this disparate group of languages, many other issues cut across the divide between widely-spoken low-resource languages and endangered languages. In this position paper, we discuss the unique technological, cultural, practical, and ethical challenges that researchers and indigenous speech community members face when working together to develop language technology to support endangered language documentation and revitalization. We report the perspectives of language teachers, Master Speakers and elders from indigenous communities, as well as the point of view of academics. We describe an ongoing fruitful collaboration and make recommendations for future partnerships between academic researchers and language community stakeholders.
Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval

The Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) is a chal-lenging task because of the large domain gap between sketches and natural images as well as the semantic inconsistency between seen and unseen categories. Previous literature bridges seen and unseen categories by semantic embedding, which requires prior knowledge of the exact class names and additional extraction ef-forts. And most works reduce domain gap by mapping sketches and natural images into a common high-level space using constructed sketch-image pairs, which ignore the unpaired information between images and sketches. To address these issues, in this paper, we propose a novel Three-Stream Joint Training Network (3JOIN) for the ZS-SBIR task. To narrow the domain diﬀerences between sketches and images, we extract edge maps for natural images and treat them as a bridge between images and sketches, which have similar content to images and similar style to sketches. For exploiting a suﬃcient combination of sketches, natural images, and edge maps, a novel three-stream joint training network is proposed. In addition, we use a teacher network to extract the implicit semantics of the samples without the aid of other semantics and transfer the learned knowledge to unseen classes. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed method.