Miracle-dz/UniFAR-FTUs huggingface dataset:
of Marketing Relationship value and relationship quality : Broadening the nomological network of business-to-business relationships

Purpose – Established models of buyer-seller relationships do not reflect managerial emphasis on supplier performance evaluation when modelling business relationships. Proposes that relationship value should be included as a key constituent in such models. Aims to explore the construct’s links with key constituents of relationship quality, i.e. commitment, satisfaction, and trust. Design/methodology/approach – A two-stage research design was used. First, depth-interviews were conducted with ten senior-level purchasing managers in US manufacturing companies. Second, data were gathered in a nation-wide mail survey among 400 purchasing professionals. Findings – The findings suggest that relationship value is an antecedent to relationship quality and behavioural outcomes in the nomological network of relationship marketing. Value displays a stronger impact on satisfaction than on commitment and trust. Value also directly impacts a customer’s intention to expand business with a supplier. In turn, its impact on the propensity to leave a relationship is mediated by relationship quality. Contrary to previous research, trust does not appear in this study as an antecedent of behavioural outcomes, but as a mediator of the satisfaction-commitment link. Research limitations/implications – Confirms the role of value as a key relationship building-block. Researchers should integrate this cognitive performance-based construct in models of business relationships. Limitations and research directions refer to the sampling procedure, the need to include the supplier’s value perceptions, the possibility of conducting longitudinal research, and the opportunity to assess additional moderating variables. Practical implications – When the goal is to increase business with an existing customer, managers should focus on relationship value. In turn, when managers are concerned with the risk of customers leaving a relationship, they should focus on relationship quality. Trust appears as an important ingredient in stabilising existing business relationships. Originality/value – Stresses the pivotal role of relationship value in marketing. Contributes to a better fit between relationship marketing models and managerial practice in business markets.
COMPARISON OF TAGUCHI METHOD AND CENTRAL COMPOSITE DESIGN FOR OPTIMIZING PROCESS PARAMETERS IN VERTICAL DOUBLE GATE MOSFET

As the MOSFETs becoming smaller, the process parameters of the MOSFET are difficult to be perfectly controlled which eventually leads to the statistical variation of many process variables. The statistical modeling is one of the approaches that can be implemented to control the process parameter variations, thereby optimizing the device characteristics. This paper presents a comparative study of Taguchi method and central composite design (CCD) for optimizing the process parameters in Vertical Double Gate MOSFET. The L27 orthogonal array of Taguchi method and CCD has been utilized to optimize six process parameters towards the device characteristics. The comparative analysis between Taguchi method and CCD for optimizing the process parameters in vertical double-gate MOSFET are performed in term of their efficiency and simplicity. The observation of the final results indicates that the Taguchi method is the most suitable statistical tools over the CCD for optimizing the process parameters in the device due to its simplicity (requires less experiment runs) and its efficiency (better in overall device characteristics).
Higher order Dictionary Learning for Compressed Sensing based Dynamic MRI reconstruction

Compressed sensing (CS) is one of the predominant tools used presently to explore the possibilities in accelerating cardiac and body Magnetic Resonance (MR) imaging for achieving shorter scans and accommodating a wider patient group. CS accomplishes this by manoeuvring the scanning hardware to make much fewer measurements and imposing sparsity of the MR image in a known basis on the reconstruction process. Prior works have adopted ﬁxed transforms as well as dictionaries learned on image patches to incorporate the sparsifying basis. Despite the obvious merits of 1-D dictionary learning methods, they suffer from high computational and memory complexity when extended to higher patch sizes and are thus restricted to capturing only local features. Thus, there arises the need for an efﬁcient framework to extend the advantages of dictionary learning to higher dimensional applications like dynamic MRI (dMRI) where there exists a strong correlation across successive frames. This work employs a tensor decomposition based dictionary learning approach to effectively extend CS to dMRI and exploit the temporal gradient (TG) sparsity, thereby retaining maximum spectral and temporal resolution at higher under-sampling factors. The proposed technique is experimentally validated to achieve signiﬁcant improvement in reconstruction quality over the current state-of-art in dynamic MRI under distinct sampling trajectories and noisy conditions. Further, it facilitates faster reconstructions of the dMRI volume than existing methods, rendering it an ideal choice in critical scenarios which demand a swift diagnosis.
Laparoscopic resection of gastric GISTs. Where do we stand now? A single-centered experience

Introduction. Gastrointestinal stromal tumors (GISTs) represent a rare type of gastrointestinal neoplasms. Resection with negative margins has been established as a mainstay treatment, but laparoscopic resections are still open to debate. Material and method. This retrospective study was conducted at a single institution, with data collected over 2 years (01.01.2017-01.01.2019). The variables examined were age, tumor location with regard to the gastric wall, the results of the intraoperative endoscopy, intraoperative and postoperative complications, the surgical technique, and histopathological reports. Results. We identified 12 relevant cases, of which 8 were females and 4 males. The average tumor diameter was 2.3 cm. The majority of the lesions were located on the anterior gastric wall (8 cases), on the small curvature (2 cases), and in the pyloric region (2 cases). Intraoperative endoscopy was performed successfully in 10 cases in order to identify the lesions and guide the resection. The average operative time was 120 minutes and the average hospital stay was 5 days. The gastric wall with the lesion was resected using an Ultrasonic device, a 2-cm oncological safety margin was preserved. Conclusion. Complete surgical resection independent from the tumor size represents the current optimal treatment. From a surgical point of view, these tumors must be considered malignant and the surgeon must respect principles of oncological surgery. Maintaining tumor integrity at dissection is critical for the patient’s long-term prognosis. Laparoscopic resection independent of the tumor size is feasible.
Towards CNN-based Acoustic Modeling of Seventh Chords for Automatic Chord Recognition

In this paper, we build upon a recently proposed deep convolutional neural network architecture for automatic chord recognition (ACR). We focus on extending the commonly used major/minor vocabulary (24 classes) to an extended chord vocabulary of seven chord types with a total of 84 classes. In our experiments, we compare joint and separate classiﬁcation of the chord type and chord root pitch class using one or two separate models, respectively. We perform a large-scale evaluation using various combinations of training and test sets of different timbre complexity. Our results show that ACR with an extended chord vocabulary achieves high f-scores of 0.97 for isolated chord recordings and 0.66 for mixed contemporary popular music recordings. While the joint ACR modeling leads to the best results for isolated instrument recordings, the separate modeling strategy performs best for complex music recordings. Alongside with this paper, we publish a novel dataset for extended-vocabulary chord recognition which consists of synthetically generated isolated recordings of various musical instruments.
Financial Constraints in Search Equilibrium :

A key lesson from the Great Recession is that firms’ leverage and access to finance are important for hiring and firing decisions. It is now empirically established that bank lending is correlated with employment losses when credit conditions deteriorate. We provide further evidence of this and make causal inferences on the effect of leverage on job losses drawing on a new firm-level dataset that we assembled on employment and financial positions of European firms. Yet, in the Diamond Mortensen Pissarides (DMP) model there is no role for finance. All projects that display positive net present values are realized and financial markets are assumed to be perfect. What if financial markets are not perfect? Does a different access to finance influence the firm’s hiring and firing decisions? The paper uses the concept of limited pledgeability proposed by Holmstrom and Tirole to integrate financial imperfections and labor market imperfections. A negative shock wipes out the firm’s physical capital and leads to job destruction unless internal cash was accumulated by firms. If firms hold liquid assets they may thus protect their search capital, defined as the cost of attracting and hiring workers. The paper explores the trade-off between size and precautionary cash holdings in both partial and general equilibrium. We find that if labor market frictions disappear, so does the motive for firms to hold liquidity. This suggests a fundamental complementarity between labor market frictions and holding of liquid assets by firms.
Does power matter ? The role of power in supplier relationship management

Purpose of the paper and literature addressed: The study focuses on power relations between buyers and suppliers and discusses the role of power relations in the firm’s supplier relationship management (SRM). The nature of the buyer-supplier relationship and the ways how these relationships are handled vary due to the different influencing factors. In several studies it is argued that power balance and power relation between buyer and supplier influence strongly to the nature of the relationship and moreover, to the supply strategies used. Thus, the study aims to analyze how power influences supplier relationship management. Research method: The possible influence of power on firm’s supplier relationship management is empirically analyzed by using a case study with three cases and 23 interviews. The case companies are Finnish companies representing different size companies from three different industries. Thus, the role of power in the firm’s supplier relationship management is discussed in three different empirical contexts. Moreover, the case companies are having different kind of power relations towards their suppliers which offers various viewpoints to the research topic. Research findings: Two of the cases are on high level concerning the maturity of the supply management. The power positions of case companies when compared to their suppliers are mainly dominant. All cases have some critical supplier relationships which require special attention, however, SRM as a conscious process is not fully implemented in any case company. Two cases use power as an influencing factor in their business negotiations where possible. One case company seeks equal partners as it requires that their suppliers are powerful and can provide the best competences and create value. The main finding is that supplier relationship management differs in different power situations. When the power relation between the buyer and supplier is in balance there is a good basis for a collaborative relationship and for the adoption of strategies based on collaboration. Balanced power does not inhibit but rather encourages the use of a collaborative strategy, whereas inequality in terms of power and imbalance in the relation may prevent it. If the buyer firm is more powerful than the supplier, the buyer may not be willing to adopt a strategy based on collaboration and may prefer to sustain its power position. Main contribution: The literature review revealed that there is a research gap in studies of power and SRM because the relation between those has not been discussed extensively enough. Therefore, this study contributes by discussing the role of power relation in supplier relationship management and by analyzing how SRM and supply strategies are affected by power.
Skeleton-based Coherence Modeling in Narratives

Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them. There has been recent work in using neural networks to extract a skeleton from one sentence, and then use that skeleton to generate the next sentence for coherent narrative story generation. In this project, we aim to study if the consistency of skeletons across subsequent sentences is a good metric to characterize the coherence of a given body of text. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than baseline similarity techniques like cosine similarity and Euclidean distance. Although skeletons appear to be promising candidates for modeling coherence, our results show that sentence-level models outperform those on skeletons for evaluating textual coherence, thus indicating that the current state-of-the-art coherence modeling techniques are going in the right direction by dealing with sentences rather than their sub-parts.
A TEMPORAL SEMANTICS-BASED META RULE MODEL FROM TIME SERIES MINING

In the nature of time, time series are present in various applications. Time series rule mining is one of the most significant and interesting topics in the time series mining area. Many works on time series rule mining have been introduced. However, they have not yet had a relevant model. Modelfree rule discovery has been subject to considerable criticism. Moreover, time factor and user support were not observed much. Hence, we define a temporal semantics-based meta rule model from time series mining so as to propose formal definitions of patterns, rules, and measures using predicate logic, Allen’s temporal algebra, and temporal logic. This approach considers time factor and user support in rule mining process. Thus, discovered rules are always meaningful to users. We also develop a time series rule mining system to implement the model. Our proposed model was proved to be not only theoretical but also feasible and applicable.
Diagnostic value of specific antibody synthesis in brain of patients with neuroinfections

Introduction Viral encephalitis accounts for 40-70% of all cases worldwide, central nervous system infections pose a diagnostic challenge because clinical manifestations are not typically pathognomonic for specific pathogens, and a wide range of agents can be causative. The aim: To assess the diagnostic value of intrathecal synthesis of specific antibodies in patients with inflammatory lesions of the central nervous system. Material and methods Within the framework of the study, two groups of 90 people in each were formed from the patients with neuroinfections admitted to our Center. Intrathecal synthesis (ITS) of total (unspecific) IgG in members of one of group (group of compare) was determined. Brain synthesis of specific antibodies (Ab) to some neurotropic pathogens (herpes simplex virus 1/2, cytomegalovirus, Epstein-Barr virus, varicella zoster virus, rubella virus, Borrelies) was studied in the second group of patients (group of interest). There were no statistically significant differences between groups by gender and age. Encephalitis and encephalomyelitis prevailed among patients of both groups. Results ITS of total IgG was established in 30 (33.3 ± 6.1 %) patients of the first group with IgG index more than 0.6 indicating on inflammatory process in CNS and no marked changes of CSF. ITS of specific Ab was determined in 23 of 90 (25.6 ± 4.6 %) patients included into group of interest. In more than half of cases Ab to several infectious agents were detected simultaneously. ITS of various specificity, in particular, to measles and rubella viruses, and VZV, known as MRZ-reaction, is characteristic of some autoimmune lesions of CNS, multiple sclerosis first of all. In fact, further research of 5 patients with MRZ-reaction confirmed their autoimmune failure of CNS. Detection of ITS in the CSF samples didn’t depend on concentration of specific Ab in serum and CSF and wasn’t followed by HEB dysfunctions which were observed with the same frequency in patients with or without ITS (13.0 % and 13.6 % respectively). Conclusions Specific Ab synthesis to several neurotropic pathogens in the CSF of significant part of examined patients was established. Thus, diagnostic value of ITS of specific immunoglobulins seems to be limited to cases in which autoimmune damage of the CNS is suspected.
Curb Detection Method Based on Curvatures for Urban Autonomous Navigation

This paper addresses the problem of curb detection for ADAS or autonomous navigation in urban scenarios. The algorithm is based on clouds of 3D points and it is evaluated using 3D information from a pair of stereo cameras and also from a LIDAR. The feature to detect the curbs is the curvature of the road surface. The curvature estimation requires a dense point cloud, therefore the density of the LIDAR cloud has been increased using Iterative Closest Point (ICP) with the previous scans. The proposed algorithm can deal with curbs of different curvature and heights, from as low as 3 cms, in a range up to 20 m if the curbs are connected in the curvature image. The parameters of the curb are modeled with lines and lateral error is compared to a ground truth. The ground truth sequences were manually labeled by an expert and they are publicly available for the community.
Intervention in sexual abuse

Intervention in sexual abuse cases has several important purposes: (1) to assess risk to children and to establish a safe family environment; (2) to identify sexual offenders, to hold them accountable and/or to protect the community; and (3) to treat the psychological consequences of abuse experiences and to promote healthy development that will reduce risk for long term negative outcomes. Child protection, criminal justice, and therapeutic interventions may be necessary depending on the individual case circumstances.
CORRECTION OF MOVEMENT STEREOTYPE IN PATIENTS WITH SPASTIC FORMS OF CEREBRAL PALSY

It is well known that the main clinical pattern of cerebral palsy is characterized by the inability of the patient to maintain normal vertical posture of the body and perform voluntary movements [4]. Very promising approach to reducing treatment in this sense is the consideration of human movement stereotype in the context of balanced work of the functional association of the skeletal muscles which are represented by the longitudinal muscle groups, muscle pairs and muscle spirals (MS). Interacting with each other, paired longitudinal associations of muscles retain symmetry of the body and are involved in the movements of the spine and the axial skeleton as a whole. Ventrally located spinal muscles act as ß exors; muscles located dorsally act as extensors. Simultaneous reduction of homolateral, ventral and dorsal groups complement the action of the lateral metameric muscles that provide lateral tilt of the spine. Muscle pairs are associations of muscles that provide stabilization and motion kinematic parts of the body around particular axis of rotation. Muscle helix is a functional association of muscles which provide rotational-translational movement. Basis MS is a chain of the skeletal muscles, essential to the functioning of transmission from link to link. Wherein each spiral belongs to a certain set of muscles, from which some of them may be involved in other spirals. In case of violation the spiral muscle interactions develop changes that cut through bilateral symmetry of the body and reduce muscle performance. MS normally run from the head, neck, extend to the muscles of the upper limbs, the muscles of the back, chest to the opposite leg. They support the axial skeleton, dynamically
Running Head: ROTC ACRONYM USAGE AND GROUP ASSIMLATION AUIRTROSAI (Acronym Usage in ROTC: The Relationship of Socialization and Identification) Word Count: 5,456 ROTC ACRONYM USAGE AND GROUP ASSIMLATION

This study analyzed the relationship between knowledge of acronym meaning and group member’s socialization and identification. It examined the understudied relationship between knowledge of acronym meaning by cadets in Reserve Officer Training Corps (ROTC) programs and their socialization and identification in the group. Research has concluded that when assimilated into a group the group members contribute more, and overall have greater satisfaction (Riddle, Anderson, & Martin, 2000). This assimilation can be separated into two variables, socialization and identification of group members. Research on the relationship for knowledge of acronym meaning and assimilation has been understudied; therefore, this project explored how the terms we use in groups impacts our connection within the group. ROTC ACRONYM USAGE AND GROUP ASSIMLATION 3 AUIRTROSAI (Acronym Usage in ROTC: The Relationship of Socialization and Identification) Have you ever joined a new group excited for all the possibilities your membership can bring, then quickly realize the other members are using unfamiliar terms? This experience depicts the impact of jargon usage in organizations, defined as “the technical terminology of characteristic idiom of a special activity or group” (Strehlow, 1983, pg. 23). The member is privy to more information than the newcomer (Wang, Cheng, & Wang, 2016). The newcomer is left feeling disconnected from the organization they just joined, since they cannot readily communicate with other group members using the jargon. Research has concluded that when assimilated into a group, the group members contribute more and have greater satisfaction (Riddle, Anders, & Martin, 2000). This assimilation can be broken down into two variables: socialization and identification of group members. This paper is organized around prior research of group socialization and identification, the methods for the study, and a final discussion. This research project analyzes the relationship between knowledge of acronyms and the group member’s socialization and identification.
A deep learning assessment of spike detection with multi-electrode arrays

To understand how the brain produces the diversity of behaviours observed in animals it is important to have quantitative methods to measure neural activity, particularly at the population level. Recent developments in integrated circuit design and microfabrication have made possible the production of large and dense multi-electrode arrays with hundreds of electrodes. However, computational methods to analyze the data recorded by these new generation probes did not keep up with the technological evolution. In Neto et al. (2016), a ground-truth dataset from simultaneous in-vivo recordings of 128-channel dense extracellular silicon probe and a juxtacellular probe was ﬁrst presented. In this work, these data were used to evaluate the performance and limitations of a recent method for spike detection proposed in Rossant et al. called SpikeDetekt. After, is reported the application of methods for deep learning to perform detection of extracellular action potentials from the same data. Diﬀerent models are tested and applied to the same data. In all datasets the performance achieved was equal or better than that achieved with SpikeDetekt. Finally limitations of this new method are discussed and possible directions for improvement are proposed.