Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploration of an effective development process in integrative strategy

Abstract Through complete supply chain integration, manufacturers can effectively improve performance. However, not all manufacturers can improve performance through a complete integration. According to related research, the problem arises from a poor integration strategy development process. For this reason, the purpose of this study is to identify an effective development process for an integration strategy. Based on the purpose of this study, the two following research propositions should be explored: (1) identify the development stage number in the process and (2) explore the compositions of integration strategies and the competitive capabilities for each development stage to further explore how to establish an integration strategy incrementally. The taxonomy approach was the main methodology used in this study. Empirical data from the International Manufacturing Strategy Survey were used to test this research question. By testing and satisfying these two research propositions, it was determined that an effective development integration strategy process can be divided into four stages as follows: starter, advanced integrator, positive integrator and complete integrator. This study provides a suggestion for an effective gradual integration with partners and obtains a stepwise competitive advantage. Based on the research results, manufacturers can effectively develop a stepwise integration strategy to attain competitive qualifications in the initial period and maintain a competitive advantage.
A study of the laryngeal and pharyngeal consonants in Jordanian Arabic using nasoendoscopy, videofluoroscopy and spectrography

Instrumental data from seven speakers of Jordanian Arabic are analysed to gain further understanding of the production of the laryngeal and pharyngeal phonemes and their phonetic and phonological relationships. Results from nasoendoscopy and videofluoroscopy show that the extent of retraction of the epiglottis into the pharynx and over the glottis is an important factor distinguishing between laryngeals on the one hand and pharyngeals on the other. Spectrographic analysis identifies degree of approximation of F1 and F2 as the principal acoustic correlate of the distinction, and psychoacoustic analysis shows that auditory integration of F1 and F2 takes place in open vowels in the context of the pharyngeals but not the laryngeals. The chapter concludes by suggesting that Esling’s model of the laryngeal articulator can coherently justify a phonological analysis in which the pharyngeals are regarded as the emphatic counterparts of the laryngeals, i.e. as emphatic laryngeals.
Content of a Warm up programme for instrumental musicians: A Delphi study

The role of warm up or physical exercise in the prevention of Playing Related Musculoskeletal Disorders (PRMD) among instrumental musicians has been identified over the years. However, its role could not be ascertained probably due to the non – standardised practice and pattern of the warm up exercise and physical exercise. Stretching is usually considered as a physical warm up whereas the mode and type of stretching is not usually specified. The aim of this study is to determine the content of a warm up exercise programme for instrumental musicians. This is a Delphi study and it was conducted in three phases. Experts with experience in the field of music, research and managing injuries of the performing artists, were purposively selected to be members of the panel via the Performing Arts Medicine Association (PAMA) member directory. Seven experts in the performing arts medicine participated in the study. The content of the warm up program using the Delphi study includes stretching exercises and musical warm up prior to playing while strengthening and conditioning should be done thrice a week.
Capitalizing on SME Green Innovation Capabilities: Lessons from Irish-Welsh Collaborative Innovation Learning Network

Abstract Adopting an EU policy lens, this chapter primarily addresses the proposed pivotal role of firm-level innovation capability (FLIC) in small and medium-sized enterprises (SMEs) as a stimulant of sustainable development (SD) and green growth in Ireland/Wales. The chapter specifically examines the scale and scope of the green economy (GE), and considers the importance of organizational inherent “green” innovation capabilities (GICs) to achieve it. Underpinning the study is the methodology and concept of utilizing a facilitated cross-border multi-stakeholder learning network to enable knowledge transfer and exchange practices to flourish between partners, acting as a significant predictor of the development of SME GICs structures. Specifically, against the backdrop of the Green Innovation and Future Technologies (“GIFT” hereafter) INTERREG 4A Project, the research assesses how academic–industry partner exchange and inter-group learning and cooperation facilitates the development of GICs in smaller enterprises to realize a sustainable smart green economy in Ireland.
A CASE STUDY ON IMPLICIT AND EXPLICIT ONLINE PRONUNCIATION TUTORING TO CHINESE INTERNATIONAL GRADUATE STUDENTS

Pronunciation matters the and does not Especially where the negative washback from exam-driven English teaching styles is still prominent. This study compared the progress of two Chinese graduate students in a Midwestern university, one received one-on-one implicit instruction and another explicit instruction online. The measurements of progress in this case study include segmental perception and production, fluency, and intelligibility rate in both controlled and spontaneous speech. Both participants received eight weekly tutoring sessions in a semester, with one hour per session. All participants took a pre-test and a post-test the weeks before and after the instructions. The tests aimed to get and examine vowels /ʊ/, /u/, /i:/, /ɪ/, /æ/, /ɛ/ and consonants /v/, /w/, /s/, /z/, also temporal variables including words per minute (WPM) and filled-pause ratio (FPR). The results found that the explicit participant showed a slight improvement of their segmental perception and production of some back vowels and the labiodental consonant, as well as fluency and intelligibility rate, especially in spontaneous speech. But both instructed participants reported an elevated confidence in spoken English after the treatment. participate in a voluntary research study. The of this study is to discover efficient methods for short-term online pronunciation tutoring. Participating in this study will involve a pre- and post- test, and up to 8 one-hour-long tutoring sessions. Your participation will last 6 to 8 weeks. Risks related to this research are no greater than experienced in everyday life. Benefits related to this research include a possible enhancement of confidence in oral English and communicative skills during the period of this study.
Distance to the Technology Frontier and the Allocation of Talent

This paper develops a matching model - in which heterogeneous individuals form production teams and choose an appropriate technology - to study how the technological environment changes the pattern of matching and the production structure of an economy. I show that if technology improvements are expensive, teams bring high and low skilled individuals together and all teams choose similar technologies. In contrast, if improvements are inexpensive, talent concentrates in teams that choose the most advanced technologies, leading to larger dispersion of economic activity. Then, I apply the theory to study cross-country differences in the allocation of talent. Since relatively poor countries can make cheap and large improvements through technology adoption, the theory predicts that they should have stronger concentration of talent than relatively rich ones. I derive an empirical measure of the concentration of talent from the model to validate this prediction using micro data from several countries; in the cross-section, using a sample of 63 countries, and in the time-series, comparing the growth experiences of South Korea and United States. Finally, a dynamic extension of the theoretical framework demonstrates that endogenous allocation of talent coupled with localized technological progress may prevent cross-country convergence even in the absence of exogenous barriers.
Diagnostic errors: understanding the concepts through spine examples.

Poster: "ECR 2016 / C-0444 / Diagnostic errors: understanding the concepts through spine examples. " by: "R. Miron Mombiela1, F. Facal de Castro2, J. Mota Martinez3; 1Valencia, Gu/ES, 2Valencia/ES, 3Barcelona/ES"
Brain networks alterations in cocaine use and gambling disorders during emotion regulation

Abstract Background Cocaine use disorder (CUD) and gambling disorder (GD) share clinical features and neural alterations, including emotion regulation deficits and dysfunctional activation in related networks. However, they also exhibit differential aspects, such as the neuroadaptive effects of long-term drug consumption in CUD as compared to GD. Neuroimaging research aimed at disentangling their shared and specific alterations can contribute to improve understanding of both disorders. Methods We compared CUD (N = 15), GD (N = 16) and healthy comparison (HC; N = 17) groups using a network-based approach for studying temporally coherent functional networks during functional magnetic resonance imaging (fMRI) of an emotion regulation task. We focused our analysis in limbic, ventral frontostriatal, dorsal attentional (DAN) and executive networks (FPN), given their involvement in emotion regulation and their alteration in CUD and GD. Correlations with measures of emotional experience and impulsivity (UPPS-P) were also performed. Results The limbic network was significantly decreased during emotional processing both for CUD and GD individuals compared to the HC group. Furthermore, GD participants compared to HC showed an increased activation in the ventral frontostriatal network during emotion regulation. Finally, networks' activation patterns were modulated by impulsivity traits. Conclusions Functional network analyses revealed both overlapping and unique effects of stimulant and gambling addictions on neural networks underpinning emotion regulation.
Analogous Hawking radiation and quantum entanglement in two-component Bose-Einstein condensates: The gapped excitations

The condensates of cold atoms at zero temperature in the tunable binary Bose-Einstein condensate system are studied with the Rabi transition between atomic hyperﬁne states where the system can be represented by a coupled two-ﬁeld model of gapless excitations and gapped excitations. We set up the conﬁguration of the supersonic and subsonic regimes with the acoustic horizon between them in the elongated two-component Bose-Einstein condensates, trying to mimic Hawking radiations, in particular due to the gapped excitations. The simpliﬁed steplike sound speed change is adopted for the subsonic-supersonic transition so that the model can be analytically treatable. The eﬀective-energy gap term in the dispersion relation of the gapped excitations introduces the threshold frequency ω min in the subsonic regime, below which the propagating modes do not exist. Thus, the particle spectrum of the Hawking modes signiﬁcantly deviates from that of the gapless cases near the threshold frequency due to the modiﬁed gray-body factor, which vanishes as the mode frequency is below ω min . The inﬂuence from the gapped excitations to the quantum entanglement of the Hawking mode and its partner of the gapless excitations is also studied according to the Peres-Horodecki-Simon (PHS) criterion. It is found that the presence of the gapped excitations will deteriorate the quantumness of the pair modes of the gapless excitations when the frequency of the pair modes in particular is around ω ∼ ω min . On top of that, when the coupling constant between the gapless and gapped excitations becomes large enough, the huge particle density of the gapped excitations in the small ω regime will signiﬁcantly disentangle the pair modes of the gapless excitations. The detailed time-dependent PHS criterion will be discussed.
Adversarial Estimators

We develop an asymptotic theory of adversarial estimators ('A-estimators'). They generalize maximum-likelihood-type estimators ('M-estimators') as their average objective is maximized by some parameters and minimized by others. This class subsumes the continuous-updating Generalized Method of Moments, Generative Adversarial Networks and more recent proposals in machine learning and econometrics. In these examples, researchers state which aspects of the problem may in principle be used for estimation, and an adversary learns how to emphasize them optimally. We derive the convergence rates of A-estimators under pointwise and partial identification, and the normality of functionals of their parameters. Unknown functions may be approximated via sieves such as deep neural networks, for which we provide simplified low-level conditions. As a corollary, we obtain the normality of neural-net M-estimators, overcoming technical issues previously identified by the literature. Our theory yields novel results about a variety of A-estimators, providing intuition and formal justification for their success in recent applications.
Multi-scale VLBI observations of the candidate host galaxy of GRB 200716C

We present the discovery and the subsequent follow up of radio emission from SDSS J130402.36+293840.6 (J1304+2938), the candidate host galaxy of the gamma-ray burst (GRB) GRB 200716C. The galaxy is detected in the RACS (0.89 GHz), the NVSS, the Apertif imaging survey, and the FIRST (1.4 GHz), the VLASS (3 GHz), and in public LOFAR (130–170 MHz), WISE (3.4–22 μm), and SDSS (z, i, r, g, u filters) data. The luminosity inferred at 1.4 GHz is (5.1 ± 0.2) × 1030 erg s−1 Hz−1. To characterise the emission and distinguish between different components within the galaxy, we performed dedicated, high-sensitivity and high-resolution observations with the European VLBI Network (EVN) + e-MERLIN at 1.6 and 5 GHz. We did not detect any emission from a compact core, suggesting that the presence of a radio-loud active galactic nucleus (AGN) is unlikely, and therefore we ascribe the emission observed in the public surveys to star-forming regions within the galaxy. We confirm and refine the redshift estimate, z = 0.341 ± 0.004, with a dedicated Telescopio Nazionale Galileo (TNG) spectroscopic observation. Finally, we compiled a list of all the known hosts of GRB afterglows detected in radio and computed the corresponding radio luminosity: if GRB 200716C belongs to J1304+2938, this is the third most radio-luminous host of a GRB, implying one of the highest star-formation rates (SFRs) currently known, namely SFR ∼ 324±61 M⊙ yr−1. On the other hand, through the analysis of the prompt emission light curve, recent works suggest that GRB 200716C might be a short-duration GRB located beyond J1304+2938 and gravitationally lensed by an intermediate-mass black hole (IMBH) hosted by the galaxy. Neither the public data nor our Very Long Baseline Interferometry (VLBI) observations can confirm or rule out the presence of an IMBH acting as a (milli-)lens hosted by the galaxy, a scenario still compatible with the set of radio observations presented in this work.
Three-vertex prime graphs and reality of trees

Abstract We continue the study of prime simple modules for quantum affine algebras from the perspective of q-fatorization graphs. In this paper we establish several properties related to simple modules whose q-factorization graphs are afforded by trees. The two most important of them are proved for type A. The first completes the classification of the prime simple modules with three q-factors by giving a precise criterion for the primality of a 3-vertex line which is not totally ordered. Using a very special case of this criterion, we then show that a simple module whose q-factorization graph is afforded by an arbitrary tree is real. Indeed, the proof of the latter works for all types, provided the aforementioned special case is settled in general.
Sparse Graphical Modelling via the Sorted L$_1$-Norm

Sparse graphical modelling has attained widespread attention across various academic fields. We propose two new graphical model approaches, Gslope and Tslope, which provide sparse estimates of the precision matrix by penalizing its sorted L1-norm, and relying on Gaussian and T-student data, respectively. We provide the selections of the tuning parameters which provably control the probability of including false edges between the disjoint graph components and empirically control the False Discovery Rate for the block diagonal covariance matrices. In extensive simulation and real world analysis, the new methods are compared to other state-of-the-art sparse graphical modelling approaches. The results establish Gslope and Tslope as two new effective tools for sparse network estimation, when dealing with both Gaussian, t-student and mixture data.
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map

Deep learning has recently achieved significant progress in trajectory forecasting. However, the scarcity of trajectory data inhibits the data-hungry deep-learning models from learning good representations. While mature representation learning methods exist in computer vision and natural language processing, these pre-training methods require large-scale data. It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e.g., 34K samples in the nuScenes dataset). To work around the scarcity of trajectory data, we resort to another data modality closely related to trajectories-HD-maps, which is abundantly provided in existing datasets. In this paper, we propose PreTraM, a self-supervised pre-training scheme via connecting trajectories and maps for trajectory forecasting. Specifically, PreTraM consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset. We show that PreTraM improves data efficiency and scales well with model size.
Crescent: taming memory irregularities for accelerating deep point cloud analytics

3D perception in point clouds is transforming the perception ability of future intelligent machines. Point cloud algorithms, however, are plagued by irregular memory accesses, leading to massive inefficiencies in the memory sub-system, which bottlenecks the overall efficiency. This paper proposes Crescent, an algorithm-hardware co-design system that tames the irregularities in deep point cloud analytics while achieving high accuracy. To that end, we introduce two approximation techniques, approximate neighbor search and selectively bank conflict elision, that "regularize" the DRAM and SRAM memory accesses. Doing so, however, necessarily introduces accuracy loss, which we mitigate by a new network training procedure that integrates approximation into the network training process. In essence, our training procedure trains models that are conditioned upon a specific approximate setting and, thus, retain a high accuracy. Experiments show that Crescent doubles the performance and halves the energy consumption compared to an optimized baseline accelerator with < 1% accuracy loss. The code of our paper is available at: https://github.com/horizon-research/crescent.
Keypoint based Sign Language Translation without Glosses

Sign Language Translation (SLT) is a task that has not been studied relatively much compared to the study of Sign Language Recognition (SLR). However, the SLR is a study that recognizes the unique grammar of sign language, which is diﬀerent from the spoken language and has a problem that non-disabled people cannot easily interpret. So, we’re going to solve the problem of translating directly spoken language in sign language video. To this end, we propose a new keypoint normalization method for performing translation based on the skeleton point of the signer and robustly normalizing these points in sign language translation. It contributed to performance improvement by a customized normalization method depending on the body parts. In addition, we propose a stochastic frame selection method that enables frame augmentation and sampling at the same time. Finally, it is translated into the spoken language through an Attention-based translation model. Our method can be applied to various datasets in a way that can be applied to datasets without glosses. In addition, quantitative experimental evaluation proved the excellence of our method.
Differentially Private Learning with Margin Guarantees

We present a series of new differentially private (DP) algorithms with dimension-independent margin guarantees. For the family of linear hypotheses, we give a pure DP learning algorithm that benefits from relative deviation margin guarantees, as well as an efficient DP learning algorithm with margin guarantees. We also present a new efficient DP learning algorithm with margin guarantees for kernel-based hypotheses with shift-invariant kernels, such as Gaussian kernels, and point out how our results can be extended to other kernels using oblivious sketching techniques. We further give a pure DP learning algorithm for a family of feed-forward neural networks for which we prove margin guarantees that are independent of the input dimension. Additionally, we describe a general label DP learning algorithm, which benefits from relative deviation margin bounds and is applicable to a broad family of hypothesis sets, including that of neural networks. Finally, we show how our DP learning algorithms can be augmented in a general way to include model selection, to select the best confidence margin parameter.
In Private, Secure, Conversational FinBots We Trust

In the past decade, the financial industry has experienced a technology revolution. While we witness a rapid introduction of conversational bots for financial services, there is a lack of understanding of conversational user interfaces (CUI) features in this domain. The finance industry also deals with highly sensitive information and monetary transactions, presenting a challenge for developers and financial providers. Through a study on how to design text-based conversational financial interfaces with N=410 participants, we outline user requirements of trustworthy CUI design for financial bots. We posit that, in the context of Finance, bot privacy and security assurances outweigh conversational capability and postulate implications of these findings. This work acts as a resource on how to design trustworthy FinBots and demonstrates how automated financial advisors can be transformed into trusted everyday devices, capable of supporting users' daily financial activities.