Miracle-dz/UniFAR-FTUs huggingface dataset:
VC-GPT: Visual Conditioned GPT for End-to-End Generative Vision-and-Language Pre-training

Vision-and-language pre-trained models (VLMs) have achieved tremendous success in the cross-modal area, but most of them require a large amount of parallel image-caption data for pre-training. Collating such data is expensive and labor-intensive. In this work, we focus on reducing such need for generative vision-and-language pre-training (G-VLP) by taking advantage of the visual pre-trained model (CLIP-ViT) as encoder and language pre-trained model (GPT2) as decoder. Unfortunately, GPT2 lacks a necessary cross-attention module, which hinders the direct connection of CLIP-ViT and GPT2. To remedy such defects, we conduct extensive experiments to empirically investigate how to design and pre-train our model. Based on our experimental results, we propose a novel G-VLP framework, Visual Conditioned GPT (VC-GPT) , and pre-train it with a small-scale image-caption corpus (Visual Genome, only 110k distinct images). Evaluating on the image captioning downstream tasks (MSCOCO and Flickr30k Captioning), VC-GPT achieves either the best or the second-best performance across all evaluation metrics over the previous works which consume around 30 times more distinct images during cross-modal pre-training.
Feature-Attending Recurrent Modules for Generalizing Object-Centric Behavior

To generalize in object-centric tasks, a reinforcement learning (RL) agent needs to exploit the structure that objects induce. Prior work has either hard-coded object-centric features, used complex object-centric generative models, or updated state using local spatial features. However, these approaches have had limited success in enabling general RL agents. Motivated by this, we introduce ``Feature-Attending Recurrent Modules'' (FARM), an architecture for learning state representations that relies on simple, broadly applicable inductive biases for capturing spatial and temporal regularities. FARM learns a state representation that is distributed across multiple modules that each attend to spatiotemporal features with an expressive feature attention mechanism. This enables FARM to represent diverse object-induced spatial and temporal regularities across subsets of modules. We hypothesize that this enables an RL agent to flexibly recombine its experiences for generalization. We study task suites in both 2D and 3D environments and find that FARM better generalizes compared to competing architectures that leverage attention or multiple modules.
Shelf-life of papaya (Carica papaya L.) as influenced by fruit maturity, holding time in ambient and 1-methylcyclopropene treatment.

Experiments were done to determine the postharvest responses of two fruit maturity stages (mature green, 5-15% yellow, Y) of newly harvested Carica papaya 'Kapoho' to various holding times (0, 1, 2 days) in ambient prior to treatment with 1-methylcyclopropene (1-MCP) in an air-tight chamber. Total soluble solid (TSS) at the end of shelf life was higher in 5-15Y fruit treated with 100 nl L -1 1-MCP. In the 5-15Y lot, peel yellowing was lower in fruit held for 2 days suggesting slower peel color change at 6 days after treatment (DAT) but not at 9 DAT. Visual quality was better in mature green fruit. In the 5-15Y lot, treated fruit were firmer; disease less severe; weight loss lower; and there was a 59% improvement in shelf life, 73% delay in days to edible ripe stage (DERS), 51 % delay in days to disease occurrence (DDO) in fruit held 2 days relative to the control. DDO was also delayed in mature green fruit held for 1 or 2 days prior to treatment. Fruit (5-15Y) held for a day prior to treatment with various 1-MCP concentrations (0, 50, 100, 150 nl L -1 ) showed that 1-MCP at 100 nl L -1 resulted in a longer shelf life of 14.3 days or 30% improvement, less yellow but firmer fruit at 9 DAT as well as DERS and DDO at 23 and 25%, respectively. Respiration rate in treated 5-15Y was lower regardless of holding time. All treated fruit reached edible ripe stage. Thus, holding fruit for a day or two in ambient conditions prior to treatment with 100 nl L -1 1-MCP is effective in prolonging shelf life of 'Kapoho' papaya.
Fatigue Life Monitoring System for Aircraft to Flexibilize Operations and Maintenance Planning

The damage tolerance analysis, which uses the force structural management plan, is usually employed in airplane fatigue life prediction. This plan, based on a mission “mix” defined at the airplane structure design phase, induces the squadrons to follow this mix and interferes in the fleet’s usage efficiency. Deviations in the mission mix have impacts on inspection intervals and maintenance costs. In this paper, a fatigue life monitoring system is proposed. It is able to incorporate each mission, individually, in a specific aircraft’s fatigue life analysis, adjusting the maintenance plan into a more realistic usage profile, based on the individual aircraft tracking methodology. This monitoring system is implemented in a computer software and it is applied to F-5E fighter aircraft of the Brazilian Air Force. In addition, experimental fatigue tests that simulate critical locations of this aircraft are compared with the proposed monitoring system, which shows ability to predict, in conservative and approximat.
Gasdermin D Inhibits Coronavirus Infection by Promoting the Noncanonical Secretion of Beta Interferon

Coronaviruses, primarily targeting respiratory and gastrointestinal epithelia in vivo, have a serious impact on humans and animals. GSDMD, a main executioner of pyroptosis, is highly expressed in epithelial cells and involves viral infection pathogenesis. ABSTRACT Pyroptosis, a programmed cell death, functions as an innate immune effector mechanism and plays a crucial role against microbial invasion. Gasdermin D (GSDMD), as the main pyroptosis effector, mediates pyroptosis and promotes releasing proinflammatory molecules into the extracellular environment through pore-forming activity, modifying inflammation and immune responses. While the substantial importance of GSDMD in microbial infection and cancer has been widely investigated, the role of GSDMD in virus infection, including coronaviruses, remains unclear. Enteric coronavirus transmissible gastroenteritis virus (TGEV) and porcine deltacoronavirus (PDCoV) are the major agents for lethal watery diarrhea in neonatal pigs and pose the potential for spillover from pigs to humans. In this study, we found that alphacoronavirus TGEV upregulated and activated GSDMD, resulting in pyroptosis after infection. Furthermore, the fragment of swine GSDMD from amino acids 242 to 279 (242-279 fragment) was required to induce pyroptosis. Notably, GSDMD strongly inhibited both TGEV and PDCoV infection. Mechanistically, the antiviral activity of GSDMD was mediated through promoting the nonclassical release of antiviral beta interferon (IFN-β) and then enhancing the interferon-stimulated gene (ISG) responses. These findings showed that GSDMD dampens coronavirus infection by an uncovered GSDMD-mediated IFN secretion, which may present a novel target of coronavirus antiviral therapeutics. IMPORTANCE Coronaviruses, primarily targeting respiratory and gastrointestinal epithelia in vivo, have a serious impact on humans and animals. GSDMD, a main executioner of pyroptosis, is highly expressed in epithelial cells and involves viral infection pathogenesis. While the functions and importance of GSDMD as a critical regulator of inflammasome activities in response to intracellular bacterial infection have been extensively investigated, the roles of GSDMD during coronavirus infection remain unclear. We here show that alphacoronavirus TGEV triggered pyroptosis and upregulated GSDMD expression, while GSDMD broadly suppressed the infection of enteric coronavirus TGEV and PDCoV by its pore-forming activity via promoting unconventional release of IFN-β. Our study highlights the importance of GSDMD as a regulator of innate immunity and may open new avenues for treating coronavirus infection.
MOBILE CRM IN INDUSTRIAL FIRMS: SALESPEOPLE'S INTENTION TO ADOPT MOBILE SOLUTIONS

Nisula, Rosa; Pirttiniemi, Janne Mobile CRM in industrial firms: Salespeople’s intention to adopt mobile solutions Jyvaskyla: University of Jyvaskyla, 2013, 119 p. Information Systems, Master’s Thesis Supervisor(s): Frank, Lauri; Karjaluoto, Heikki Mobile technology and services have gained attention in organisations and academia alike in terms of what are their impact on organisational performance. There is however little information on what drives salespeople to adopt mobile technology. Our study contributes this discourse by investigating what are the antecedents of behavioural intention when regarding the use of mobile customer relationship management in the course of sales managers’ work. To examine this, a conceptual model was developed based on literature on technology adoption and acceptance research. Specific insight was sought from literature focusing on mobile technology adoption and sales automation technology. This study utilised exploratory quantitative approach in order to empirically test the model. An online survey was sent to 5 Finnish firms operating in industrial sector. In total, the survey gathered 105 responses globally. The model was tested via structural equation modelling (SEM), and more specifically partial least squares (PLS) was chosen to estimate the parameters of SEM. Our findings indicated that behavioural intention was sufficiently explained by perceived behavioural control, perceived usefulness and perceived reachability. Social influences explained both key technology related beliefs (i.e., perceived ease of use and perceived usefulness), whereas personal innovativeness only explained perceived ease of use. Contrary to our hypothesis, attitude did not exert sufficient effect on behavioural intention.
Tsunami flood risk prediction using a neural network

The 2011 earthquake off the Pacific coast of Tohoku (known as the “Sendai Earthquake”) occurred on March 11, 2011 and severely damaged the Tohoku coastal area facing the Pacific Ocean. The number of casualties in the Sendai earthquake, as of August 9, 2013, included 15,883 deaths, with 2656 people still missing. The cause of death for nearly 90% of those killed was drowning because many people were swept away by the resulting tsunami. Focusing on the area inundated by the current induced by the tsunami, even in adjacent areas, a difference was apparent in the inundation distances from the coastline. Clarification of the reason for this difference is necessary for city planning when considering disaster reduction in the future. In this study, we first clarify the factors that promote inundation in the Sendai area, excluding the effects from small ground undulations, by using a neural network that has been used to solve a complex relationship with each of the factors. Based on the results of the factors that promote inundation, we have selected Iwata City in the Shizuoka Prefecture as a study area because its region is similar to the Sendai area that has flat level ground and also it is likely to be struck by a tsunami generated from any Tokai, Tounankai, and Nankai type earthquake. In our risk prediction, we estimate the risk for tsunami damage in Iwata City and propose measures to reduce this damage.
Crowdsourcing Backstories for Complex Task-Based Search

Backstories provide vital contextual information for information retrieval evaluation. They are useful as textual representations of information needs, for example to aid in relevance judgements as part of test collections for performance evaluation, for studying longer search queries, and for interactive retrieval. While backstories exist for some popular search tasks and domains thanks to evaluation campaigns such as TREC, NTCIR, and CLEF, they are not available for a large range of other tasks and domains. In this paper, we explore crowdsourcing as an approach for obtaining high-quality backstories, with the aim of supporting the development of backstories as key resources for new domains and search tasks. Compared to typical crowdsourcing tasks in the IR domain, such as gathering relevance judgements or short textual search queries, obtaining backstories is more complex. Workers are required to think of information need scenarios and put these thoughts into comparatively lengthy text fragments. This possibly entails a higher cognitive load and longer working time. We describe a crowdsourcing methodology to maximise the usefulness of results, using the creation of backstories for the job search domain as an example. We also present and release a collection of 756 job search backstories, which was obtained via the proposed methodology.
Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures

The problem of the electrical characterization of single-phase transformers is addressed in this research through the application of the crow search algorithm (CSA). A nonlinear programming model to determine the series and parallel impedances of the transformer is formulated using the mean square error (MSE) between the voltages and currents measured and calculated as the objective function. The CSA is selected as a solution technique since it is efficient in dealing with complex nonlinear programming models using penalty factors to explore and exploit the solution space with minimum computational effort. Numerical results in three single-phase transformers with nominal sizes of 20 kVA, 45 kVA, 112.5 kVA, and 167 kVA demonstrate the efficiency of the proposed approach to define the transformer parameters when compared with the large-scale nonlinear solver fmincon in the MATLAB programming environment. Regarding the final objective function value, the CSA reaches objective functions lower than 2.75×10−11 for all the simulation cases, which confirms their effectiveness in minimizing the MSE between real (measured) and expected (calculated) voltage and current variables in the transformer.
Neural Correlates of Physical Activity Moderate the Association Between Problematic Mobile Phone Use and Psychological Symptoms

Background Despite the evidence of an association between problematic mobile phone use (PMPU) and psychological symptoms, a few studies explore whether physical activity (PA) could moderate the effect of PMPU on psychological symptoms and its neural substrates. The aim of this study was to examine the association between PMPU and psychological symptoms in late adolescents, along with the potential moderating effect of PA and neural basis by brain gray matter volume (GMV). Methods A total of 251 college students reported on their PMPU, PA, and psychological symptoms and subsequently underwent structural magnetic resonance imaging to explore the neural basis of their PA characteristics. A multiple regression model was performed to detect brain GMV associated with PA by the voxel-based morphometry (VBM) method. Moderating analysis was conducted using PROCESS macro in the SPSS software. Results Behavioral results showed that PMPU was correlated to depression, anxiety, and stress, and PA has significantly moderated the association between PMPU with depression, anxiety, and stress. The VBM analysis showed that PA was correlated to GMV of the right fusiform gyrus (FFG), left precuneus (PCUN), left insula (INS), and left triangular part of inferior frontal gyrus (IFGtriang). Moreover, GMV of the left INS moderated the relationship between PMPU and depression. Conclusion This study has shed light on the neural perspective of PA that moderates the relationship between PMPU and depressive symptom.
Stealth "radar"

The system presented in this paper has the purpose of airplane detection including stealth airplanes. The system is sensitive to any natural emission of energy or background noise coming from anything beyond the targeted airplane. In contrast to conventional radar that illuminates the target with microwaves, the targeted airplane does not receive any signal that it has been detected by this new system. The algorithm for computing of what is being detected considers that the airplane causes a shade on the background noise and this shade indicates the position of the airplane. The polar diagram of sensitivity of the detector may be the narrowest possible, however it does not need to be narrower than the solid angle of the airplane because it will always disturb consistently the reception of the background noise.
Graph Rewriting Techniques in Engineering Design

Capturing human knowledge underlying the design and engineering of products has been among the main goals of computational engineering since its very beginning. Over the last decades, various approaches have been proposed to tackle this objective. Among the most promising approaches is the application of graph theory for representing product structures by defining nodes representing entities and edges representing relations among them. The concrete meaning of these structures ranges from geometry representations over hierarchical product breakdowns to functional descriptions and flows of information or resources. On top of these graph structures, graph rewriting techniques provide another powerful layer of technology. By enabling the formal definition of rules for transforming graph structures, they allow on the one hand side to formally capture the engineering development process. On the other hand, the assembly of rewriting rules into graph grammars allows for an exhaustive search of the solution space of the engineering problem at hand. In combination with search strategies, an automated optimization of the design under given constraints and objectives can be realized. The paper provides an overview of the current state-of-the-art in graph rewriting and its applications in engineering design, with a focus on the built environment. It concludes with a discussion of the progress achieved and the missing research gaps.
A 0.18-μm CMOS High-Performance Up-Conversion Mixer for 2.4-GHz Transmitter Application

In this paper, a low-voltage low-power up-conversion mixer, designed in the chartered 0.18-mm RFCMOS technology, is proposed to realize the transmitter front-end in the frequency band of 2.4 GHz. The proposed mixer uses the dual current-reuse and current-bleeding techniques in both the driver and switching stages with a simple impedance degeneration. The proposed mixer can convert a 10 MHz intermediate frequency (IF) signals to a 2.4 GHz RF signals, with a local oscillator (LO) power of 2dBm at 2.39 GHz. A comparison with conventional CMOS up-conversion mixer shows that this mixer has advantages of low-voltage, low-power consumption and high performance. Simulation results demonstrate that at 2.4 GHz, the circuit provides 6.3 dB of conversion gain and the input-referred third-order intercept point (IIP3) of 13.45dBm, while drawing only 4 mA for the mixer core under a supply voltage of 1.2 V. The chip area including testing pads is only 0.7 mmÔ0. 8 mm. Index Terms – Up-conversion mixer, CMOS, current-reuse, current-bleeding, high linearity, low power
Quantile Treatment Effect of Zinc Lozenges on Common Cold Duration: A Novel Approach to Analyze the Effect of Treatment on Illness Duration

Calculation of the difference of means is the most common approach when analyzing treatment effects on continuous outcomes. Nevertheless, it is possible that the treatment has a different effect on patients who have a lower value of the outcome compared with patients who have a greater value of the outcome. The estimation of quantile treatment effects (QTEs) allows the analysis of treatment effects over the entire distribution of a continuous outcome, such as the duration of illness or the duration of hospital stay. Furthermore, most of these outcomes have asymmetric distributions with fat tails, and censored observations are not uncommon. These features can be accounted for in the analysis of the QTE. In this paper, we use the QTE approach to analyze the effect of zinc lozenges on common cold duration. We use the data set of the Mossad (1996) trial with zinc gluconate lozenges, and three data sets of trials with zinc acetate lozenges. In the Mossad (1996) trial, zinc gluconate lozenges shortened common cold duration on average by 4.0 days (95% CI 2.3–5.7 days). However, the QTE analysis indicates that 15- to 17-day colds were shortened by 8 days, and 2-day colds by just 1 day, for the group taking zinc lozenges. Thus, the overall 4.0-day average effect of zinc gluconate lozenges in the Mossad (1996) trial is inconsistent with our QTE findings for both short and long colds. Similar results were found in our QTE analysis of the pooled data sets of the three zinc acetate lozenge trials. The average effect of 2.7 days (95% CI 1.8–3.3 days) was inconsistent with the effects on short and long colds. The QTE approach may have broad usefulness for examining treatment effects on the duration of illness and hospital stay, and on other similar outcomes.
Neural Substrates and Models of Omission Responses and Predictive Processes

Predictive coding theories argue that deviance detection phenomena, such as mismatch responses and omission responses, are generated by predictive processes with possibly overlapping neural substrates. Molecular imaging and electrophysiology studies of mismatch responses and corollary discharge in the rodent model allowed the development of mechanistic and computational models of these phenomena. These models enable translation between human and non-human animal research and help to uncover fundamental features of change-processing microcircuitry in the neocortex. This microcircuitry is characterized by stimulus-specific adaptation and feedforward inhibition of stimulus-selective populations of pyramidal neurons and interneurons, with specific contributions from different interneuron types. The overlap of the substrates of different types of responses to deviant stimuli remains to be understood. Omission responses, which are observed both in corollary discharge and mismatch response protocols in humans, are underutilized in animal research and may be pivotal in uncovering the substrates of predictive processes. Omission studies comprise a range of methods centered on the withholding of an expected stimulus. This review aims to provide an overview of omission protocols and showcase their potential to integrate and complement the different models and procedures employed to study prediction and deviance detection.This approach may reveal the biological foundations of core concepts of predictive coding, and allow an empirical test of the framework’s promise to unify theoretical models of attention and perception.