Miracle-dz/UniFAR-FTUs huggingface dataset:
SakA and MpkC Stress MAPKs Show Opposite and Common Functions During Stress Responses and Development in Aspergillus nidulans

Stress activated MAP kinases (SAPKs) of the Hog1/Sty1/p38 family are specialized in transducing stress signals. In contrast to what is seen in animal cells, very few fungal species contain more than one SAPK. Aspergillus nidulans and other Aspergilli contain two SAPKs called SakA/HogA and MpkC. We have shown that SakA is essential for conidia to maintain their viability and to survive high H2O2 concentrations. H2O2 induces SakA nuclear accumulation and its interaction with transcription factor AtfA. Although SakA and MpkC show physical interaction, little is known about MpkC functions. Here we show that ΔmpkC mutants are not sensitive to oxidative stress but in fact MpkC inactivation partially restores the oxidative stress resistance of ΔsakA mutants. ΔmpkC mutants display about twofold increase in the production of fully viable conidia. The inactivation of the SakA upstream MAPKK PbsB or the simultaneous elimination of sakA and mpkC result in virtually identical phenotypes, including decreased radial growth, a drastic reduction of conidiation and a sharp, progressive loss of conidial viability. SakA and to a minor extent MpkC also regulate cell-wall integrity. Given the roles of MpkC in conidiation and oxidative stress sensitivity, we used a functional MpkC::GFP fusion to determine MpkC nuclear localization as an in vivo indicator of MpkC activation during asexual development and stress. MpkC is mostly localized in the cytoplasm of intact conidia, accumulates in nuclei during the first 2 h of germination and then becomes progressively excluded from nuclei in growing hyphae. In the conidiophore, MpkC nuclear accumulation increases in vesicles, metulae and phialides and decreases in older conidia. Oxidative and osmotic stresses induce MpkC nuclear accumulation in both germinating conidia and hyphae. In all these cases, MpkC nuclear accumulation is largely dependent on the MAPKK PbsB. Our results indicate that SakA and MpkC play major, distinct and sometimes opposing roles in conidiation and conidiospore physiology, as well as common roles in response to stress. We propose that two SAPKs are necessary to delay (MpkC) or fully stop (SakA) mitosis during conidiogenesis and the terminal differentiation of conidia, in the highly prolific phialoconidiation process characteristic of the Aspergilli.
ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers

In this letter, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage.
Russian Roulette or A Game of Whack A Mole? Effect of Overconfidence in Protecting Information Systems

Informed decision-making requires that individuals understand the risks and tradeoffs in making decisions on the potential threats that they encounter. However, it comes as a surprise that individuals who should know better may put themselves, others and their institutions at risk. The protection motivation theory states that an individual may protect themselves based on four factors. That is the perceived severity of a threatening event, the perceived probability of the occurrence, or vulnerability, the efficacy of the recommended preventive behavior, and the perceived self-efficacy. This research in progress seeks to understand why I.T professions may be susceptible to security threats. We develop a conceptual model aimed at exploring how overconfidence plays an important role in the experiences of IT Professionals. We contribute to extant literature by integrating confidence and PMT in behavioral information security.
The Relationship between Mobile Web and Mobile App Channels for Retailers

As smartphones and tablets are becoming ubiquitous, mobile ecommerce is also evolving rapidly. Consumers can shop on mobile devices in two ways; they either open a mobile browser and visit a retailer's website, or download the retailer's mobile app and shop within the app. However, it is unclear how retailers should manage these two emerging channels together. This proposed study aims to investigate the relationship between mobile web and mobile app channels by analyzing how a change to one channel affects the outcome in the other. To infer causality, we utilize an exogenous event in the mobile web channel to assess how it influences the demand of retailers' mobile apps. The results could reveal whether these two mobile channels complement or substitute each other. This study contributes to the literature of multi-channel management in mobile commerce and provides important managerial implications for retailers to better leverage the growing mobile channels.
Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation

Learning to cooperate is crucially important in multi-agent re- inforcement learning. The key is to take the inﬂuence of other agents into consideration when performing distributed deci- sion making. However, multi-agent environment is highly dynamic, which makes it hard to learn abstract representations of inﬂuences between agents by only low-order features that existing methods exploit. In this paper, we propose a graph convolutional model for multi-agent cooperation. The graph convolution architecture adapts to the dynamics of the un- derlying graph of the multi-agent environment, where the inﬂuence among agents is captured by their abstract relation representations. High-order features extracted by relation kernels of convolutional layers from gradually increased recep- tive ﬁelds are exploited to learn cooperative strategies. The gradient of an agent not only backpropagates to itself but also to other agents in its receptive ﬁelds to reinforce the learned cooperative strategies. Moreover, the relation representations are temporally regularized to make the cooperation more consistent. Empirically, we show that our model enables agents to develop more cooperative and sophisticated strategies than existing methods in jungle and battle games and routing in packet switching networks.
Does Extreme Language Control Training Improve Cognitive Control? A Comparison of Professional Interpreters, L2 Teachers and Monolinguals

There is currently a lively debate in the literature whether bilingualism leads to enhanced cognitive control or not. Recent evidence suggests that knowledge of more than one language does not always suffice for the manifestation of a bilingual cognitive control advantage. As a result, ongoing research has focused on modalities of bilingual language use that may interact with the bilingual advantage. In this study, we explored the cognitive control performance of simultaneous interpreters. These highly proficient bilinguals comprehend information in one language while producing in the other language, which is a complex skill requiring high levels of language control. In a first experiment, we compared professional interpreters to monolinguals. Data were collected on interference suppression (flanker task), prepotent response inhibition (Simon task), and short-term memory (digit span task). The results showed that the professional interpreters performed similarly to the monolinguals on all measures. In Experiment 2, we compared professional interpreters to monolinguals and second language teachers. Data were collected on interference suppression (advanced flanker task), prepotent response inhibition (advanced flanker task), attention (advanced flanker task), short-term memory (Hebb repetition paradigm), and updating (n-back task). We found converging evidence for our finding that experience in interpreting may not lead to superior interference suppression, prepotent response inhibition, and short-term memory. In fact, our results showed that the professional interpreters performed similarly to both the monolinguals and the second language teachers on all tested cognitive control measures. We did, however, find anecdotal evidence for a (small) advantage in short-term memory for interpreters relative to monolinguals when analyzing composite scores of both experiments together. Taken together, the results of the current study suggest that interpreter experience does not necessarily lead to general cognitive control advantages. However, there may be small interpreter advantages in short-term memory, suggesting that this might be an important cognitive control aspect of simultaneous interpreting. The results are discussed in the light of ongoing debates about bilingual cognitive control advantages.
Pretreatment and Multi-Feed Anaerobic Co-digestion of Agro-Industrial Residual Biomass for Improved Biomethanation and Kinetic Analysis

Batch biochemical methane potential (BMP) test of agro-industrial, agricultural, and municipal solid waste (MSW) individuals like groundnut straw, rice bran, and guar husk, mung bean husk (MBH), wheat straw (WS) and organic fraction of municipal solid waste (OFMSW), under mesophilic conditions, were performed to evaluate the biogas potential. BMP test for multi-feed anaerobic co-digestion (AcoD) of WS, OFMSW, and MBH at five mixing ratio was performed to evaluate the synergistic effect of multi feed-stocks of blending the feedstocks. Mixture ratio having OFMSW:WS:MBH of 25:5:70% composition resulted into 37, 20, and 4% higher methane yield up to 280 ml/g Volatile solid (VS) in comparison with mono-digestion of OFMSW, WS and MBH. Ultrasonic pretreatment was also performed and the experimental results showed that varying the sonication time have a significant improvement on substrate’s biodegradation and solubilization augmenting the methane yield from OFMSW, WS, and MBH by 71, 75, and 46% respectively at sonication period of 60 min. Effect of pretreatment on the substrate studied through Scanning electron microscopy (SEM), XRD and FT-IR analysis. Cone and Exponential models showed an average coefficient of determination R2=0.9855 and R2=0.9893 respectively, and RMSE values for cone model was lower than that of Exponential model showing Cone model was more precised.
Molecular Ontogeny of First-Feeding European Eel Larvae

Digestive system functionality of fish larvae relies on the onset of genetically pre-programmed and extrinsically influenced digestive functions. This study explored how algal supplementation (green-water) until 14 days post hatch (dph) and the ingestion of food [enriched rotifer (Brachionus plicatilis) paste] from 15 dph onward affects molecular maturation and functionality of European eel larval ingestion and digestion mechanisms. For this, we linked larval biometrics to expression of genes relating to appetite [ghrelin (ghrl), cholecystokinin (cck)], food intake [proopiomelanocortin (pomc)], digestion [trypsin (try), triglyceride lipase (tgl), amylase (amyl)], energy metabolism [ATP synthase F0 subunit 6 (atp6), cytochrome-c-oxidase 1 (cox1)], growth [insulin-like growth factor (igf1)] and thyroid metabolism [thyroid hormone receptors (thrαA, thrβB)]. Additionally, we estimated larval nutritional status via nucleic acid analysis during transition from endogenous and throughout the exogenous feeding stage. Results showed increased expression of ghrl and cck on 12 dph, marking the beginning of the first-feeding window, but no benefit of larviculture in green-water was observed. Moreover, expression of genes relating to protein (try) and lipid (tgl) hydrolysis revealed essential digestive processes occurring from 14 to 20 dph. On 16 dph, a molecular response to initiation of exogenous feeding was observed in the expression patterns of pomc, atp6, cox1, igf1, thrαA and thrβB. Additionally, we detected increased DNA contents, which coincided with increased RNA contents and greater body area, reflecting growth in feeding compared to non-feeding larvae. Thus, the here applied nutritional regime facilitated a short-term benefit, where feeding larvae were able to sustain growth and better condition than their non-feeding conspecifics. However, RNA:DNA ratios decreased from 12 dph onward, indicating a generally low larval nutritional condition, probably leading to the point-of-no-return and subsequent irreversible mortality due to unsuccessful utilization of exogenous feeding. In conclusion, this study molecularly identified the first-feeding window in European eel and revealed that exogenous feeding success occurs concurrently with the onset of a broad array of enzymes and hormones, which are known to regulate molecular processes in feeding physiology. This knowledge constitutes essential information to develop efficient larval feeding strategies and will hopefully provide a promising step toward sustainable aquaculture of European eel.
Privacy Negotiation Mechanism in Internet of Things Environments

Internet of Things (IoT) is considered one of the emerging technologies in the area of information technology. However, many challenges still need to be overcome, one of which is privacy. The constant collection of information by IoT devices can bring serious problems to its users' privacy. In some scenarios, users are unaware that the information shared can bring such risks. In this context, this work aims to present a privacy preservation mechanism in IoT environments. This mechanism aims to mediate the exchange of information in these environments to provide users with the ability to control the disclosure of their data. To validate the mechanism, an experiment was made with the users, in which they were underwent to several scenarios that could occur in IoT environments. The results of the mechanism learning process obtained an accuracy rate of 88.62% in the predicted scenarios. In addition, the mechanism presents the essential characteristics to help users preserve their privacy.
Rethinking Technology Acceptance: Towards a Theory of Technology Utilization

The current paper aims at introducing a new approach in technology acceptance called “Technology Utilization”. There is a body of research in failure of technology acceptance theories to support the empirical studies. The present research suggests technology utilization; that is last phase of acceptance where the main focus is on how the technology is being used. The paper reviews the literature on technology acceptance and illustrates the need for a new approach that highlights the use of technology as opposed to its uptake. The article demonstrates technology utilization concepts through an open source software project. The paper extends the literature for theoretical supports of technology acceptance by introducing the concept of technology utilization, for further development and investigation. The paper also informs practitioners that adoption of a technology is not only about its uptake but also relates to its use.
Novel Insights Into the Role of Glycans in the Pathophysiology of Glomerular Endotheliosis in Preeclampsia

The polysaccharide heparan sulfate is ubiquitously expressed as a proteoglycan in extracellular matrices and on cell surfaces. In the glomerular filtration barrier, the action of the heparan sulfate is directly related to the function of glomerular filtration, mostly attributed to the sulfated domains that occur along the polysaccharide chain, as evidenced by fact that release of fragments of heparan sulfate by heparanase significantly increases the permeability of albumin passage through the glomerular endothelium, event that originates proteinuria. This review aims to show the importance of the structural domains of heparan sulfate in the process of selective permeability and to demonstrate how these domains may be altered during the glomerular inflammation processes that occur in preeclampsia.
BCWS: Bilingual Contextual Word Similarity

This paper introduces the first dataset for evaluating English-Chinese Bilingual Contextual Word Similarity, namely BCWS (this https URL). The dataset consists of 2,091 English-Chinese word pairs with the corresponding sentential contexts and their similarity scores annotated by the human. Our annotated dataset has higher consistency compared to other similar datasets. We establish several baselines for the bilingual embedding task to benchmark the experiments. Modeling cross-lingual sense representations as provided in this dataset has the potential of moving artificial intelligence from monolingual understanding towards multilingual understanding.
Cheers to Untappd! Preferences for Beer Reflect Cultural Differences Around the World

Common methods to collect data to perform cross-cultural studies, such as interviews or surveys, can be costly, thus, not scaling easily. Recently, studies have found evidence that data collected from location-based social networks (LBSNs) have the potential to revolutionize the study of urban societies. In this study, we investigate if preferences for beers shared in Untappd, an LBSN specialized in beer, reflect cultural differences. We explore an approach for identifying cultural similarities between groups of users considering preferences for beers. Using a large dataset of several cities in different countries worldwide, we found evidence that preferences for beers shared in Untappd reflect cultural differences that could be used to extract similarities between cultures, which could enable new services and applications.
Eco-Feedback Interventions: Selective Attention and Actual Behavior Change

The right information can foster behavior change towards energy conservation – given that the receiving individuals pay attention to the feedback provided and integrate the information in their decision making. As human information processing capabilities are limited, intervention designers need to choose content carefully to avoid information overload. Based on energy consumption and survey data collected in field trial, this article investigates the attention paid to different elements of the user interface (N=426) and establishes a relationship to actual energy conservation. We find that self-reported attention paid to content explains only very little of the measured behavior change (explanatory power of approx. 3%). The article is a first step towards better understanding the black box of feedback interventions in the energy sector. The results highlight the importance of collecting real-world data on behavior – rather than relying on selfreported user perceptions – in the resource consumption context and beyond.
Improved Techniques for GAN based Facial Inpainting

In this paper we present several architectural and optimization recipes for generative adversarial network(GAN) based facial semantic inpainting. Current benchmark models are susceptible to initial solutions of non-convex optimization criterion of GAN based inpainting. We present an end-to-end trainable parametric network to deterministically start from good initial solutions leading to more photo realistic reconstructions with significant optimization speed up. For the first time, we show how to efficiently extend GAN based single image inpainter models to sequences by a)learning to initialize a temporal window of solutions with a recurrent neural network and b)imposing a temporal smoothness loss(during iterative optimization) to respect the redundancy in temporal dimension of a sequence. We conduct comprehensive empirical evaluations on CelebA images and pseudo sequences followed by real life videos of VidTIMIT dataset. The proposed method significantly outperforms current GAN based state-of-the-art in terms of reconstruction quality with a simultaneous speedup of over 15$\times$. We also show that our proposed model is better in preserving facial identity in a sequence even without explicitly using any face recognition module during training.
Deep Learning with the Random Neural Network and its Applications

The random neural network (RNN) is a mathematical model for an "integrate and fire" spiking network that closely resembles the stochastic behaviour of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.
Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome’s type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net). It follows three stages. The first stage is to learn both global and local features. We extract global features and detect finer local regions via the G-Net. By proposing a varifocal mechanism, we zoom into local parts and extract local features via the L-Net. Residual learning and multi-task learning strategies are utilized to promote high-level feature extraction. The detection of discriminative local parts is fulfilled by a localization subnet of the G-Net, whose training process involves both supervised and weakly supervised learning. The second stage is to build two multi-layer perceptron classifiers that exploit features of both two scales to boost classification performance. The third stage is to introduce a dispatch strategy of assigning each chromosome to a type within each patient case, by utilizing the domain knowledge of karyotyping. The evaluation results from 1909 karyotyping cases showed that the proposed Varifocal-Net achieved the highest accuracy per patient case (%) of 99.2 for both type and polarity tasks. It outperformed state-of-the-art methods, demonstrating the effectiveness of our varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The proposed method has been applied to assist practical karyotype diagnosis.
Geometry-Contrastive GAN for Facial Expression Transfer.

In this paper, we propose a Geometry-Contrastive Generative Adversarial Network (GC-GAN) for transferring continuous emotions across different subjects. Given an input face with certain emotion and a target facial expression from another subject, GC-GAN can generate an identity-preserving face with the target expression. Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions. In order to handle the misalignment across different subjects or emotions, contrastive learning is used to transform geometry manifold into an embedded semantic manifold of facial expressions. Therefore, the embedded geometry is injected into the latent space of GANs and control the emotion generation effectively. Experimental results demonstrate that our proposed method can be applied in facial expression transfer even there exist big differences in facial shapes and expressions between different subjects.