Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Aging and Time of Day on Inhibitory Control: An Event-Related Potential Study

Time of day (TOD) influences on executive functions have been widely reported, with greater efficiency demonstrated at optimal relative to non-optimal TOD according to one’s chronotype (i.e., synchrony effect). Older adults (OAs) show declines in inhibitory control and are more sensitive to the effects of circadian variation on executive functioning. To date, no studies have investigated the effects of TOD and aging on executive functioning using electrophysiological measures. The present study investigated the effects of aging and TOD on the neural correlates of inhibitory processing (N2 and P3) using event-related potentials (ERPs). Go-NoGo and Flanker tasks were administered to 52 OAs of morning chronotype and 51 younger adults (YAs) of afternoon-to-evening chronotype who were randomly assigned to morning or afternoon test sessions, with the optimal TOD for OAs in the morning and for YAs in the afternoon/evening. While behavioral results demonstrated no TOD effects, ERPs indicated synchrony effects. Both YAs and OAs showed greater modulation of Go-NoGo N2 and greater P3 amplitude during the non-optimal than optimal TOD, consistent with the synchrony effect. For the Flanker task, age differences in P3 amplitude were only apparent during the non-optimal TOD. These results suggest that processes associated with inhibitory control are differentially affected by TOD and aging, with age-related reductions in inhibitory efficiency during off-peak test times on measures of interference control. These findings highlight the sensitivity of ERPs to detect TOD effects in the absence of behavioral differences, confirm more pronounced TOD effects in OAs relative to YAs on ERP measures of interference control, and reinforce the need to assess and control for circadian typology in research studies.
Identification of Key microRNAs and Genes in Infantile Hemangiomas

Infantile hemangiomas (IHs) are the most frequent vascular tumors that occur during infancy. Microribonucleic acids (miRNAs) have been demonstrated as critical regulators of gene expression in various diseases. However, the function of miRNAs in IH still remains largely unknown. In the present study, we performed a miRNA microarray analysis of IH and identified 68 differentially expressed miRNAs (DEMs). In addition, miRNA-gene networks and protein-protein interactions were constructed, and the hub miRNAs and genes of IH were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used for biological analysis of DEMs and differentially expressed genes (DEGs). The pathway enrichment analysis of DEMs revealed several tumor-related pathways, including proteoglycans in cancer, signaling pathway regulating pluripotency of stem cells and TGF-beta signaling pathway. DEGs were mainly enriched in biological processes, including intracellular signal transduction, cell adhesion, and cell death. KEGG pathway analysis indicated that DEGs were enriched in tumorigenesis- and angiogenesis-related pathways such as proteoglycans in cancer, MAPK signaling pathway and Rap1 signaling pathway. Collectively, this study first established a comprehensive miRNA-gene network in IH, which should provide novel insights into IH pathogenesis and be beneficial to the understanding of neovascularization-related disorders.
Co-Animation in Troubles-Talk

ABSTRACT Through troubles-talk, participants disclose and negatively assess unfortunate past or habitual happenings and offenses, and these are often vividly staged in the here and now by temporarily “doing being” past Self or others, what we call animation. In this study, we show how by animating their own affective reactions toward the recounted experiences, tellers cast themselves as victims and their recipients as witnesses. More importantly, we demonstrate how animation is also a relevant practice for recipients, who in a contiguous position often offer a responsive co-animation of the same figure, validating and amplifying their teller’s affective displays, turning the experience into a common cause. This study contributes further to our understanding of empathic responses in troubles-talk in English by inscribing co-animation as a previously undescribed alternative at the response slot that allows recipients to temporarily position themselves “into the moment” as co-victims, co-experiencers, and co-sanctioners. Data are in English.
Understanding Creative Thinking Processes in Searching for New Ideas

Creative thinking processes involve a set of cognitive operations and behaviors people engage to generate new ideas and solutions. Information searching is an integral part of the creative thinking process. The purpose of this study is to discover the creative thinking processes and search strategies students use to generate new ideas in academic and everyday life information use contexts. The data were collected through a pre-task interview, a search session for a creative task, and a post-task interview. Fifteen graduate students at a research university participated in the study. From the data analysis, we identified four distinctive, yet interconnected creative thinking processes: planning for creative search tasks, searching for new ideas, synthesizing search results, and organizing ideas. Based on these findings, we point out the need to design search systems to support creative thinking processes during searching to facilitate generation of new ideas.
Turn and Face the Strange: Investigating Filter Bubble Bursting Information Interactions

It is a ‘truth universally acknowledged’ that people prefer to minimize encounters with information they disagree with and ignore it where they find it. Algorithms purportedly support this avoidance by creating filter bubbles filled only with agreeable information potentially increasing polarisation and undermining democracy. How accurate is this portrayal, though? Recent research has begun to cast doubt. We challenge these assumptions and report a two-phase analysis of filter bubble-bursting behavior. The first phase reports novel incidental findings from an interview study on the role of information interaction in view change. Participants demonstrated a clear interest in a diversity of information, including information specifically opposed to their own views. The second phase reports findings from a diary study specifically designed to investigate people's interactions with information that reflected a different view to theirs. We examine how people found disagreeable information, how they responded to it and the factors affecting their responses. We find that people will sometimes actively seek and engage with disagreeable information, rather than avoid and ignore it. Our findings pave the way for future information interfaces that support this previously undiscussed information interaction.
The Influence of Data Storytelling on the Ability to Recall Information

With ever-increasing amounts of complex data, we need compelling ways to distill this information into meaningful, memorable and engaging insights. Data storytelling is an emerging visualization paradigm that aims to “tell a story” with data in order to elicit deeper reflections in an effective manner. However, the effects of adding a narrative to a visualization on the memorability of the information remain speculative. Based on a review of related work, we synthesize a framework of data storytelling principles with concrete actions for every principle. We use this framework to design an online, controlled experiment to test compare traditional data visualizations with data storytelling visualizations in terms of their effects on short-term and long-term recall of information displayed in the visualizations. In general, despite long-held assumptions in the visualization community, we find no significant differences in recall between traditional visualizations and data storytelling visualization. However, we find indications that the cognitive load induced by different chart types and self-assessed prior knowledge on the chart topics could possibly have a moderating effect on information recall.
Have a Clue! The Effect of Visual Cues on Children’s Search Behavior in the Classroom

We study the effect of visual cues on children searching in the classroom. We do so by examining whether Search Engine Result Pages (SERP) enhanced with emojis, unlike standard SERP, affect children’s search behaviour. To capture search behaviour, we use well-known metrics, in addition to users’ success in identifying relevant results on SERP. Outcomes from our work reveal that a one-size-fits-all approach for SERP does not befit students who are searching for learning. Thus, we discuss the implications of our findings and suggest directions for future research, focused on the design and evaluation of information retrieval systems that can better support the classroom setting.
Double Atention based on Graph Atention Network for Image Multi-Label Classification

The task of image multi-label classiication is to accurately recognize multiple objects in an input image. Most of the recent works need to leverage the label co-occurrence matrix counted from training data to construct the graph structure, which are inlexible and may degrade model generalizability. In addition, these methods fail to capture the semantic correlation between the channel feature maps to further improve the model performance. To address these issues, we propose a D ouble A ttention framework based on G raph A ttention ne T work (DA-GAT) to efectively learn the correlation between labels from training data. Firstly, we devise a new channel attention mechanism to enhance the semantic correlation between channel feature maps, so as to implicitly capture the correlation between labels. Secondly, we propose a new label attention mechanism to avoid the adverse impact of manually constructed label co-occurrence matrix. It only needs to leverage the label embedding as the input of network, and then automatically constructs the label relation matrix to explicitly establish the correlation between labels. Finally, we efectively fuse the output of these two attention mechanisms to further improve the model performance. Extensive experiments are conducted on three public multi-label classiication benchmarks. Our DA-GAT model achieves the mAPs of 87.1%, 96.6%, and 64.3% on MS-COCO 2014, PASCAL VOC 2007, and NUS-WIDE respectively, and obviously outperforms other existing state-of-the-art methods. In addition, visual analysis experiments demonstrate that each attention mechanism can capture the correlation between labels well, and signiicantly promote the model performance.
Situating Search

Search systems, like many other applications of machine learning, have become increasingly complex and opaque. The notions of relevance, usefulness, and trustworthiness with respect to information were already overloaded and often difficult to articulate, study, or implement. Newly surfaced proposals that aim to use large language models to generate relevant information for a user’s needs pose even greater threat to transparency, provenance, and user interactions in a search system. In this perspective paper we revisit the problem of search in the larger context of information seeking and argue that removing or reducing interactions in an effort to retrieve presumably more relevant information can be detrimental to many fundamental aspects of search, including information verification, information literacy, and serendipity. In addition to providing suggestions for counteracting some of the potential problems posed by such models, we present a vision for search systems that are intelligent and effective, while also providing greater transparency and accountability.
Design of a 400 MHz Cavity Backed CPW Fed Bow-Tie Antenna for GPR Applications

This study presents the design a 400 MHz CPW-fed bow-tie slot antenna backed by a cavity. The application of UWB signal sources in ground penetrating radar (GPR) is widely recognised. The antennas are first modelled and simulated, and then tested using a VNA. The antenna has a high gain and very good (F/B) ratio. The antenna exhibits resonance at 401 MHz and 421 MHz and has a measured bandwidth of 47.5%. It has a realised gain of 6.68 dB at 400 MHz.
A Survey of Secure Boot Schemes for Embedded Devices

With the rapid development of Internet of things and wireless communication technology, embedded devices are widely used in every aspect of our daily lives. Due to the lack of built-in security mechanism, the embedded devices are facing more and more threats from the malicious code attack, DDoS attack and so on. Secure boot technology can assure that the device boots up with an untampered and authorized software provided by a legitimate vendor and becomes a critical step in the device firmware and software security at the booting time. Aiming at designing and implementing the secure booting schemes, researchers have presented many effective approaches. This paper summarizes existing secure boot schemes for embedded devices and also compares and analyses the advantage and disadvantages of these existing schemes.
COVID-19 Diagnosis Using VGG-16 with CT Scans

Artificial intelligence techniques known as deep learning are a collection of approaches that use interconnected networks to capture higher levels of abstraction in scene data. Analysis of vast amounts of data helps it learn abstraction. Imaging techniques such as x-ray, ultrasound, CT, MRI, and fluoroscopy are used in the medical field of radiology to diagnose and treat disease. Computer performance and networking advancements have made it possible for this field to benefit from the use of deep neural networks. When it comes to lung disease, the most common problem is that the symptoms aren't obvious to patients until they've progressed significantly. To prevent the spread of this disease, it is necessary to educate the public about its symptoms. Artificial intelligence approaches known as “deep learning” use a network of interconnected nodes to extract higher levels of abstraction from raw data such as images and video. Analysis of vast amounts of data helps it learn abstraction. Imaging techniques such as x-ray, ultrasound, CT, MRI, and fluoroscopy are used in the medical field of radiology to diagnose and treat disease. Due to advances in computer performance and networking, deep neural networks are now possible in this field. More than 1.3 million people have been infected by COVID-19 (coronavirus disease 2019) and more than 106,000 people have died. One of the biggest obstacles to containing the spread of this disease is the lack of effective and affordable medical diagnostics. There is a growing interest in developing deep learning methods for detecting COVID-19 from CT scans. These efforts, however, are difficult to replicate and adapt because the CT data used in their investigations is not publicly available. ” A large number of CT scans are required for many projects to train appropriate diagnosis models, which are difficult to obtain.
The influence of learning on robustness of working memory to distractors

Inhibition of irrelevant information is a crucial process within the working memory (WM) system. In the present study, we explored how inhibition of distractors is learned within a Sternberg-like WM task and how this learning process affects readout performance. We introduced distractors of varying strength as well as novel stimuli to a classic Sternberg task. Our findings show that the length of the training period affected obstructiveness of distractors as well as differences between WM loads. Significant differences between conditions observed early on in accuracy became void after general ceiling performance was reached. Moreover, accuracy and reaction times, which are commonly used as readouts of WM performance, were affected differently by training. While both improved significantly, only accuracy exhibited differential dependency of experimental conditions on training. It is essential to consider these dynamics when introducing a WM task which requires a long training period.
Organ- and Function-Specific Anatomical Organization and Bioelectronic Modulation of the Vagus Nerve

Afferent and efferent fibers in the vagus nerve travel inside fascicles and form branches to innervate organs and regulate organ functions. The spatial organization of fibers and fascicles, with respect to the functions they mediate and the organs they innervate, is unknown. Accordingly, it is unknown whether such organization can be leveraged by bioelectronic devices for function- and organ-specific neurostimulation. To characterize the functional microscopic anatomy of the vagus, we developed a pipeline consisting of micro-computed tomography imaging of fascicles and quantitative immunohistochemistry of single fibers. We found that, in swine, the vagus contains clusters of fascicles, organized along 2 overlapping functional axes: one axis specific to sensory vs. motor somatic and visceral functions, with clusters increasingly separating in the cephalad direction, and a second axis specific to innervated organs, including larynx, lungs and heart, with increasing separation in the caudad direction. Large myelinated afferents and efferents, and small unmyelinated efferents occupy large areas of specific clusters of fascicles; small unmyelinated afferents are numerous and occupy small areas across every fascicle. To test whether fiber populations can be selectively modulated, we developed a multi-contact cuff electrode accommodating the nerve fascicular structure. Stimulation targeting separate nerve sections evokes compound action potentials from fibers of distinct functional types and elicits differential organ responses, including laryngeal muscle contraction, cough reflex, and changes in breathing and heart rate. Our results indicate that vagus fibers are anatomically organized according to functions they mediate and organs they innervate and can be differentially modulated by spatially selective nerve stimulation. Many mixed BP-RLN-cardiac fascicles Fascicular branching, where one fascicle branches to form two fascicles, or fascicular merging, where two fascicles merged to form one fascicle is observed throughout the length of the nerve (Suppl. Figure S5, and Supplementary video 1). These findings indicate that fascicles in the thoracic and cervical vagus trunk form spatially distinct organ-specific clusters close to the levels of entry of branches to those organs; clusters progressively merge into larger mixed clusters in the rostral direction. EMG slow-fiber-mediated
Helix 8 in chemotactic receptors of the complement system

Host response to infection involves activation of the complement system leading to producing of anaphylotoxins C3a and C5a. A complement factor C5a exerts its effect through activation of C5aR1, chemotactic receptor 1, and triggers the G protein-coupled signaling cascade. Orthosteric and allosteric antagonists of C5aR1 are a novel strategy for anti-inflammatory therapies. Here, we discuss recent crystal structures of inactive C5aR1 in terms of an inverted orientation of helix H8, unobserved in other GPCR structures. Analysis of mutual interactions of subunits in the C5aR1 - G protein complex has provided new insights into the activation mechanism of this distinct receptor. By comparison of C5aR1 and its homolog C5aR2 we explained differences between their signaling pathways on the molecular level. A comparison of microsecond MD trajectories started from active and inactive receptor conformations also enabled to elucidate details of local and global changes in the transmembrane domain induced by interactions with the Gα subunit and to explain the impact of inverted H8 on the receptor activation.
Machine Learning Models for Predicting and Clustering Customer Churn Based on Boosting Algorithms and Gaussian Mixture Model

Customer churn prediction is becoming increasingly important in the telecom sector, as churn is a significant driver of EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) margin. Processing of vast consumer data has made churn prediction and factor identification for customer retention a challenging task. The proposed work presents an effective Machine Learning (ML) approach that includes boosting algorithms to identify Customer Churn (CC) and Gaussian Mixture Model to cluster the churned customers. The proposed work also implements the Light Gradient Boosting (LightGB) model, which outperforms Adaboost and XGBoost for churn prediction by a factor of 15x. Further, the Gaussian Mixture Model (GMM) opted for clustering yielded a silhouette score of 0.36.
Bio-inspired neural networks implement different recurrent visual processing strategies than task-trained ones do

Behavioral studies suggest that recurrence in the visual system is important for processing degraded stimuli. There are two broad anatomical forms this recurrence can take, lateral or feedback, each with different assumed functions. Here we add four different kinds of recurrence—two of each anatomical form—to a feedforward convolutional neural network and find all forms capable of increasing the ability of the network to classify noisy digit images. Specifically, we take inspiration from findings in biology by adding predictive feedback and lateral surround suppression. To compare these forms of recurrence to anatomically-matched counterparts we also train feedback and lateral connections directly to classify degraded images. Counter-intuitively, we find that the anatomy of the recurrence is not related to its function: both forms of task-trained recurrence change neural activity and behavior similarly to each other and differently from their bio-inspired anatomical counterparts. By using several analysis tools frequently applied to neural data, we identified the distinct strategies used by the predictive versus task-trained networks. Specifically, predictive feedback de-noises the representation of noisy images at the first layer of the network and decreases its dimensionality, leading to an expected increase in classification performance. Surprisingly, in the task-trained networks, representations are not de-noised over time at the first layer (in fact, they become ‘noiser’ and dimensionality increases) yet these dynamics do lead to de-noising at later layers. The analyses used here can be applied to real neural recordings to identify the strategies at play in the brain. Our analysis of an fMRI dataset weakly supports the predictive feedback model but points to a need for higher-resolution cross-regional data to understand recurrent visual processing.
Earthquake Magnitude Prediction using Spatia-temporal Features Learning Based on Hybrid CNN- BiLSTM Model

Earthquakes are a very catastrophic natural event that occurs due to sudden changes in the earth's crust, leading to human, financial, and environmental losses in society. Therefore, employing an efficient and dependable method for earthquake prediction can significantly reduce casualties. In this regard, we proposed a deep neural network called the hybrid convolutional neural network and bi-directional long-short-term memory (HC-BiLSTM) to predict the mean magnitude of the future earthquake in a specific area of Japan. To achieve this goal, we suggest a strategy based on four key steps: the division of areas, the preprocessing, the spatial and temporal feature learning, and the prediction. In the division of areas step, The part of Japan is divided into 49 smaller areas to better predict the next earthquake's location. The preprocessing step uses the zero-order hold method in the time series of the mean magnitude of the earthquake. In the next step, the learning spatial and temporal characteristics between earthquake data include three layers of CNN and pooling and two layers of LSTM. Finally, the prediction step has two fully connected layers that combine information supplied by HC-BiLSTMs to predict the mean magnitude for the earthquake next month. As a result, using a comparative method, this study demonstrates the superiority of the proposed method over other common earthquake prediction methods.