Miracle-dz/UniFAR-FTUs huggingface dataset:
Driver Emotion Recognition for Intelligent Vehicles

Driving can occupy a large portion of daily life and often can elicit negative emotional states like anger or stress, which can significantly impact road safety and long-term human health. In recent decades, the arrival of new tools to help recognize human affect has inspired increasing interest in how to develop emotion-aware systems for cars. To help researchers make needed advances in this area, this article provides a comprehensive literature survey of work addressing the problem of human emotion recognition in an automotive context. We systematically review the literature back to 2002 and identify 63 peer-review published articles on this topic. We overview each study’s methodology to measure and recognize emotions in the context of driving. Across the literature, we find a strong preference toward studying emotional states associated with high arousal and negative valence, monitoring the different states with cardiac, electrodermal activity, and speech signals, and using supervised machine learning to automatically infer the underlying human affective states. This article summarizes the existing work together with publicly available resources (e.g., datasets and tools) to help new researchers get started in this field. We also identify new research opportunities to help advance progress for improving driver emotion recognition.
The living landscape

Following your need to always fulfil the inspiration to obtain everybody is now simple. Connecting to the internet is one of the short cuts to do. There are so many sources that offer and connect us to other world condition. As one of the products to see in internet, this website becomes a very available place to look for countless living landscape sources. Yeah, sources about the books from countries in the world are provided.
Neural Serendipity Recommendation

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity, which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.
Concurrent Immune Suppression and Hyperinflammation in Patients With Community-Acquired Pneumonia

Background The nature and timing of the host immune response during infections remain uncertain and most knowledge is derived from critically ill sepsis patients. We aimed to test the hypothesis that community-acquired pneumonia (CAP) is associated with concurrent immune suppression and systemic inflammation. Methods Blood was collected from 79 CAP patients within 24 h after hospitalization and 1 month after discharge; 42 age- and sex-matched subjects without acute infection served as controls. Blood leukocytes were stimulated with lipopolysaccharide (LPS) or Klebsiella pneumoniae, and cytokines were measured in supernatants. Fifteen plasma biomarkers reflective of key host response pathways were compared between CAP patients with the strongest immune suppression (lowest 25% blood leukocyte tumor necrosis factor (TNF)-α production in response to LPS) and those with the least immune suppression (highest 25% of LPS-induced TNF-α production). Results Blood leukocytes of CAP patients (relative to control subjects) showed a reduced capacity to release TNF-α, interleukin (IL)-1β, IL-6 and IL-10 upon stimulation with LPS or K. pneumoniae, with a concurrently enhanced ability to release the anti-inflammatory mediator IL-1 receptor antagonist, irrespective of the presence of sepsis (18.9% of cases). Low (relative to high) TNF-α producers displayed higher plasma levels of biomarkers reflecting systemic inflammation, neutrophil degranulation, endothelial cell activation, a disturbed vascular barrier function and coagulation activation. Conclusion CAP replicates a common feature of immune suppression in sepsis. The coexistence of immune suppression and hyperinflammation in CAP argues against the theory of two distinct phases during the host response to sepsis.
After the Honeymoon: Neural and Genetic Correlates of Romantic Love in Newlywed Marriages

In Western culture, romantic love is commonly a basis for marriage. Although it is associated with relationship satisfaction, stability, and individual well-being, many couples experience declines in romantic love. In newlyweds, specifically, changes in love predict marital outcomes. However, the biological mechanisms underlying the critical transition to marriage are unknown. Thus, for the first time, we explored the neural and genetic correlates of romantic love in newlyweds. Nineteen first-time newlyweds were scanned (with functional MRI) while viewing face images of the partner versus a familiar acquaintance, around the time of the wedding (T1) and 1 year after (T2). They also provided saliva samples for genetic analysis (AVPR1a rs3, OXTR rs53576, COMT rs4680, and DRD4-7R), and completed self-report measures of relationship quality including the Eros (romantic love) scale. We hypothesized that romantic love is a developed form of the mammalian drive to find, and keep, preferred mates; and that its maintenance is orchestrated by the brain’s reward system. Results showed that, at both time points, romantic love maintenance (Eros difference score: T2-T1) was associated with activation of the dopamine-rich substantia nigra in response to face images of the partner. Interactions with vasopressin, oxytocin, and dopamine genes implicated in pair-bonding (AVPR1a rs3, OXTR rs53576, COMT rs4680, and DRD4-7R) also conferred strong activation in the dopamine-rich ventral tegmental area at both time points. Consistent with work highlighting the role of sexual intimacy in relationships, romantic love maintenance showed correlations in the paracentral lobule (genital region) and cortical areas involved in sensory and cognitive processing (occipital, angular gyrus, insular cortex). These findings suggest that romantic love, and its maintenance, are orchestrated by dopamine-, vasopressin- and oxytocin-rich brain regions, as seen in humans and other monogamous animals. We also provide genetic evidence of polymorphisms associated with oxytocin, vasopressin and dopamine function that affect the propensity to sustain romantic love in early stage marriages. We conclude that romantic love maintenance is part of a broad mammalian strategy for reproduction and long-term attachment that is influenced by basic reward circuitry, complex cognitive processes, and genetic factors.
Third-Generation Sequencing Reveals LncRNA-Regulated HSP Genes in the Populus x canadensis Moench Heat Stress Response

Long non-coding RNAs (lncRNAs) regulate plant responses to abiotic stresses. However, the short reads produced by second-generation sequencing technology make it difficult to accurately explore full-length transcripts, limiting the study of lncRNAs. In this study, we used third-generation long-read sequencing technology with the PacBio Sequel and Illumina platform to explore the role of lncRNAs in the heat stress response of Populus x canadensis Moench trees. We using 382,034,416 short reads to correct 4,297,179 long reads by resulted in 66,657 full-length transcripts, representing 33,840 genes. Then, 753 putative lncRNAs were identified, including 658 sense lncRNAs (87.38%), 41 long intervening/intergenic non-coding RNAs (lincRNAs) (5.44%), 12 antisense lncRNAs (1.59%), and 42 sense intronic lncRNAs (5.58%). Using the criteria | log2FC| ≥ 1 and q-value < 0.05, 3,493 genes and 78 lncRNAs were differentially expressed under the heat treatment. Furthermore, 923 genes were detected as targets of 43 differently expressed lncRNAs by cis regulation. Functional annotation demonstrated that these target genes were related to unfolded protein binding, response to stress, protein folding, and response to stimulus. Lastly, we identified a lncRNA–gene interaction network consisting of four lncRNAs and six genes [Heat Shock Protein 82 (HSP82), HSP83, Disease Resistance Protein 27 (DRL27), DnaJ family protein (DNJH), and two other predicted protein-coding genes], which showed that lncRNAs could regulate HSP family genes in response to heat stress in Populus. Therefore, our third-generation sequencing has improved the description of the P. canadensis transcriptome. The potential lncRNAs and HSP family genes identified here present a genetic resource to improve our understanding of the heat-adaptation mechanisms of trees.
Cysteine Depletion, a Key Action to Challenge Cancer Cells to Ferroptotic Cell Death

Cancer cells are characterized as highly proliferative at the expense of enhancement of metabolic rate. Consequently, cancer cells rely on antioxidant defenses to overcome the associated increased production of reactive oxygen species (ROS). The reliance of tumor metabolism on amino acids, especially amino acid transport systems, has been extensively studied over the past decade. Although cysteine is the least abundant amino acid in the cell, evidences described it as one of the most important amino acid for cell survival and growth. Regarding its multi-functionality as a nutrient, protein folding, and major component for redox balance due to its involvement in glutathione synthesis, disruption of cysteine homeostasis appears to be promising strategy for induction of cancer cell death. Ten years ago, ferroptosis, a new form of non-apoptotic cell death, has been described as a result of cysteine insufficiency leading to a collapse of intracellular glutathione level. In the present review, we summarized the metabolic networks involving the amino acid cysteine in cancer and ferroptosis and we focused on describing the recently discovered glutathione-independent pathway, a potential player in cancer ferroptosis resistance. Then, we discuss the implication of cysteine as key player in ferroptosis as a precursor for glutathione first, but also as metabolic precursor in glutathione-independent ferroptosis axis.
Write Back Energy Optimization for STT-MRAM-based Last-level Cache with Data Pattern Characterization

Traditional memory technologies face severe challenges in meeting the ever-increasing power and memory bandwidth requirements for high-performance computing and big-data analyses. Several emerging memory technologies are promising as the replacements of SRAM or DRAM. Among them, STT-MRAM can be used to replace SRAM as the last-level cache (LLC). However, it suffers from high write energy and latency. In this article, we investigate data patterns written from SRAM-based upper-level cache to STT-MRAM-based LLC to explore the write energy reduction potential. Depending on the data layout within a cache line, redundant bits can be identified and eliminated from write back operations to save STT-MRAM write energy. We also propose a dynamic profiling method to accommodate different application characteristics. The extensive simulation results show that write energy can be saved by 37.05% ∼ 38.89% for static profiling and 19.76% ∼ 34.29% for dynamic profiling.
Multi-View Graph Matching for 3D Model Retrieval

3D model retrieval has been widely utilized in numerous domains, such as computer-aided design, digital entertainment, and virtual reality. Recently, many graph-based methods have been proposed to address this task by using multi-view information of 3D models. However, these methods are always constrained by many-to-many graph matching for the similarity measure between pairwise models. In this article, we propose a multi-view graph matching method (MVGM) for 3D model retrieval. The proposed method can decompose the complicated multi-view graph-based similarity measure into multiple single-view graph-based similarity measures and fusion. First, we present the method for single-view graph generation, and we further propose the novel method for the similarity measure in a single-view graph by leveraging both node-wise context and model-wise context. Then, we propose multi-view fusion with diffusion, which can collaboratively integrate multiple single-view similarities w.r. t. different viewpoints and adaptively learn their weights, to compute the multi-view similarity between pairwise models. In this way, the proposed method can avoid the difficulty in the definition and computation of the traditional high-order graph. Moreover, this method is unsupervised and does not require a large-scale 3D dataset for model learning. We conduct evaluations on four popular and challenging datasets. The extensive experiments demonstrate the superiority and effectiveness of the proposed method compared against the state of the art. In particular, this unsupervised method can achieve competitive performances against the most recent supervised and deep learning method.
A Survey of Multitier Programming

Multitier programming deals with developing the components that pertain to different tiers in the system (e.g., client and server), mixing them in the same compilation unit. In this paradigm, the code for different tiers is then either generated at run time or it results from the compiler splitting the codebase into components that belong to different tiers based on user annotations, static analysis, types, or a combination of these. In the Web context, multitier languages aim at reducing the distinction between client and server code, by translating the code that is to be executed on the clients to JavaScript or by executing JavaScript on the server, too. Ultimately, the goal of the multitier approach is to improve program comprehension, simplify maintenance and enable formal reasoning about the properties of the whole distributed application. A number of multitier research languages have been proposed over the last decade, which support various degrees of multitier programming and explore different design tradeoffs. In this article, we provide an overview of the existing solutions, discuss their positioning in the design space, and outline open research problems.
Shelfie: A Framework for Designing Material Representations of Physical Activity Data

Self-monitoring devices are becoming increasingly popular in the support of physical activity experiences. These devices mostly represent on-screen data using numbers and graphs and in doing so, they may miss multi-sensorial methods for engaging with data. Embracing the opportunity for pleasurable interactions with one’s own data through the use of different materials and digital fabrication technology, we designed and studied three systems that turn this data into 3D-printed plastic artifacts, sports drinks, and 3D-printed chocolate treats. We utilize the insights gained from associated studies, related literature, and our experiences in designing these systems to develop a conceptual framework, “Shelfie. ” The “Shelfie” framework has 13 cards that convey key themes for creating material representations of physical activity data. Through this framework, we present a conceptual understanding of relationships between material representation and physical activity data and contribute guidelines to the design of meaningful material representations of physical activity data.
Index of Predation on Juvenile Salmonids by Northern Pikeminnow in the Lower Columbia River Basin, 1994–1996

Abstract We estimated relative abundance of northern pikeminnow Ptychocheilus oregonensis and relative consumption of juvenile salmonids Oncorhynchus spp. by northern pikeminnow at standardized sites in the lower Columbia and lower Snake rivers from 1994 to 1996. Indexes of abundance and consumption were compared with indexes measured from 1990 to 1993 to evaluate changes in predation concurrent with a predator control program in the lower Columbia basin. Reductions in indexes of northern pikeminnow abundance, consumption, or both resulted in mean 1994–1996 predation index values that were 44–91% lower than mean 1990–1993 values throughout the lower Columbia basin. Consumption of juvenile salmonids by surviving northern pikeminnow has not increased in response to predator control efforts. Spatial patterns were consistent among years, being greatest downstream from Bonneville Dam, intermediate in lower Columbia River reservoirs, and lowest at Snake River sites. Reductions in relative predation were consist.
Towards Effective Interface Designs for Collaborative HRI in Manufacturing

We present a comprehensive framework and test methodology for the evaluation of human-machine interfaces (HMI) and human-robot interactions (HRI) in collaborative manufacturing applications. An overview of the challenges that face current- and next-generation collaborative robot systems is presented, specifically focused on the interactions between man and machine, and a series of objectively quantitative and subjectively qualitative metrics are given to guide the development and assessment of interfaces and interactions. A generalized set of guidelines for the design of HMI is also proposed to address these challenges and thereby enable effective and intuitive diagnostics and error corrections when process failures occur. These guidelines are aimed at aiding researchers in developing effective interface and interaction technologies, maximizing operator situation awareness in human-robot collaborative manufacturing teams, promoting effective process and system diagnostics reporting, and enabling faster responses to equipment or application errors.
Elizabethkingia anophelis: Physiologic and Transcriptomic Responses to Iron Stress

In this study, we investigated the global gene expression responses of Elizabethkingia anophelis to iron fluxes in the midgut of female Anopheles stephensi mosquitoes fed sucrose or blood, and in iron-poor or iron-rich culture conditions. Of 3,686 transcripts revealed by RNAseq technology, 218 were upregulated while 112 were down-regulated under iron-poor conditions. Hemolysin gene expression was significantly repressed when cells were grown under iron-rich or high temperature (37°C) conditions. Furthermore, hemolysin gene expression was down-regulated after a blood meal, indicating that E. anophelis cells responded to excess iron and its associated physiological stress by limiting iron loading. By contrast, genes encoding respiratory chain proteins were up-regulated under iron-rich conditions, allowing these iron-containing proteins to chelate intracellular free iron. In vivo studies showed that growth of E. anophelis cells increased 3-fold in blood-fed mosquitoes over those in sucrose-fed ones. Deletion of siderophore synthesis genes led to impaired cell growth in both iron-rich and iron-poor media. Mutants showed more susceptibility to H2O2 toxicity and less biofilm formation than did wild-type cells. Mosquitoes with E. anophelis experimentally colonized in their guts produced more eggs than did those treated with erythromycin or left unmanipulated, as controls. Results reveal that E. anophelis bacteria respond to varying iron concentration in the mosquito gut, harvest iron while fending off iron-associated stress, contribute to lysis of red blood cells, and positively influence mosquito host fecundity.
Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures

Epilepsy is a chronic medical condition that involves abnormal brain activity causing patients to lose control of awareness or motor activity. As a result, detection of pre-ictal states, before the onset of a seizure, can be lifesaving. The problem is challenging because it is difficult to discern between electroencephalogram signals in pre-ictal states versus signals in normal inter-ictal states. There are three key challenges that have not been addressed previously: (1) the inconsistent performance of prediction models across patients, (2) the lack of perfect prediction to protect patients from any episode, and (3) the limited amount of pre-ictal labeled data for advancing machine learning methods. This article addresses these limitations through a novel approach that uses adversarial examples with optimized tuning of a combined convolutional neural network and gated recurrent unit. Compared to the state of the art, the results showed an improvement of 3x in model robustness as measured in reduced variations and superior accuracy of the area under the curve, with an average increase of 6.7%. The proposed method also exhibited superior performance with other advances in the field of machine learning and customized for epilepsy prediction including data augmentation with Gaussian noise and multitask learning.
Trust Mechanism of Feedback Trust Weight in Multimedia Network

It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model is constructed based on the collaborative filtering algorithm and fuzzy network theory to calculate the node trust value as the weight of weighted data fusion. First, a FTWDF (Feedback Trust Weighted for Data Fusion) is proposed. Second, EEFA (Efficiency unequal Fuzzy clustering Algorithm) is introduced into FTWDF considering the defects of the clustering structure caused by ignoring the randomness of node energy consumption and cluster head selection in the practical application of the existing data fusion algorithm. Besides, the fuzzy logic is applied to cluster head selection and node clustering. Finally, an FTWDF-EEFA clustering algorithm is constructed for generating candidate cluster head nodes, which is verified by simulation experiments. The comparative analysis reveals that the accuracy of the FTWDF-EEFA clustering algorithm is 4.1% higher than that of the TMDF (Trust Multiple attributes Decision-making-based data Fusion) algorithm, and 8.3% higher than that of LDTS (Larger Data fusion based on node Trust evaluation in wireless Sensor networks) algorithm. It performs better in accuracy and recommendation results during the processing of ML100M dataset and NF5M dataset. Besides, the new clustering algorithm increases the survival time of nodes when analyzing the number of death nodes to prolong networks’ lifespan. It improves the survival period of nodes, balances the network load, and prolongs networks’ lifespan. Furthermore, the FTWDF-EEFA clustering algorithm can balance nodes’ energy consumption and effectively save nodes’ overall energy through analysis. Therefore, the optimized algorithm can increase the lifespan of network and improve the trust mechanism effectively. The performance of the algorithm has reached the expected effect, providing a reference for the practical application of the trust mechanism in networks.