Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry

Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have to a lesser extent been characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18–87 years. To compare the tissue- specific brain ages and their cognitive sensitivity we applied each of the 11 models in an independent and cognitively well-characterized sample (n=265, 20–88 years). Correlations between true and estimated age in our test sample were highest for the most comprehensive brain morphometry (r=0.83, CI:0.78–0.86) and white matter microstructure (r=0.79, CI:0.74–0.83) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.
Complete Genome Sequences of Historic Clostridioides difficile Food-Dwelling Ribotype 078 Strains in Canada Identical to That of the Historic Human Clinical Strain M120 in the United Kingdom

Clostridioides (Clostridium) difficile is a spore-forming anaerobic bacterium that causes severe intestinal diseases in humans. Here, we report the complete genome sequence of the first C. difficile foodborne type strain (PCR ribotype 078) isolated from food animals in Canada in 2004, which has 100% similarity to the genome sequence of the historic human clinical strain M120. ABSTRACT Clostridioides (Clostridium) difficile is a spore-forming anaerobic bacterium that causes severe intestinal diseases in humans. Here, we report the complete genome sequence of the first C. difficile foodborne type strain (PCR ribotype 078) isolated from food animals in Canada in 2004, which has 100% similarity to the genome sequence of the historic human clinical strain M120.
A Novel Text Classification Method for Emergency Event Detection on Social Media

Currently event detection based on social media becomes a more and more active research field. Text classification is a challenging task in event detection on social media because a large number of irrelevant texts are provided at the same time of collecting relevant texts. Most of existing text classification methods are based on the supervised machine learning which requires labeled and balanced training dataset. However, it is very difficult or even impossible for emergency event detection to collect balanced and labeled training data. In this paper, we propose a novel text classification method based on one-class Support Vector Machine (SVM) for emergency event detection, which takes only irrelevant texts as training data to detect the relevant texts to the emergency event. More importantly, we propose to extract variable length n-gram features for representing texts that takes the sequential relations and semantic relations among words into account. Finally we conduct two set of experiments to evaluate our method and experimental results show our method can perform text classification for detecting emergency event on social media with a comparable accuracy.
A Conceptual Framework of Narrative Persuasion

This article draws insights from several disciplines to propose an integrated perspective on mechanisms underlying narrative persuasion. One approach to narratives emphasizes a deictic shift into the narrative, resulting in an absorbed state of processing and a loss of one’s sense of self (e.g., transportation, narrative engagement, identification). Another approach focuses on processes to construct meaning from a narrative; that is, how narratives are actively compared with and applied to one’s life. The current work has conceptualized the relationship between these two broad processes as occurring in sequence, and as a pathway of narrative persuasion: A shift and absorption into the narrative leads to a process of reflecting on the narrative, which is antecedent to narrative influence.
IF-CNN: Image-Aware Inference Framework for CNN With the Collaboration of Mobile Devices and Cloud

Improving the performance of CNN-based mobile applications by offloading its computation from mobile devices to the cloud has attracted the attention of the community. Generally, there are three stages in the workflow, including local inference on the mobile device, data transmission of the intermediate result, and remote inference in the cloud. However, the time cost of local inference and data transmission are still the bottleneck in reaching the desirable inference performance. In this paper, we propose an image-aware inference framework called IF-CNN to enable fast inference based on computation offloading. In the framework, we first build a model pool consisting of CNN models with different complexities. The most efficient one from such candidate models is selected to process the corresponding image. During the selection process, we have designed an effective model to predict the confidence based on multi-task learning. After model selection, half-floating optimization and feature compression are applied to accelerate the process of distributed inference between mobile devices and cloud. Experimental results show that IF-CNN is credible to identify the most effective model for different images and the total inference performance could be significantly improved. Meanwhile, IF-CNN is complementary to other inference acceleration methods of CNN models.
Threshold-Guided Design and Optimization for Harris Corner Detector Architecture

High-speed corner detection is an essential step in many real-time computer vision applications, e.g., object recognition, motion analysis, and stereo matching. Hardware implementation of corner detection algorithms, such as the Harris corner detector (HCD) has become a viable solution for meeting real-time requirements of the applications. A major challenge lies in the design of power, energy and area efficient architectures that can be deployed in tightly constrained embedded systems while still meeting real-time requirements. In this paper, we proposed a bit-width optimization strategy for designing hardware-efficient HCD that exploits the thresholding step in the algorithm to determine interest points from the corner responses. The proposed strategy relies on the threshold as a guide to truncate the bit-widths of the operators at various stages of the HCD pipeline with only marginal loss of accuracy. Synthesis results based on 65-nm CMOS technology show that the proposed strategy leads to power-delay reduction of 35.2%, and area reduction of 35.4% over the baseline implementation. In addition, through careful retiming, the proposed implementation achieves over 2.2 times increase in maximum frequency while achieving an area reduction of 35.1% and power-delay reduction of 35.7% over the baseline implementation. Finally, we performed repeatability tests to show that the optimized HCD architecture achieves comparable accuracy with the baseline implementation (average decrease of repeatability is less than 0.6%).
An Optically Transparent Broadband Microwave Absorber Based on Resistive Frequency Selective Surface

An optically transparent broadband RFSS absorber, with wide incident angle range was demonstrated based on ITO films. Equivalent TLM and full-wave electromagnetic simulation were used to analyze and optimize the absorber. The experimental results agree well with calculated ones. Specifically, the bandwidths of absorptivity $\geq 80\%$ under incident angle 45° are over 9GHz for both TE and TM polarization, respectively.
Method for Practical Use of Parasitic Capacitances of Reference Current Sources in Dual-Input-Stages for Increasing the SR of Operational Amplifiers in Inverting Connection Circuits

An unconventional method for increasing the slew rate (SR) of CMOS and BJT operational amplifiers (OPAMP) is considered. In order to obtain a significant gain for the SR (by a factor of 10-100), the parasitic capacitances are used in the common source (emitter) circuit of its input differential stage (DS). The Dual-Input-Stage scheme is applied as a DS. It is shown that the proposed "paradoxical circuitry" works in inverting connections of the OPAMP.
Ant Colony Optimization Based Salient Object Detection for Weak Light Images

Since substantial attention has been received over last decade, salient object detection becomes a fundamental research in computer vision community and has tremendous potential to solve real world problems. Although various works have achieved remarkable success in saliency detection tasks, there still remains a challenging issue on how to process weak light images, mainly due to low signal to noise ratios and limited effective features. In this paper, an ant colony optimization (ACO) based framework is proposed to detect the salient object presented in weak light backgrounds. The input image is first represented as an undirected graph, the nodes of which are generated by superpixel segmentation. Then, the optimal feature selection strategy is adopted to capture the useful information and the spatial contrast information is introduced to explore the global saliency cues. To acquire more accurate saliency estimation, the ACO measure is conducted to optimize the saliency map. Experiments on three public datasets and our weak light dataset demonstrate that the proposed model has promising performance compared to 11 state-of-the-art saliency approaches.
An Optimized Ultrathin and Broadband Metamaterial Absorber Using Slotted Square Loop with Multi Layers

An optimized ultrathin and broadband absorber is proposed in this paper. It is composed of a periodic array of slotted square loop-dielectric multilayered structure. The bottom layer consists of one dielectric with edge-overlapped slotted square metal loop on it. The simulated results indicate that the absorption at normal incidence is above 90% in almost the whole frequency range of 6.2S-18GHz. Moreover, the designed absorber is ultrathin with a thickness of only 0.13-0.38 free-space wavelength at the lowest and highest frequencies, respectively.
Visual Analysis of Vertex-Disjoint Path Connectivity in Networks

The visualization of large graphs in interactive applications, specifically on small devices, can make harder to understand and analyze the displayed information. We show as simple topological properties of the graph can provide an efficient automatic computation of features which improves the "readibility" of a large graph by a proper selection of the displayed information. The connectivity (existence of a path) is a very intuitive structural property of a network; in this paper we propose an approach to the visualization of a network based on connectivity and related concepts as effective tools for visual analysis. In particular, given a root vertex r and a target vertex t, it is possible to check at a glance if there are some dominators, i.e., mandatory vertices that are on every path from r to t. Furthermore, using a recent graph algorithm from Georgiadis and Tarjan [19], [20], by selecting a target vertex it is possible to see two distinct paths from r to t: the paths are vertex-disjoints if there are no dominators from r to t, otherwise the paths have only the dominators in common. We conclude by presenting, as a relevant case study that motivated our work, as this approach improves a personalized eLearning application. In a framework, presented in [27], for dynamic configuration of paths of learning activities for both individual and group education, we can add visual analysis capabilities for both the final user/learner, and for the administrator of a repository.
Genome Sequence of Chiqui Virus, a Novel Reovirus Isolated from Mosquitoes Collected in Colombia

We report here the complete genome sequence of a novel reovirus, designated Chiqui virus (CHQV) strain CoB38d, that was isolated from a pool of unidentified mosquitoes collected in northern Colombia in 2013. CHQV has nine double-stranded DNA (dsRNA) genome segments and has similarity to viruses belonging to the family Reoviridae, subfamily Spinareovirinae. ABSTRACT We report here the complete genome sequence of a novel reovirus, designated Chiqui virus (CHQV) strain CoB38d, that was isolated from a pool of unidentified mosquitoes collected in northern Colombia in 2013. CHQV has nine double-stranded DNA (dsRNA) genome segments and has similarity to viruses belonging to the family Reoviridae, subfamily Spinareovirinae.
A Novel Ultra-Wideband Microstrip-to-Waveguide Transition Based on Microstrip Tramped Antenna

In this paper, we propose a novel microstrip-to-waveguide transition where a tramped antenna is utilized to reach the transmission characters of ultra-wide band and end-fire radiation. With such a new excitation scheme, compact design and low cost manufacturing can be obtained without multi-layer substrates or waveguide sidewall slits. Under mechanical alignment, the so designed UWB antenna coupled with microstrip-to-waveguide connector can obtain a return loss less than -10dB, VSWR less than 1.8 within the band (6GHz ~ 50GHz).
The Context Matters: Predicting the Number of In-game Actions Using Traces of Mobile Augmented Reality Games

Augmented Reality (AR) is an approach to enrich the real world with additional information. It allows users to interact with virtual objects that are linked to locations in the real world. In the area of mobile computer games, AR is quite demanding for managing resources in communication networks, since in-game points of interest typically lead to high network loads, whereas network utilization is otherwise below the average. Predicting the number of in-game actions of mobile AR games can help to scale the game back-end reasonably without over- or under-provisioning of resources. Context information like weather or available Wi-Fi access points can play a key role in estimating or predicting the number of in-game actions. In this paper, we analyze a comprehensive dataset that contains players' actions of one of the most popular mobile AR games, Ingress. The dataset entails more than 23.9 million player actions over a period of 17 months, as well as additional contextual information. Our analysis shows a highly significant relationship between context factors and in-game actions, which explains large parts of users' behavior in terms of when, where and how often they play the game. By combining four different context factors (i.e., time, user, physical and computing context), our analysis can explain up to 84.44% of the variation in the number of in-game actions.
Persuasive design of a mobile coaching app to encourage a healthy lifestyle during menopause

The menopause transition is associated with physiological changes that increase women's cardiovascular and metabolic risk. Healthier and more conscious behavior in specific areas (diet, physical activity, smoking, etc.) can mitigate this risk. However, in order to modify such behavior, women must be aware of the need to use endogenous and exogenous strategies to improve their lifestyle habits. Despite the explosion of health-related apps, there are currently no innovative examples addressing a self-care approach to menopause by applying personalization, adaptability, and persuasion to induce women to improve their health-related lifestyle. Therefore, this paper describes the theoretical foundations and design phases of a system that coaches women to improve their lifestyle during menopause. To this end, we used a Participatory Design approach involving different groups of women along the different design phases to define the system's user interfaces and interaction mechanisms.
Commentary (Engstrom/Langer): Management of Cancer in the Elderly

With the aging of the Western population, cancer in the older person is becoming increasingly common. After considering the relatively brief history of geriatric oncology, this article explores the causes and clinical implications of the association between cancer and aging. Age is a risk factor for cancer due to the duration of carcinogenesis, the vulnerability of aging tissues to environmental carcinogens, and other bodily changes that favor the development and the growth of cancer. Age may also influence cancer biology: Some tumors become more aggressive (ovarian cancer) and others, more indolent (breast cancer) with aging. Aging implies a reduced life expectancy and limited tolerance to stress. A comprehensive geriatric assessment (CGA) indicates which patients are more likely to benefit from cytotoxic treatment. Some physiologic changes (including reduced glomerular filtration rate, increased susceptibility to myelotoxicity, mucositis, and cardiac and neurotoxicity) are common in persons aged 65 years and older. The administration of chemotherapy to older cancer patients involves adjustment of the dose to renal function, prophylactic use of myelopoietic growth factors, maintenance of hemoglobin levels around 12 g/dL, and proper drug selection. Age is not a contraindication to cancer treatment: With appropriate caution, older individuals may benefit from cytotoxic chemotherapy to the same extent as the youngest patients.
Reading Scheduler: Proactive Recommendations to Help Users Cope with Their Daily Reading Volume

To help deal with daily reading volumes, we present Reading Scheduler, a smartphone application linked to people's reading list, which triggers reading reminders throughout the day. The app suggests articles according to their length, complexity, and the time available for reading as indicated by the user. In a field study, we collected usage data from ten participants over the course of two weeks. During this time, we recorded mobile sensor data and trained a classifier to detect opportune moments for reading. Participants read 182 articles while we collected 787,752 sensor data points. Together with an assessment of the feasibility of proactive reading suggestions, we present a prediction model with close to 73% accuracy, that can be used to build mobile recommender systems for utilizing idle moments for reading throughout the user's day.
Room-temperature cycling of metal fluoride electrodes: Liquid electrolytes for high-energy fluoride ion cells

Working toward fluoride batteries Owing to the low atomic weight of fluorine, rechargeable fluoride-based batteries could offer very high energy density. However, current batteries need to operate at high temperatures that are required for the molten salt electrolytes. Davis et al. push toward batteries that can operate at room temperature, through two advances. One is the development of a room-temperature liquid electrolyte based on a stable tetraalkylammonium salt–fluorinated ether combination. The second is a copper–lanthanum trifluoride core-shell cathode material that demonstrates reversible partial fluorination and defluorination reactions. Science, this issue p. 1144 Fluoride ion–conducting liquid electrolytes enable room-temperature cycling of fluoride ion electrochemical cells. Fluoride ion batteries are potential “next-generation” electrochemical storage devices that offer high energy density. At present, such batteries are limited to operation at high temperatures because suitable fluoride ion–conducting electrolytes are known only in the solid state. We report a liquid fluoride ion–conducting electrolyte with high ionic conductivity, wide operating voltage, and robust chemical stability based on dry tetraalkylammonium fluoride salts in ether solvents. Pairing this liquid electrolyte with a copper–lanthanum trifluoride (Cu@LaF3) core-shell cathode, we demonstrate reversible fluorination and defluorination reactions in a fluoride ion electrochemical cell cycled at room temperature. Fluoride ion–mediated electrochemistry offers a pathway toward developing capacities beyond that of lithium ion technology.
Exploring Head Mounted Display based Augmented Reality for Factory Workers

We present our prototype application of head mounted display (HMD) based augmented reality (AR) for use in in factory contexts. The prototype was evaluated in a focus group based user study held at an industrial production line. The prototype was implemented using a Microsoft HoloLens, and supported placing different AR notifications, such as machinery fault or maintenance messages, in the environment. The prototype was used as a stimulus in the focus group consisting of factory workers (n = 6) for exploring the potential use of HMD AR in the industrial production line context. As the main findings, we identified two themes where factory workers saw HMD AR as a potential technology: in task instruction, and in confirming correct functionality. The technology was seen as having potential for collaborative tasks, especially for receiving instructions. Hands-free usage and ability to interact whilst wearing gloves were highly appreciated.
Mellitus: A Smartphone Application for Image Processing and Colorimetric Analysis

Together with materials researchers at CENIMAT, a mobile application was developed in order to perform colorimetric analysis of a glucose paper biosensor to be used for the diagnostic of one of the most incident diseases in the world, Diabetes, using Image Processing Techniques and Machine Learning algorithms. This application was designed based on a User Story and various scenarios describing how users can interact with it. Testing showed that the app is capable of distinguishing high glucose concentrations from low concentrations, while a usability questionnaire made to potential users showed that they thought the application was easy to use, being capable of becoming a portable and fast mean of diagnostic.