Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictors of Source Memory Success and Failure in Older Adults

Source memory decline has been identified as one of the types of memory most seriously affected during older age. It refers to our capacity to recollect the contextual information in which our experiences take place. Although most elderly adults will be affected by progressive source memory decline, a subset of individuals will not follow this average pattern; instead, their source memory capabilities will remain functional. Likewise, a minority of individuals will manifest an extreme decay of their source memory abilities. The objective of the present study was to identify among 120 potential predictors that significantly contributed to these two extreme source memory outcomes. Spatial source memory was measured in a sample of 519 healthy individuals between 61 and 80 years old. Individuals who performed below the 20th and above the 80th percentiles in the source memory task were defined as individuals whose episodic memory failed and succeeded, respectively. Logistic models identified five and six significant predictors of source memory success and failure in older age, respectively. High source memory performance was mainly predicted by healthy cardiovascular markers and psychological traits, whereas low source memory performance was primarily predicted by consumption habits and by less engagement in mental activities. The models identified relevant biological and life experiences that underlie these unusual source memory outcomes in older age.
Residual Activatability of Circulating Tfh17 Predicts Humoral Response to Thymodependent Antigens in Patients on Therapeutic Immunosuppression

The generation of antibodies against protein antigens (such as donor-specific HLA molecules) requires that T follicular helper cells (Tfh) provide help to B cells. Immunosuppressive (IS) armamentarium prevents T cell activation, yet a significant proportion of renal transplant patients develop donor-specific antibodies (DSA), which suggests that IS drugs do not efficiently block T follicular helper cells. To test this hypothesis, the number of circulating Tfh, their polarization profile, and ability to up-regulate (i) the co-stimulatory molecules CD40L and ICOS, and (ii) the activation marker CD25, following in vitro stimulation in presence of IS drugs, were compared between 36 renal transplant patients (6–72 months post transplantation) and nine healthy controls. IS drugs reduced the number of Tfh1 and 2 but had little impact on Tfh17, which was the dominant subset in transplant patients. Although, IS drugs decreased activation-induced expression of co-stimulatory molecules by Tfh, the impact was highly variable between individuals. Furthermore, 20% of transplant patients displayed normal expression of CD25 on Tfh following in vitro stimulation (i.e., “residual activatability”). To test whether residual activatability of Tfh correlates with antibody response against thymo-dependent antigens we took advantage of the 2015 influenza vaccination campaign, which provided a normalized setting for antigenic stimulation. In line with our hypothesis, responders to influenza vaccine exhibited significantly higher percentage of CD25-expressing Tfh17 after in vitro stimulation. A results that was confirmed retrospectively in nine transplanted patients at the time of first DSA detection. We concluded that “residual activatability” of Tfh17 might be used as a non-invasive biomarker to identify transplant patients at higher risk to develop DSA under immunosuppression. If validated in larger studies, this assay might help optimizing the prevention of DSA through personalized adaptation of immunosuppressive regimen.
RSSI-based Floor Localization Using Principal Component Analysis and Ensemble Extreme Learning Machine Technique

With the increasing requirement of the location based services, the floor localization which can determine the floor level of a mobile user in a building received much attentions. In this paper, a novel received signal strength indicator (RSSI) based method for floor localization via principal component analysis (PCA) and ensemble extreme learning machine (ELM) techniques is proposed. In the off-line phase, based on the developed RSSI fingerprint collection software in Android system, the RSSI measurement-floor level training data set was formed. Since too much access point (AP) feature dimension will lead to great redundant information which can increase the computational complexity and prolong the training time, the PCA feature extraction pre-processing is utilized for dimension reduction of the training set. Meanwhile, in order to improve the learning ability, ensemble ELM, rather single ELM, is proposed for classification learning and obtained the classification functions. In the on-line phase, the PCA is utilized to map the received RSSI measurement from higher-dimensional space to lower dimensional space. Then the classification functions are utilized for final floor level estimation. The experiment results show that the proposed RSSI-based method can offer a more accurate floor level estimation results than other existing algorithm.
THE FUTURE OF CONTEXTUAL KNOWLEDGE IN GAMING SIMULATIONS: A RESEARCH AGENDA

Gaming simulations (games) are increasingly becoming the tool of choice for modeling and understanding the complexity of today’s systems. This increased popularity has consequently revealed the weaknesses of games in several areas. These limitations range from inconsistencies on the game design to the unexploited explicit and tacit knowledge that games invoke. This paper focuses on games that do not aim at generalizing the produced knowledge but, instead, at understanding how a system works within a specific context. The first step of the analysis is identifying these limitations based on an extensive literature review. Based on this, different directions that could mitigate or even fully address these limitations are proposed. The paper concludes with a focused research agenda.
Demonstration of HfO2-Based Gate Dielectric With Low Interface State Density and Sub-nm EOT on Ge by Incorporating Ti Into Interfacial Layer

HfO<sub>2</sub>-based gate stacks with titanium (Ti) incorporated into a GeO<sub><italic>x</italic></sub> interfacial layer (IL) were fabricated on p-Ge substrates. X-ray photoelectron spectroscopy results, incorporating Ti, can efficiently suppress the GeO volatilization. This contributed to achievement of an equivalent oxide thickness of approximately 0.8 nm, a low interface states density <inline-formula> <tex-math notation="LaTeX">$\text{D}_{\textsf {it}}$ </tex-math></inline-formula> of approximately 2.5 <inline-formula> <tex-math notation="LaTeX">$\times \,\,10^{\textsf {11}}$ </tex-math></inline-formula> eV<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {1}}$ </tex-math></inline-formula>cm<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {2}}$ </tex-math></inline-formula>, and a smaller <inline-formula> <tex-math notation="LaTeX">$\text{D}_{\textsf {it}}$ </tex-math></inline-formula> increment of approximately 1 <inline-formula> <tex-math notation="LaTeX">$\times \,\,10^{\textsf {11}}$ </tex-math></inline-formula> eV<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {1}}$ </tex-math></inline-formula>cm<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {2}}$ </tex-math></inline-formula> after constant negative voltage stress at a field of 8 MV/cm. Therefore, Ti is appropriate for improving the quality of GeO<sub>x</sub> through high temperature annealing.
MiniTracker: A Lightweight CNN-based System for Visual Object Tracking on Embedded Device

Visual object tracking (VOT) is a computer vision application and has a wide range of use. However, related state of the art algorithms using deep learning methods, are computationally intensive and storage explosive. Whats more, despite many deep learning accelerators have been proposed, many of them are general structure. So, in this paper, we propose a lightweight CNN-based system–-MiniTracker, integration of algorithm and hardware–-particularly efficient for VOT. Because of the fully-convolutional Siamese network we used, the parameters of network do not need online training, which reduces computation consumptions dramatically. We adapt the original Siamese network (SN) into effective hardware implementation by parameter pruning and quantization. Then a lightweight CNN with the 8-bit parameters is produced, which is only 1.939MB. The real tracking rate is 18.6 frames per second at the cost of 1.284W on ZedBoard. Moreover, Compared with other hardware implementations, our system is robust to challenging scenarios, such as occlusions, changing appearance, illumination variations and etc.
A Method for Face Fusion Based on Variational Auto-Encoder

Face fusion refers to fuse two different facial images into a new face image that retains the facial features of the original image. Our network model is combine Variational Auto-Encoder(V Ae)and Generative Adversarial Networks(GAN), which achieved the end-to-end fusion task. Not only does it guarantee the quality of the fusion image(the image generated by GAN is sharp and photorealistic), but also doesn't lose specific details of the face(This is guaranteed by VAE). In the end, the experiment achieved a promising result on the CelebA dataset.
Captioning with Language-Based Attention

The goal of image captioning via machine learning is to automatically learn to provide a free-form description of an image, while focusing on the significant objects in an image. Inspired by recent work on attention in image captioning, we study in this paper different attention mechanisms within a deep learning setting. In contrast to previous research on attention models which focus on applying attention to the image modality, we introduce three language-based attention models. These language-based attention models, which we developed iteratively from simpler RNN-and LSTM-based baseline models, consist of two sub-networks: a deep recurrent neural network for the language modality and a convolutional neural network for the image modality. The language-based attention models learn a joint representation of the language and image modalities, given the image and the previous words in the caption. At test time, novel captions are produced from this learned distribution. We provide a comparative quantitative and qualitative analysis of our three language-based attention models, which outperform the simple baseline models. We validate the effectiveness of our attention models with state-of-the-art performance on the Flickr8k dataset.
MULTI-SENSOR FAULTDETECTION AND ISOLATION FOR AERO-ENGINE DCS WITH MARKOV TIME DELAY BASED ON H∞UNKNOWN INPUT OBSERVERS

In this paper, a novel approach is proposed to design a bank of unknown input observers(UIOs) for aero-engine distribute control system(DCS) with multi-sensor fault. First, considering the system with multi-sensor fault which doesn’t satisfy the observer matching condition, a H∞ UIO is proposed based on the disturbance decoupling principle of unknown input observer(UIO) and H∞ control theory. The parameter matrixes of UIO are solved in terms of linear matrix inequality(LMI) toolbox. Second, the fault detection and isolation scheme is proposed for DCS with multi-sensor fault and the UIOs are designed for a bank of fault detection and isolation sets which are obtained by reorganizing the control system. The residual signal generated by each observer is robust to the sensor fault in the matching set, and sensitive to the sensor fault out of the matching set. Finally, the residual signals were simulated and the results show the effectiveness of the proposed method.
Improved Electrical Characteristics of Bulk FinFETs With SiGe Super-Lattice-Like Buried Channel

Although SiGe super-lattice (SL) buried channel was proposed to enhance carrier mobility in MOSFETs, its application on FinFETs is not reported yet. The electrical characteristics of nFinFETs with SiGe and SiGe SL-like buried channels were studied in this letter for the first time. The results show that the electron mobility of nFinFETs with SiGe buried channel is enhanced by an SL-like structure. A higher ON-state current and ON-state/ OFF-state current ratio are also achieved. However, the sub-threshold swing and reliability characteristics need improvement due to slight degradation of interface quality.
Dispersion-Engineered Transmission Line Loaded Slot Antenna for UWB Applications

A transmission line (TL) loaded, compact, ultrawideband (UWB) square slot antenna is proposed in this letter. The square slot is a microstrip line fed and loaded with an array of periodically perturbed TLs to achieve a UWB response from 2.1 to 11.5 GHz. The slot is designed to resonate at 2.6 GHz. The TLs are dispersion engineered to resonate at several frequencies so as to provide a wideband response. Loading of the engineered TLs on the square slot produced multiple resonances without affecting the fundamental mode of the slot antenna. The proposed antenna design is analyzed using dispersion relations and equivalent circuits. The proposed antenna is fabricated on an RT/Duroid 5880 substrate with a size of 50 × 50 × 1.52 mm3. The antenna performance metrics, such as impedance bandwidth, gain, and efficiency, are experimentally verified. Good agreement is obtained between simulated and measured results.
Human Lesion Detection Method Based on Image Information and Brain Signal

The brain is the largest and most complex structure in the central nervous system. It dominates all activities in the body, and the lesions in the human body are also reflected in the brain signal. In this paper, the image method is used to assist the brain signal to detect the human lesion. Due to the particularity of medical images, there is no common segmentation method for any medical image, and there is no objective standard to judge whether the segmentation is effective. Medical image segmentation technology is still a bottleneck restricting the development and the application of other related technologies in medical image processing. Based on the above reasons, this paper proposes an improved region growing algorithm based on the fuzzy theory and region growing algorithm. The algorithm is used to segment the medical images of the liver and chest X-ray of different human organs. The improved algorithm uses a threshold segmentation algorithm to assist in the automatic selection of seed points and improves the region growing rules, then morphological post-processing is used to improve the segmentation effect. The experimental results show that the improved region growing algorithm has better segmentation effect under two different organs, which proves that the algorithm has certain applicability, and its accuracy and segmentation quality are better than the traditional region growing algorithm. This algorithm combines the advantages of the threshold method and traditional region growing method. It is feasible in algorithm and has certain application value.
A Wideband Polarization-Reconfigurable Water Dielectric Resonator Antenna

A water dielectric resonator antenna with reconfiguration and broadband characteristics is presented. By injecting pure water into various cavities of the Plexiglass holder, the antenna can be switched among left-hand circular polarization, right-hand circular polarization, and linear polarization in almost the same operating frequency range. With the high relative permittivity, water acts as a dielectric resonator as well as an interface similar to electric wall to form a broad axial-ratio bandwidth. Thus, a broad usable bandwidth is measured to be over 22%, and the measured radiation efficiency and realized gain centralize in 60%–70% and 3–4 dBi, respectively.
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline. Participants were provided with 899 whole-slide images (WSIs) for developing their algorithms. The developed algorithms were evaluated based on the test set encompassing 100 patients and 500 WSIs. The evaluation metric used was a quadratic weighted Cohen’s kappa. We discuss the algorithmic details of the 10 best pre-conference and two post-conference submissions. All these participants used convolutional neural networks in combination with pre- and postprocessing steps. Algorithms differed mostly in neural network architecture, training strategy, and pre- and postprocessing methodology. Overall, the kappa metric ranged from 0.89 to −0.13 across all submissions. The best results were obtained with pre-trained architectures such as ResNet. Confusion matrix analysis revealed that all participants struggled with reliably identifying isolated tumor cells, the smallest type of metastasis, with detection rates below 40%. Qualitative inspection of the results of the top participants showed categories of false positives, such as nerves or contamination, which could be targeted for further optimization. Last, we show that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination.
A Wearable PIFA With an All-Textile Metasurface for 5 GHz WBAN Applications

In this letter, a wearable all-textile metasurface antenna (MSA) for 5 GHz wireless body area network (WBAN) applications is proposed. All the components of the proposed MSA are made of the comfort textile materials. A metasurface with high permittivity values is placed right above a wide-bandwidth planar inverted-F antenna to realize size miniaturization and gain enhancement. The proposed MSA has a profile of 4 mm (0.07<italic>λ</italic><sub>0</sub>) and occupies an area of 42 mm × 28 mm (0.77<italic>λ</italic><sub>0</sub> × 0.51<italic>λ</italic><sub>0</sub>). Moreover, this antenna realizes a measured peak gain of 6.70 dBi, an average efficiency of 77%, and an operating band from 4.96 to 5.90 GHz that covers the 5 GHz WBAN band. In addition, the on-body studies show that the MSA is suitable for wearable applications.
Learning Multi-Domain Convolutional Network for RGB-T Visual Tracking

Object tracking is one of the challenging problems in the field of computer vision. Affected by the unstructured environments, for example, the occlusion, noise, and light, These factors can affect the appearance of the specific object and result in failures when tracking specific objects. To address this issue, we propose a novel visual tracking method based on multimodal convolutional network learning. Our framework adopts a parallel structure, which consists of two shallow convolutional neural networks. First, the parallel network is used to draw the different features of the RGB- T (RGB and thermal) data separately. Second, this two kind of features are mixed together and finally the mixed feature is sent to domain-specific layers for binary classification and identification of the targets. We perform comprehensive experiments on RGBT234 visual data and the results prove that the proposed visual tracking method improves the effects significantly through the use of multi-modal features, which illustrates that our method is competitive in performances against with the state-of-the-art tracking algorithms.
Convolutional Neural Networks for Disaggregated Population Mapping Using Open Data

High resolution population count data are vital for numerous applications such as urban planning, transportation model calibration, and population growth impact measurements, among others. In this work, we present and evaluate an end-to-end framework for computing disaggregated population mapping employing convolutional neural networks (CNNs). Using urban data extracted from the OpenStreetMap database, a set of urban features are generated which are used to guide population density estimates at a higher resolution. A population density grid at a 200 by 200 meter spatial resolution is estimated, using as input gridded population data of 1 by 1 kilometer. Our approach relies solely on open data with a wide geographical coverage, ensuring replicability and potential applicability to a great number of cities in the world. Fine-grained gridded population data is used for 15 French cities in order to train and validate our model. A stand-alone city is kept out for the validation procedure. The results demonstrate that the neural network approach using massive OpenStreetMap data outperforms other approaches proposed in related works.
A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews

With the emergence of opinion spam, scholars in recent years have been investigating how to distinguish between authentic and fake online reviews. In this research area however, constructing ground truth has been a tricky problem. When labeled datasets of authentic and fake reviews are unavailable, it becomes impossible to systematically investigate differences between the two. In light of this problem, the goal of this paper is three-fold: (1) To review existing approaches of developing ground truth, (2) To present an improved methodological template to construct ground truth, and (3) To conduct a quality-check of the newly constructed ground truth. The existing approaches are dissected to identify several peculiarities. The new approach invests in mitigating pitfalls in the current approaches. In the newly constructed ground truth, authentic reviews were found to be not easily distinguishable from fake reviews. Finally, new research directions are identified with the hope that scholars would be able to stay ahead in their relentless race against spammers.
Parallel Branching of Two 2-DIMM Sections With Write-Direction Impedance Matching for an 8-Drop 6.4-Gb/s SDRAM Interface

A parallel branching synchronous dynamic random access memory (SDRAM) channel with write-direction impedance matching (parallel branching with write-direction impedance matching (PBIM)) is proposed for an 8-drop 6.4-Gb/s SDRAM interface. The 8-drop PBIM channel consists of two parallel branches; each branch consists of a series connection of two dual in-line memory modules for a 4-drop configuration and the data (data pin) channel uses two kinds of transmission lines with characteristic impedances of 50 and $25~\Omega $ and a resistor on the motherboard. Measurements on the test setup show that the proposed 8-drop channel works at the DDR5 target data rate of 6.4 Gb/s in both write and read directions by using the same motherboard area as that of the stub series terminated logic channel and a bit-error-rate tester for transmitter and receiver.
A SIMULATION MODEL TO ASSESS THE IMPACT OF INSURANCE EXPANSION ON COLORECTAL CANCER SCREENING AT THE POPULATION LEVEL

Recent health care reform debates have triggered substantial discussion on how best to improve access to insurance. Colorectal cancer (CRC) is an example of a largely preventable condition, if access to and use of healthcare is increased. Early and ongoing screening and intervention can identify and remove polyps before they become cancerous. We present the development of an individual-based discrete-event simulation model to estimate the impact of insurance expansion scenarios on CRC screening, incidence, mortality, and costs. A national repeated cross-sectional survey was used to estimate which individuals obtained insurance in North Carolina (NC) after the Affordable Care Act (ACA). The potential impact of expanding the state’s Medicaid program is tested and compared to no insurance reform and the ACA without Medicaid expansion. The model integrates a census-based synthetic population, national data, claims based statistical models, and a natural history module in which simulated polyps and cancer progress.