Miracle-dz/UniFAR-FTUs huggingface dataset:
Relay-Aided Multi-User OFDM Relying on Joint Wireless Power Transfer and Self-Interference Recycling

Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network’s sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source.
Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data

Alzheimer’s disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer’s disease. Source code is available at https://github.com/JaesikKim/temporal-GNN.
Optimizing the Opening Period and the Timing of Intake and Exhaust Valves to Improve Engine Performance in a Supermilage Vehicle

To improve engine torque and specific fuel consumption in a supermilage vehicle, we experimentally adjusted the valve lift and opening period in rocker arms, testing various follower configurations and adjust screws. Using the follower configuration in a commercially-available rocker arm, we compared 4 different levels of valve lift and opening period in the intake, and 4 different levels in the exhaust, making 16 combinations. Then, utilizing 5 kinds of modified follower configurations of the rocker arms in the intake, and 3 in the exhaust, we also compared 24 combinations (including the commercially-available follower configurations). We tested our experimental supermilage engine under full-load at 2000 to 4500rpm, simulating powering a supermilage vehicle. Results indicated that (1) with a commercially-available follower configuration of the rocker arm, specific fuel consumption decreased when setting both the open timing in the intake valve, and the close timing in the exhaust valve, at or near the top dead center (TDC), while decreasing overlap period of intake and exhaust valves. (2) With the modified follower configurations of the rocker arms, engine torque increased while specific fuel consumption decreased, when setting both the open timing in the intake valve and the close timing in the exhaust valve at or near the TDC, and the close timing in the intake valve at or near the bottom dead center (BDC), and the open timing in the exhaust valve at or near BDC, or the early opening timing in the exhaust valve.
The Prototype Design of Water pH and TDS Indicator Device based on Microcontroller Arduino

Fishery sector plays important role in fulfilling the food supply. The increase in fish consumption directly impacts the fishery sector, on how to deliver well-qualified fishery products. The company that operates in the fishery sector is the agribusiness company located in Jakarta. Two products are manufactured, which are Red Tilapia fish and Catfish. This research aims to design the work system for a water pH and TDS indicator device by using Microcontroller Arduino, specifically designated for a fishpond. This device uses a sensor and utilizes a block diagram for device design. Several tests on various ponds were conducted to determine error percentage. According to the pH sensor, Pond 1 was recorded with 0.658%, Pond 2 with 1.091%, and Pond 3 with 1,495%. While, by using the TDS sensor, it can be calculated that Pond 1 resulted in 2,0%, Pond 2 with 4,701%, Pond 3 with 1,495%, for its error percentage. For an accurate result, the company should perform routine inspections and maintenance on devices.
How Inclusive Are Wikipedia’s Hyperlinks in Articles Covering Polarizing Topics?

Wikipedia relies on an extensive review process to verify that the content of each individual page is unbiased and presents a "neutral point of view. " Less attention has been paid to possible biases in the hyperlink structure of Wikipedia, which has a significant influence on the user’s exploration process when visiting more than one page. The evaluation of hyperlink bias is challenging because it depends on the global view rather than the text of individual pages.In this paper, we focus on the influence of the interconnect topology between articles describing complementary aspects of polarizing topics. We introduce a novel measure of exposure to diverse information to quantify users’ exposure to different aspects of a topic throughout an entire surfing session, rather than just one click ahead. We apply this measure to six polarizing topics (e.g., gun control and gun right), and we identify cases in which the network topology significantly limits the exposure of users to diverse information on the topic, encouraging users to remain in a knowledge bubble. Our findings demonstrate the importance of evaluating Wikipedia’s network structure in addition to the extensive review of individual articles.
Therapeutic Claims in Cannabidiol (CBD) Marketing Messages on Twitter

Although the U.S. FDA has only approved exactly one cannabidiol (CBD) drug product (specifically to treat seizures), CBD products are proliferating rapidly through different modes of usage including food products, cosmetics, vaping pods, and supplements (typically, oils). Despite the FDA clearly warning consumers about unproven health claims made by manufacturers selling CBD products over the counter, the CBD market share was nearly 3 billion USD in 2020 and is expected to top 55 billion USD in 2028. In this context, it is important to assess the presence of health claims being made on social media, especially claims that are part of marketing messages. To this end, we collected over two million English tweets discussing CBD themes. We created a hand-labeled dataset and built machine learned classifiers to identify marketing tweets from regular tweets that may be generated by consumers. The best classifier achieved 85% precision, 83% recall, and 84% F-score. Our analyses showed that pain, anxiety disorders, sleep disorders, and stress are the four main therapeutic claims made constituting 31.67%, 27.11%, 13.77%, and 10.37% of all medical claims made on Twitter, respectively. Also, more than 93% of advertised CBD products are edibles or oil/tinctures. Our effort is the first to demonstrate the feasibility of surveillance of marketing claims for CBD products. We believe this could pave way for more explorations into this indispensable task in the current landscape of social media driven health (mis)information and communication.
Large-scale multiple-source detection using wireless sensor networks

This dissertation concerns the sequential large-scale detection of multiple potential sources using wireless sensor networks. A new 2-step approach to sequential multiple-source detection is introduced called the iterative partial sequential probability ratio test (IPSPRT) that minimizes the time-to-decision as the desired probability of false alarm and probability of miss decrease. The first step of the IPSPRT sequentially decides whether any or no sources become active at a specific time, based on a sequential probability ratio test using the generalized likelihood ratio such that the probability of indecision is minimized and the maximum probability of false alarm and maximum probability of miss are bounded. If step one decides that some source is active, step two identifies active sources through an iterative maximization of the likelihood ratio and physical inspection process such that the probability that an active source is not detected is bounded. After a decision is made regarding sources which become active at a specific time, the IPSPRT increments the time at which sources are hypothesized to become active and the procedure continues. Numerical evaluations of the IPSPRT are provided in comparison to other feasible methods for a diffusion process monitoring example consisting of 100 sensors and 100 potential sources. A new dynamic sensor selection problem is formulated for the non-Bayesian multiple source detection problem using a generalized likelihood ratio based dynamic sensor selection strategy (GLRDSS) which a minimum number of sensors to report observations at each sampling instance. An evaluation of the GLRDSS is provided through simulation. A carbon sequestration site monitoring application is introduced as a case study and a test bed implementation discussed. The robustness of the IPSPRT and dynamic sensor selection algorithm to common wireless sensor networking errors and failures is evaluated using the carbon sequestration site monitoring application as a case study.
Accelerating Geo-Distributed Transaction Processing with Fast Logging

We herein propose three novel optimization methods to accelerate distributed transaction processing into a geographically distributed database. The first optimization involves the parallelization of the write-ahead logging protocol. It allows multiple worker threads to synchronize log entries to the storage device simultaneously without any dependencies. The second optimization involves the grouped transfer of log entries from the leader to followers. This reduces the number of transmissions and effectively uses the network bandwidth. The third optimization involves the separation of the worker thread logic. By breaking the logic into the prepare phase and the commit phase, the worker threads at the leader node run asynchronously in parallel without waiting for responses from the follower nodes. The experimental results demonstrated that the proposed method achieved more than 10 million tps and less than 100 ms with client interactions through the network. The CPU utilization was almost 100%, which implied a dramatic reduction in synchronization in worker threads.
Fingerphoto Presentation Attack Detection: Generalization in Smartphones

A fingerphoto is obtained by imaging a human finger using a basic smartphone camera. Although impressive advances have been made to accurately match fingerphotos, this technology is vulnerable to presentation attacks (PAs). These algorithms do not generalize well in the presence of new presentation attacks. While previous research on this issue is limited, this paper systematically evaluates fingerphoto presentation attack detection (PAD) algorithms under unknown attacks. The proposed assessment compares different Convolutional Neural Networks (CNNs) on the IIITD Smartphone Fingerphoto database with spoof data including printout and various display attacks. These images used for the experiments were acquired indoors and subjected to background (i.e., white or natural) and capture device (i.e., Nokia or OPO) variations. Preliminary results show that the PAD based on AlexNet is robust under most types of replica unseen during the training of the detector.
TRF2 rescues telomere attrition and prolongs cell survival in Duchenne muscular dystrophy cardiomyocytes derived from human iPSCs

Duchenne muscular dystrophy (DMD) is a severe muscle wasting disease caused by the lack of dystrophin. Heart failure, driven by cardiomyocyte death, fibrosis, and the development of dilated cardiomyopathy, is the leading cause of death in DMD patients. Current treatments decrease the mechanical load on the heart, but do not address the root cause of dilated cardiomyopathy: cardiomyocyte death. Previously, we showed that telomere shortening is a hallmark of cardiomyocytes in DMD patient cardiac tissues and in a mouse model of DMD we generated that manifests dilated cardiomyopathy. We also found that this telomere shortening is recapitulated in DMD cardiomyocytes differentiated from patient-derived induced pluripotent stem cells (iPSCs). Here we further characterize that DMD cardiomyocytes exhibit reduced cell size, nuclear size, and sarcomere density compared to healthy isogenic controls. The telomere-binding protein, TRF2, is a core component of the shelterin complex, which protects chromosome ends and is expressed at lower levels in DMD cardiomyocytes compared to controls. We investigated whether preservation of telomere length could be achieved with upregulation of TRF2. We found that TRF2 upregulation in DMD cardiomyocytes increased telomere lengths, cell size, nuclear size, sarcomere density, and cell survival. These data suggest TRF2 gene therapy has the potential to delay the onset of dilated cardiomyopathy.
Membrane Interactions and Uncoating of Aichi Virus, a Picornavirus That Lacks a VP4

To initiate infection, viruses must enter a host cell and deliver their genome into the appropriate location. The picornavirus family of small nonenveloped RNA viruses includes significant human and animal pathogens and is also a model to understand the process of cell entry. ABSTRACT Kobuviruses are an unusual and poorly characterized genus within the picornavirus family and can cause gastrointestinal enteric disease in humans, livestock, and pets. The human kobuvirus Aichi virus (AiV) can cause severe gastroenteritis and deaths in children below the age of 5 years; however, this is a very rare occurrence. During the assembly of most picornaviruses (e.g., poliovirus, rhinovirus, and foot-and-mouth disease virus), the capsid precursor protein VP0 is cleaved into VP4 and VP2. However, kobuviruses retain an uncleaved VP0. From studies with other picornaviruses, it is known that VP4 performs the essential function of pore formation in membranes, which facilitates transfer of the viral genome across the endosomal membrane and into the cytoplasm for replication. Here, we employ genome exposure and membrane interaction assays to demonstrate that pH plays a critical role in AiV uncoating and membrane interactions. We demonstrate that incubation at low pH alters the exposure of hydrophobic residues within the capsid, enhances genome exposure, and enhances permeabilization of model membranes. Furthermore, using peptides we demonstrate that the N terminus of VP0 mediates membrane pore formation in model membranes, indicating that this plays an analogous function to VP4. IMPORTANCE To initiate infection, viruses must enter a host cell and deliver their genome into the appropriate location. The picornavirus family of small nonenveloped RNA viruses includes significant human and animal pathogens and is also a model to understand the process of cell entry. Most picornavirus capsids contain the internal protein VP4, generated from cleavage of a VP0 precursor. During entry, VP4 is released from the capsid. In enteroviruses this forms a membrane pore, which facilitates genome release into the cytoplasm. Due to high levels of sequence similarity, it is expected to play the same role for other picornaviruses. Some picornaviruses, such as Aichi virus, retain an intact VP0, and it is unknown how these viruses rearrange their capsids and induce membrane permeability in the absence of VP4. Here, we have used Aichi virus as a model VP0 virus to test for conservation of function between VP0 and VP4. This could enhance understanding of pore function and lead to development of novel therapeutic agents that block entry.
LncRNA PNKY Is Upregulated in Breast Cancer and Promotes Cell Proliferation and EMT in Breast Cancer Cells

Cancer stem cell (CSCs) theory states a small number of cancer cells shares various features with normal stem cells. It was demonstrated that not only stemness transcription factors such as Oct4, but also stemness long non-coding RNA, are contributing in different biological aspects of CSCs. Recently, PNKY has been found to be involved in pluripotency and differentiation of embryonic and postnatal neural stem cells (NSCs). In present study, we observed that PNKY is expressed in various cancer cells. In more detail, we defined that PNKY was significantly upregulated in breast tumors, especially in high-grade ones. Knock down experiments indicated that suppression of PNKY in breast cancer cells could restrict their proliferation through promoting apoptosis, senescence, and cell cycle disruption. Moreover, the result determined that PNKY might have crucial role in cell migration of cancer cells. We further found that PNKY might be triggering EMT in breast cancer cells through upregulated mir-150 expression and restricting the expression of Zeb1 and snail. Altogether, for the first time, this study provides novel evidence for the biological function of PNKY in cancer cells and it might be contributing in tumor growth and metastasis.
FMRP inhibits translation elongation independent of mRNA G-quadruplexes

Loss of functional fragile X mental retardation protein (FMRP) causes fragile X syndrome, the leading form of inherited intellectual disability and the most common monogenic cause of autism spectrum disorders. FMRP is an RNA-binding protein that controls neuronal mRNA localization and translation. Notably, FMRP is thought to inhibit translation elongation after being recruited to target transcripts via binding RNA G-quadruplexes (G4s) within the coding sequence. Here we directly tested this model and report that FMRP inhibits translation elongation independent of mRNA G4s. Furthermore, we found that the RGG box motif together with its natural C-terminal domain forms a non-canonical RNA-binding domain (ncRBD) that binds reporter mRNA and all four polymeric RNA sequences. The ncRBD is essential for FMRP to inhibit translation. Transcripts that are bound by FMRP through the ncRBD co-sediment with heavy polysomes, which is consistent with stalling elongating ribosomes and a subsequent accumulation of slowed polysomes. Together, this work shifts our understanding of how FMRP inhibits translation elongation and supports a model where repression is driven by local FMRP and mRNA concentrations rather than target mRNA sequence.
Emotion Recognition from Multi-channel EEG Data through A Dual-pipeline Graph Attention Network

EEG based emotion recognition technology is currently an important concept in artificial intelligence, and also holds great potential in emotional health care. Nevertheless, one major limitation of the prior approaches is they do not capture the relationships between different time-series and channels explicitly, resulting in inevitable low performance, especially in subject-independent recognition settings. In this paper, we propose a novel graph attention network based model to address this issue. Our framework includes dual-pipeline Graph Attention Network layers in parallel to learn the complex dependencies of multi-channel EEG in both temporal and spatial dimensions. The proposed method outperforms other state-of-the-art models on benchmark SEED dataset. Further analysis shows that our method also has good interpretability. As far as we know, it is the first work that introduce graph attention network into EEG based emotion detection research.
LDI: Learned Distribution Index for Column Stores

In column stores, which ingest large amounts of data into multiple column groups, query performance deteriorates. Commercial column stores use log-structured merge (LSM) tree on projections to ingest data rapidly. LSM improves ingestion performance, but in column stores the sort-merge phase is I/O-intensive, which slows concurrent queries and reduces overall throughput. In this paper, we aim to reduce the sorting and merging cost that arise when data is ingested in column stores. We present LDI, a learned distribution index for column stores. LDI learns a frequency-based data distribution and constructs a bucket worth of data based on the learned distribution. Filled buckets that conform to the distribution are written out to disk; unfilled buckets are retained to achieve the desired level of sortedness, thus avoiding the expensive sort-merge phase. We present an algorithm to learn and adapt to distributions, and a robust implementation that takes advantage of disk parallelism. We compare LDI with LSM and production columnar stores using real and synthetic datasets.
The Impact of Plating on Equipotential Surfaces and Contact Resistance

It was found that equipotential surfaces, in layered contact systems of differing conductivities, can significantly change in shape as they pass across the layer boundary. Specifically, at the boundary a discontinuity in the equipotential surfaces was found using an image charge analysis from the electrostatic equivalent of a steady state current situation. These results provided a method to determine the shape in the layer as a function d/a and $\sigma$, the layer thickness and conductivity respectively. It was found that the shape in the layer significantly changed when the ratio of plating to base metal conductivity increased above 1 $(\sigma_{0}/\sigma > 1)$. In this case, it is shown how the constriction resistance increases as the layer thickness increases. These results show a difference when compared to earlier work [1]. In addition, it is found that when the conductivity ratio falls below 1 $(\sigma_{0}/\sigma$ < 1), the change in shape is very small but still produces different results from the present authors previous work [1]. It is believed these results provide reasonable estimates for layered (plated) contact systems and that these results represent an improvement over earlier work.
Hierarchical MAP Denoising of Longitudinal Hamilton Depression Rating Scores

The Hamilton Depression Rating Scale provides ordinal ratings for evaluating different aspects of depression. These ratings are usually quite noisy, and longitudinal patterns in the ratings can be difficult to discern. This paper proposes a hierarchical maximum-a-posteriori (MAP) method for denoising the ordinal time series of such ratings. Real-world data from a clinical trial are analyzed using the model. Denoising reveals subject-specific longitudinal patterns, predicts future ratings, and reveals progression patterns via principal component analysis.