Miracle-dz/UniFAR-FTUs huggingface dataset:
A Signal Strength Based Location Estimation Algorithm within a Wireless Network

With the prevalence of Wi-Fi activity within a wireless network, there are growing interests in mobile surveillance and device tracking for better network services. With a good location estimation algorithm integrated into a wireless network, system administrators can closely monitor the network traffic as well as the behavior of the mobile users. By modifying the embedded software in off-the-shelf WLAN APs, our system can sniff out data packets transmitted by WLAN devices without the need to install client programs on mobile user devices. In this paper we propose the Aggregated Signal Layout location estimation algorithm. Experiment results show that once wireless activities had been detected, our signal strength based localization algorithm can estimate positions of the wireless mobile devices involved with great success rate and good accuracy. With an accuracy comparable to the fingerprinting method but without paying the high costs of training and maintenance of the fingerprint databases inside our WLAN system, the proposed Aggregated Signal Layout method is well-justified for its efficiency and effectiveness in locating and tracking mobile users' activities within a wireless network.
Early Acquisition of Neural Crest Competence During hESCs Neuralization

Background Neural crest stem cells (NCSCs) are a transient multipotent embryonic cell population that represents a defining characteristic of vertebrates. The neural crest (NC) gives rise to many derivatives including the neurons and glia of the sensory and autonomic ganglia of the peripheral nervous system, enteric neurons and glia, melanocytes, and the cartilaginous, bony and connective tissue of the craniofacial skeleton, cephalic neuroendocrine organs, and some heart vessels. Methodology/Principal Findings We present evidence that neural crest (NC) competence can be acquired very early when human embryonic stem cells (hESCs) are selectively neuralized towards dorsal neuroepithelium in the absence of feeder cells in fully defined conditions. When hESC-derived neurospheres are plated on fibronectin, some cells emigrate onto the substrate. These early migratory Neural Crest Stem Cells (emNCSCs) uniformly upregulate Sox10 and vimentin, downregulate N-cadherin, and remodel F-actin, consistent with a transition from neuroepithelium to a mesenchymal NC cell. Over 13% of emNCSCs upregulate CD73, a marker of mesenchymal lineage characteristic of cephalic NC and connexin 43, found on early migratory NC cells. We demonstrated that emNCSCs give rise in vitro to all NC lineages, are multipotent on clonal level, and appropriately respond to developmental factors. We suggest that human emNCSC resemble cephalic NC described in model organisms. Ex vivo emNCSCs can differentiate into neurons in Ret.k- mouse embryonic gut tissue cultures and transplanted emNCSCs incorporate into NC-derived structures but not CNS tissues in chick embryos. Conclusions/Significance These findings will provide a framework for further studying early human NC development including the epithelial to mesenchymal transition during NC delamination.
Active control of periodic impulsive noise in a non-minimum phase system using repetitive control algorithm

In this paper, active control of periodic impulsive noise is studied. The stability of an active noise control (ANC) system using repetitive control (RC) algorithm is first analysed. Based on the stability condition, a novel non-causal stable inversion approach combined with an optimal criterion is used to design the RC controller for an ANC system with a non-minimum phase secondary path. A non-causal transversal finite impulse response (FIR) filter is used as the RC learning filter to compensate the phase lag of the plant. Computer simulations have been carried out to validate the effectiveness of the proposed algorithm. The plant model used in the computer simulations is obtained from a practical ANC system in our laboratory. Simulation results show that the proposed scheme can significantly reduce periodic impulsive noise and the convergence rate is fast for a non-minimum phase plant.
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— Scheduling through wireless shared channels is a very challenging problem because of each user's link-state variations through time. The challenge lies in the support of the quality of service (QoS) of each user under the uncertainty of channel behavior. This problem becomes even more difficult for some of the 2.5G and 3.5G network architectures (e.g EDGE,HSDPA, HDR) where the supported transmission rate can take multiple values and the channel can not be modeled by the traditional ON-OFF model. In this paper we propose Expo-pred-wei, a new scheduling algorithm, which take advantage of the channel variations in order to maximize the total cell throughput and at the same time to preserve, in an efficient way, a minimum QoS for each user. In contrast to other proposed algorithms, our algorithm exhibits very good performance in a wide set of traffic scenarios including Real-Time (RT) or Non-Real-Time (NRT) traffic as well as mixed RT and NRT traffic.
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Low field relaxation and diffusion measurements have become essential tools to study the pore space of sedimentary rocks with important practical applications in the field of well logging and hydrocarbon extractions. Even at Larmor frequencies below 2 MHz, diffusion measurements are often affected noticeably by internal field inhomogeneities. These field inhomogeneities are induced by susceptibility contrast between the rock and the fluid and are evident in most sandstones. Using sets of two-dimensional diffusion–relaxation measurements in applied and internal gradients, we study in detail the correlation between the field inhomogeneities, restricted diffusion, and relaxation time in three rocks of different susceptibility. We find that in the sandstone cores, the field inhomogeneities in large pores can be described by a local gradient that scales inversely with relaxation time above 250 ms. At shorter relaxation times, the extracted internal gradients deviate from this scaling relationship and we observe a dependence on diffusion time. This demonstrates that in this case, the internal field has structure on a length scale of a few microns.
Instruction scheduling for emerging communication-exposed architectures

Technology trends present new challenges to instruction schedulers and processor architectures. Although increasing transistor counts will enable numerous execution units on a single chip, decreasing wire transmission speeds will cause on-chip latencies to increase to tens of cycles. Conventional architectures, including static VLIWs and dynamic superscalars, and their instruction schedulers are not capable of meeting these challenges. This paper proposes a new instruction scheduling algorithm for emerging wire-dominated architectures that features critical-path instruction selection, placement of instructions to minimize communication distances, and load balancing across the distributed execution units. We evaluate the algorithm on the Grid Processor Architecture (GPA), which supports a hybrid execution model that requires static instruction placement but allows dynamic execution. The combination of this novel scheduling algorithm with the GPA results in the highest demonstrated instruction-level parallelism to date, sustaining over 8 instructions per cycle on a 64-wide issue machine.
Postoperative Changes in Cerebral Metabolites Associated with Cognitive Improvement and Impairment after Carotid Endarterectomy: A 3T Proton MR Spectroscopy Study

This study assessed the use of metabolites seen on MRS as markers of change in cognitive status after carotid artery surgery. MRS and neurocognitive testing were obtained before and after surgery in 100 patients. The results showed that cognition remained unchanged in 80%, improved in 10%, and was impaired in 10% of patients postoperatively and that in these last 2 groups, NAA/Cr correlated well the clinical status. Thus, NAA/Cr may serve as a marker of neurologic status after carotid artery surgery (see accompanying editorial by Lövblad and Pereira). BACKGROUND AND PURPOSE: Cognitive function can improve or decline after carotid endarterectomy. Proton MR spectroscopy can be used evaluate cerebral metabolites, such as N-acetylaspartate, choline, and creatine, in vivo. The purpose of the present study was to determine whether postoperative changes in cerebral metabolites measured by using 3T proton MR spectroscopy were associated with changes in cognitive function after CEA. MATERIALS AND METHODS: In 100 patients undergoing CEA for ipsilateral cervical internal carotid artery stenosis (≥70%), brain proton MR spectroscopy was performed before and after surgery. NAA/Cr and Cho/Cr ratios were measured in regions of interest placed in the centrum semiovale of both cerebral hemispheres. Neuropsychological testing was also performed preoperatively and 1 month postoperatively. Multivariate statistical analysis of factors related to postoperatively changed cognition was performed, and odds ratios with 95% confidence intervals were calculated. RESULTS: On the basis of the neuropsychological assessments, 10 (10%), 80 (80%), and 10 (10%) patients were defined as having postoperatively improved, unchanged, and impaired cognition, respectively. A positive and high ΔNAA/Cr ratio (postoperative value–preoperative value) in the cerebral hemisphere ipsilateral to the operative site was significantly associated with postoperatively improved cognition (95% CI, 13.3–21.3; P = .0016). Negative and high absolute values of the ΔNAA/Cr ratio (95% CI, 0.018–0.101; P = .0039) and ΔCho/Cr ratio (95% CI, 0.042–0.135; P = .0046) in the ipsilateral cerebral hemisphere were significantly associated with postoperatively impaired cognition. CONCLUSIONS: Postoperative changes in cerebral metabolites measured by using proton MR spectroscopy were associated with changes in cognitive function after CEA.
Towards efficient heart rate variability estimation in artifact-induced Photoplethysmography signals

Heart Rate Variability (HRV) has become a marker for various health and disease conditions. Photoplethysmography (PPG) sensors integrated in wearable/portable devices such as smart watches and phones are widely used to measure heart activities. HRV requires accurate estimation of time interval between consecutive peaks in the PPG signal. Artifacts often degrade the quality of the PPG signal, which could lead to wrong HRV estimation. In this paper, we present an adaptive real-time approach that employs Linear Prediction analysis (LPC) and Wavelet transformation techniques for estimating HRV from PPG signal recorded by wearable devices. Our algorithm outperforms two other related algorithms, especially for low PPG signal to noise ratio. By comparing the proposed algorithm to the ground truth recorded simultaneously from ECG, an average temporal resolution of 8.7 ms was achieved with a sensitivity of 82.9% and a positive predictive value of 82.7%.
Drought frequency analysis of annual rainfall series in central and western Sudan

Abstract Rainfall is the most important water resource in central and western Sudan, a region affected by the recent drought in Africa. A general methodology for studying the annual rainfall process is presented and applied to data from central and western Sudan. It is assumed that certain time series models adequately describe the annual rainfall process in the region. Based on this assumption, the drought frequencies are calculated in the subregions with stationary series. The theory of runs is applied in calculating drought frequencies using a data generation method.
Hidden Genes: Understanding cancer data with matrix factorization

B iotechnological innovations in the last decade have enabled systematic large-scale cancer genome studies. Cancer projects such as The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) coordinate the generation of catalogues of tumors from more than 50 different cancer types that are of clinical importance across the globe [1]. These catalogues contain comprehensive data of more than 25,000 cancer genomes at a high, often at a single nucleotide, resolution level. The genomes of individual cancers are accompanied by the analyses of transcriptome, epigenome, and sequence variation. Furthermore, where possible, measurements are provided for matched tumor and nontumor tissue to differentiate inherited from oncogenic variation. Diversity and abundance of data provided by the cancer projects challenge computer scientists of all kinds to develop innovative software, hardware, and analytic solutions for data analysis. The hope is that with computationally and statistically stronger approaches we will be able to reveal biological features that drive cancer development, define cancer types relevant for prognosis, and, ultimately, enable the development of new cancer therapies. Here, we analyze a transcriptome data set on breast cancer obtained from the ICGC. The analysis relies on a popular data mining technique, called nonnegative matrix factorization, to find groups of genes with similar activation levels across patients.
Determining the relationship between financial development and economic growth: An application of ARDL technique to Singapore

The relationship between financial development and economic growth has been subject to considerable debate in the literature of development and growth. While empirical studies often provide a direct relationship between financial development proxies and growth, much controversy remains about how these results should be interpreted. The study, therefore, attempts to unravel the causality direction of financial development and economic growth. We used an Autoregressive Distributed Lag (ARDL) method to assess the finance-growth relation taking Gross National Expenditure, Gross Fixed Capital Formation, exports, Foreign Direct Investments and Loans made to the Private Sector as financial development indicators for Singapore over the period from 1970 to 2013. Interestingly, we found that our financial development variables had no impact on economic growth.
Market efficiency and the role of information : an experimental analysis

The purpose of this research is to gain additional insight concerning the highly efficient market outcomes generated under the rules of trade of the double auction, in which traders have the dual role of both buyer and seller and can simultaneously call out offers to buy and sell. It is conjectured that the experimental literature’s robust results detailing the efficiency of the double auction institution may be a product of the constant and known duration of trade incorporated in previous experimental designs. In one of the few relevant theoretical discussions Friedman (1984, p.71) suggests that the predetermined, known time at which trade will cease is one of a number of institutional features of experimental double auction markets that enhance the efficiency of observed market outcomes. Known trading duration may well be a key variable in the determination of the price formation process and the convergence to competitive equilibrium in the double auction institution. This study extends previous work by conducting a series of experiments designed to determine the importance of trading duration on the convergence tendencies of experimental asset markets governed by the rules of the double auction institution. The issue is of substantive theoretical and practical interest. The results of this study offer a number of conclusions. Aggregated across the eighteen experimental asset markets studied, transaction prices tend to exhibit convergence to competitive outcomes. Importantly, the etlect of known period duration on observed market behaviour is significant. Experimental asset markets that incorporate uncertain trading durations display more aggressive trading strategies. This is evidenced by an increase in the rate of trade relative to markets where the duration of trade varies but is known. The markets with uncertain trading durations also exhibit reduced levels of market efficiency relative to the other markets studied. The implication is clear, any future refinement of either theoretical models or institutions of exchange must explicitly recognise the effect of uncertain trading duration on market behaviour in double auctions.
On the design of heterogeneous sensor networks based on small world concepts

Data communication may have a strong impact on the design of a wireless sensor network (WSN) since the energy cost related to the data transmission is typically much higher than the energy cost to perform data processing. Typically, data communication in a WSN tends to be different from other "traditional" data networks such as the Internet. In a WSN, there is a special node called sink that is either the origin or the destination of a message whereas, in the other networks, data communication happens between arbitrary communicating entities. In this scenario, the latency and the energy consumption during communication between the sink node and the other nodes can be optimized using Heterogeneous Sensor Networks (HSNs) created by applying the small world concepts. In order to generate a network with small world features, one should add a small number of long-range links, called shortcuts, whose endpoints are equipped with more powerful hardware, mainly the energy resources. In this work, we propose and evaluate two small world models that can be used in the design of HSNs. In particular, the directed angulation toward the sink node model exhibits the most interesting tradeoff between energy consumption and latency and the sink node as source/destination model presents the best results related to energy consumption and latency in the nodes near to the sink.
Towards an Emotion Labeling Model to Detect Emotions in Educational Discourse

In a collaborative e-learning environment, students are usually involved in learning activities such as collaborative writing (e.g., an essay in Wiki form) and debates in the form of a chat or asynchronous forum. Capturing students' feelings and emotional states enclosed in such textual information is important, since sentiments can influence individual behavior and consequently group dynamics as well as individual and group performance. For this reason, emotion awareness becomes an important issue for both tutors and students themselves. Especially for tutors, if they have as accurate affective information as possible about the emotional state that every individual and group is encountered, they might be able to provide more effective feedback to the students that includes not only cognitive but also affective scaffold. A first important step to this endeavor is to develop an approach that enables labeling affective behavior and then presenting the emotional information to all the actors involved in the learning process.
The Hek Outer Membrane Protein of Escherichia coli Strain RS218 Binds to Proteoglycan and Utilizes a Single Extracellular Loop for Adherence, Invasion, and Autoaggregation

ABSTRACT Escherichia coli is the principal gram-negative causative agent of sepsis and meningitis in neonates. The pathogenesis of meningitis due to E. coli K1 involves mucosal colonization, transcytosis of epithelial cells, survival in the bloodstream, and eventually invasion of the meninges. The last two aspects have been well characterized at a molecular level. Less is known about the early stages of pathogenesis, i.e., adhesion to and invasion of epithelial cells. We have previously reported that the Hek protein causes autoaggregation and can mediate adherence to and invasion of epithelial cells. Here, we report that Hek-mediated adherence is dependent on binding to glycosoaminoglycan, in particular, heparin. The ability to hemagglutinate, autoaggregate, adhere, and invade is contingent on a putative 25-amino-acid loop that is exposed to the outside of the bacterial cells.
Environmental tendencies and significant life experiences of municipal sustainability coordinators in Canada

This study explored the demographics, environmental tendencies, and significant life experiences of 91 Sustainability Coordinators employed by municipalities across Canada. The intent of this research was to find a common set of activities that may have motivated these individuals, and may motivate others, to take on roles and practical challenges in the realm of sustainability. A mixed methods approach was conducted which was inclusive of a literature review, an online survey (which acquired demographic info, three environmentally themed psychological scales, and an open ended question of autobiographical recollections), and statistical analysis. The results paralleled findings of other researchers on similar environmental groups, where gender was the predominant predictor of environmental tendency and that various experiences in natural areas are what these individuals attribute these pro-environmental tendencies to. This research will add to the minimal literature available on this emerging and undocumented occupation. MUNICIPAL SUSTAINABILITY COORDINATORS 3
Screening Compounds with a Novel High-Throughput ABCB1-Mediated Efflux Assay Identifies Drugs with Known Therapeutic Targets at Risk for Multidrug Resistance Interference

ABCB1, also known as P-glycoprotein (P-gp) or multidrug resistance protein 1 (MDR1), is a membrane-associated multidrug transporter of the ATP-binding cassette (ABC) transporter family. It is one of the most widely studied transporters that enable cancer cells to develop drug resistance. Reliable high-throughput assays that can identify compounds that interact with ABCB1 are crucial for developing new therapeutic drugs. A high-throughput assay for measuring ABCB1-mediated calcein AM efflux was developed using a fluorescent and phase-contrast live cell imaging system. This assay demonstrated the time- and dose-dependent accumulation of fluorescent calcein in ABCB1-overexpressing KB-V1 cells. Validation of the assay was performed with known ABCB1 inhibitors, XR9576, verapamil, and cyclosporin A, all of which displayed dose-dependent inhibition of ABCB1-mediated calcein AM efflux in this assay. Phase-contrast and fluorescent images taken by the imaging system provided additional opportunities for evaluating compounds that are cytotoxic or produce false positive signals. Compounds with known therapeutic targets and a kinase inhibitor library were screened. The assay identified multiple agents as inhibitors of ABCB1-mediated efflux and is highly reproducible. Among compounds identified as ABCB1 inhibitors, BEZ235, BI 2536, IKK 16, and ispinesib were further evaluated. The four compounds inhibited calcein AM efflux in a dose-dependent manner and were also active in the flow cytometry-based calcein AM efflux assay. BEZ235, BI 2536, and IKK 16 also successfully inhibited the labeling of ABCB1 with radiolabeled photoaffinity substrate [125I]iodoarylazidoprazosin. Inhibition of ABCB1 with XR9576 and cyclosporin A enhanced the cytotoxicity of BI 2536 to ABCB1-overexpressing cancer cells, HCT-15-Pgp, and decreased the IC50 value of BI 2536 by several orders of magnitude. This efficient, reliable, and simple high-throughput assay has identified ABCB1 substrates/inhibitors that may influence drug potency or drug-drug interactions and predict multidrug resistance in clinical treatment.