Miracle-dz/UniFAR-FTUs huggingface dataset:
QoS-aware downlink radio resource management in OFDMA-based small cells networks

Interference management and supporting quality-of-service (QoS) requirements are the main challenges for the spectral resource allocation in orthogonal frequency division multiple access (OFDMA) small cells networks. In this study, the authors propose an algorithm for assigning physical resource block (PRB) with QoS constraints to eliminate interference among femtocells. They formulate the proposed resource allocation scheme as an optimisation problem where interference among femtocells is entirely avoided and multiple QoS for different services are supported. The proposed optimisation problem imposes the fairness among different femtocells and maximises the PRB efficiency. However, this problem is NP-complete, and thus, they offer a greedy algorithm for solving the problem. Simulation results show the throughput of their greedy algorithm is close to the optimal solution of the optimisation problem. Moreover, the proposed algorithm improves the network throughput by over 30–50% in different simulation scenarios with various femtocells densities in comparison to the previous methods. Furthermore, simulations illustrate the rejection ratio for all classes of services is lower than 3 % . Besides, the delays of different services are lower than the delay constraints.
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes

Gaussian Process bandit optimization has emerged as a powerful tool for optimizing noisy black box functions. One example in machine learning is hyper-parameter optimization where each evaluation of the target function may require training a model which may involve days or even weeks of computation. Most methods for this so-called "Bayesian optimization" only allow sequential exploration of the parameter space. However, it is often desirable to propose batches or sets of parameter values to explore simultaneously, especially when there are large parallel processing facilities at our disposal. Batch methods require modeling the interaction between the different evaluations in the batch, which can be expensive in complex scenarios. In this paper, we propose a new approach for parallelizing Bayesian optimization by modeling the diversity of a batch via Determinantal point processes (DPPs) whose kernels are learned automatically. This allows us to generalize a previous result as well as prove better regret bounds based on DPP sampling. Our experiments on a variety of synthetic and real-world robotics and hyper-parameter optimization tasks indicate that our DPP-based methods, especially those based on DPP sampling, outperform state-of-the-art methods.
The potential of clear-sky carbon dioxide satellite retrievals

Abstract. Since the launch of the Greenhouse Gases Observing Satellite (GOSAT) in 2009, retrieval algorithms designed to infer the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from hyperspectral near-infrared observations of reflected sunlight have been greatly improved. They now generally include the scattering effects of clouds and aerosols, as early work found that absorption-only retrievals, which neglected these effects, often incurred unacceptably large errors, even for scenes with optically thin cloud or aerosol layers. However, these “full-physics” retrievals tend to be computationally expensive and may incur biases from trying to deduce the properties of clouds and aerosols when there are none present. Additionally, algorithms are now available that can quickly and effectively identify and remove most scenes in which cloud or aerosol scattering plays a significant role. In this work, we test the hypothesis that non-scattering, or “clear-sky”, retrievals may perform as well as full-physics retrievals for sufficiently clear scenes. Clear-sky retrievals could potentially avoid errors and biases brought about by trying to infer properties of clouds and aerosols when none are present. Clear-sky retrievals are also desirable because they are orders of magnitude faster than full-physics retrievals. Here we use a simplified version of the Atmospheric Carbon Observations from Space (ACOS) XCO2 retrieval algorithm that does not include the scattering and absorption effects of clouds or aerosols. It was found that for simulated Orbiting Carbon Observatory-2 (OCO-2) measurements, the clear-sky retrieval had errors comparable to those of the full-physics retrieval. For real GOSAT data, the clear-sky retrieval had errors 0–20 % larger than the full-physics retrieval over land and errors roughly 20–35 % larger over ocean, depending on filtration level. In general, the clear-sky retrieval had XCO2 root-mean-square errors (RMSEs) of less than 2.0 ppm, relative to Total Carbon Column Observing Network (TCCON) measurements and a suite of CO2 models, when adequately filtered through the use of a custom genetic algorithm filtering system. These results imply that non-scattering XCO2 retrievals are potentially more useful than previous literature suggests, as the filtering methods we employ are able to remove measurements in which scattering can cause significant errors. Additionally, the computational benefits of non-scattering retrievals means they may be useful for certain applications that require large amounts of data but have less stringent error requirements.
Combined use of vancomycin-modified Ag-coated magnetic nanoparticles and secondary enhanced nanoparticles for rapid surface-enhanced Raman scattering detection of bacteria

Background Pathogenic bacteria have always been a significant threat to human health. The detection of pathogens needs to be rapid, accurate, and convenient. Methods We present a sensitive surface-enhanced Raman scattering (SERS) biosensor based on the combination of vancomycin-modified Ag-coated magnetic nanoparticles (Fe3O4@Ag-Van MNPs) and Au@Ag nanoparticles (NPs) that can effectively capture and discriminate bacterial pathogens from solution. The high-performance Fe3O4@Ag MNPs were modified with vancomycin and used as bacteria capturer for magnetic separation and enrichment. The modified MNPS were found to exhibit strong affinity with a broad range of Gram-positive and Gram-negative bacteria. After separating and rinsing bacteria, Fe3O4@Ag-Van MNPs and Au@Ag NPs were synergistically used to construct a very large number of hot spots on bacteria cells, leading to ultrasensitive SERS detection. Results The dominant merits of our dual enhanced strategy included high bacterial-capture efficiency (>65%) within a wide pH range (pH 3.0–11.0), a short assay time (<30 min), and a low detection limit (5×102 cells/mL). Moreover, the spiked tests show that this method is still valid in milk and blood samples. Owing to these capabilities, the combined system enabled the sensitive and specific discrimination of different pathogens in complex solution, as verified by its detection of Gram-positive bacterium Escherichia coli, Gram-positive bacterium Staphylococcus aureus, and methicillin-resistant S. aureus. Conclusion This method has great potential for field applications in food safety, environmental monitoring, and infectious disease diagnosis.
Predicting Resistance to Piperacillin-Tazobactam, Cefepime and Meropenem in Septic Patients With Bloodstream Infection Due to Gram-Negative Bacteria

Background Predicting antimicrobial resistance in gram-negative bacteria (GNB) could balance the need for administering appropriate empiric antibiotics while also minimizing the use of clinically unwarranted broad-spectrum agents. Our objective was to develop a practical prediction rule able to identify patients with GNB infection at low risk for resistance to piperacillin-tazobactam (PT), cefepime (CE), and meropenem (ME). Methods The study included adult patients with sepsis or septic shock due to bloodstream infections caused by GNB admitted between 2008 and 2015 from Barnes-Jewish Hospital. We used multivariable logistic regression analyses to describe risk factors associated with resistance to the antibiotics of interest (PT, CE, and ME). Clinical decision trees were developed using the recursive partitioning algorithm CHAID (χ2 Automatic Interaction Detection). Results The study included 1618 consecutive patients. Prevalence rates for resistance to PT, CE, and ME were 28.6%, 21.8%, and 8.5%, respectively. Prior antibiotic use, nursing home residence, and transfer from an outside hospital were associated with resistance to all 3 antibiotics. Resistance to ME was specifically linked with infection attributed to Pseudomonas or Acinetobacter spp. Discrimination was similar for the multivariable logistic regression and CHAID tree models, with both being better for ME than for PT and CE. Recursive partitioning algorithms separated out 2 clusters with a low probability of ME resistance and 4 with a high probability of PT, CE, and ME resistance. Conclusions With simple variables, clinical decision trees can be used to distinguish patients at low, intermediate, or high risk of resistance to PT, CE, and ME.
Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus

We present MOBISIG, a pseudosignature dataset containing finger-drawn signatures from 83 users captured with a capacitive touchscreen-based mobile device. The database was captured in three sessions resulting in 45 genuine signatures and 20 skilled forgeries for each user. The database was evaluated by two state-of-the-art methods: a function-based system using local features and a feature-based system using global features. Two types of equal error rate computations are performed: one using a global threshold and the other using user-specific thresholds. The lowest equal error rate was 0.01% against random forgeries and 5.81% against skilled forgeries using user-specific thresholds that were computed a posteriori. However, these equal error rates were significantly raised to 1.68% (random forgeries case) and 14.31% (skilled forgeries case) using global thresholds. The same evaluation protocol was performed on the DooDB publicly available dataset. Besides verification performance evaluations conducted on the two finger-drawn datasets, we evaluated the quality of the samples and the users of the two datasets using basic quality measures. The results show that finger-drawn signatures can be used by biometric systems with reasonable accuracy.
On Bandwidth Efficiency Analysis for LS-MIMO With Hardware Impairments

Large scale multiple-input-multiple output (LS-MIMO) systems are one of the most promising candidates for $5^{th}$ generation wireless communication networks. Many researchers have claimed that LS-MIMO systems increase energy efficiency (EE) and bandwidth efficiency (BE) exponentially because of large number of antennas. Significant increase in transmission directivity also increases sum rate and data throughput but implementation of LS-MIMO is complex in terms of hardware. As the number of antenna elements increase, it becomes necessary that the manufacturing material of antenna must be very cheap and size of antenna element should be very compact. Large number of antenna elements and other hardware components induce different impairments in the system due to amplifier distortion, quantization noise, and phase noise. Since these impairments come as a gift with large scale antenna regime and they effect EE and BE of the system, it is necessary to investigate them for practical scenarios. In this paper, we consider a LS-MIMO single cell scenario with the inclusion of hardware impairments. We derive new expressions for MMSE estimator and achievable rates including effects of hardware impairments. We analyze hardware impairments on the basis of derived expressions. We perform simulations using derived sophisticated impairments model with realistic measurements.
Validity of Teleneuropsychological Assessment in Older Patients with Cognitive Disorders

Objective The feasibility and reliability of neuropsychological assessment at a distance have been demonstrated, but the validity of this testing medium has not been adequately demonstrated. The purpose of this study was to determine the ability of video teleconferencing administration of neuropsychological measures (teleneuropsychology) in discriminating cognitively impaired from non-impaired groups of older adults. It was predicted that measures administered via video teleconference would distinguish groups and that the magnitude of differences between impaired and non-impaired groups would be similar to group differences achieved in traditional administration. Methods The sample consisted of 197 older subjects, separated into two groups, with and without cognitive impairment. The cognitive impairment group included 78 individuals with clinical diagnoses of mild cognitive impairment or Alzheimer's disease. All participants completed counterbalanced neuropsychological testing using alternate test forms in both a teleneuropsychology and a traditional face-to-face (FTF) administration condition. Tests were selected based upon their common use in dementia evaluations, brevity, and assessment of multiple cognitive domains. Results from FTF and teleneuropsychology test conditions were compared using individual repeated measures ANCOVA, controlling for age, education, gender, and depression scores. Results All ANCOVA models revealed significant main effects of group and a non-significant interaction between group and administration condition. All ANCOVA models revealed non-significant main effects for administration condition, except category fluency. Conclusions Results derived from teleneuropsychologically administered tests can distinguish between cognitively impaired and non-impaired individuals similar to traditional FTF assessment. This adds to the growing teleneuropsychology literature by supporting the validity of remote assessments in aging populations.
User-Centric 5G Cellular Networks: Resource Allocation and Comparison With the Cell-Free Massive MIMO Approach

Recently, the so-called cell-free (CF) massive multiple-input multiple-output (MIMO) architecture has been introduced, wherein a very large number of distributed access points (APs) simultaneously and jointly serve a much smaller number of mobile stations (MSs). The paper extends the CF approach to the case in which both the APs and the MSs are equipped with multiple antennas, proposing a beamfoming scheme that, relying on the zero-forcing strategy, does not require channel estimation at the MSs. We contrast the originally proposed formulation of CF massive MIMO with a user-centric (UC) approach wherein each MS is served only by a limited number of APs. Exploiting the framework of successive lower-bound maximization, the paper also proposes and analyzes power allocation strategies aimed at either sum-rate maximization or minimum-rate maximization, both for the uplink and downlink. Results show that the UC approach, which requires smaller backhaul overhead and is more scalable that the CF deployment, also achieves generally better performance than the CF approach for the vast majority of the users, especially on the uplink.
A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

Background Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction). Objective Our work was to create a new machine-friendly representation that resembles the semantics of medical concepts. We then developed a sequential predictive model for medical events based on this new representation. Methods We developed novel contextual embedding techniques to combine different medical events (eg, diagnoses, prescriptions, and labs tests). Each medical event is converted into a numerical vector that resembles its “semantics,” via which the similarity between medical events can be easily measured. We developed simple and effective predictive models based on these vectors to predict novel diagnoses. Results We evaluated our sequential prediction model (and standard learning methods) in estimating the risk of potential diseases based on our contextual embedding representation. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79 on chronic systolic heart failure and an average AUC of 0.67 (over the 80 most common diagnoses) using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Conclusions We propose a general early prognosis predictor for 80 different diagnoses. Our method computes numeric representation for each medical event to uncover the potential meaning of those events. Our results demonstrate the efficiency of the proposed method, which will benefit patients and physicians by offering more accurate diagnosis.
Simulating Passivity for Robotic Walkers via Authority-Sharing

We consider a robotic walking assistant used to guide senior users across a crowded space. The problem we address is how to guide the user using motorized back wheels. Our strategy aims to simulate a passive behavior in which the forward velocity is the one imposed by the user, who receives the impression of controlling the motion. The result is obtained leaving the user in control (without any actuation) when she/he follows straight lines, while the motors kick in when the user has to make a turn. We offer extensive theoretical proof of the validity of our strategy. The technique has been validated via extensive experimentation with a large group of older adults.
ER assembly of SNARE complexes mediating formation of partitioning membrane in Arabidopsis cytokinesis

Intracellular membrane fusion mediates diverse processes including cell growth, division and communication. Fusion involves complex formation between SNARE proteins anchored to adjacent membranes. How and in what form interacting SNARE proteins reach their sites of action is virtually unknown. We have addressed this problem in the context of plant cell division in which a large number of TGN-derived membrane vesicles fuse with one another to form the partitioning membrane. Blocking vesicle formation at the TGN revealed cis-SNARE complexes. These inactive cytokinetic SNARE complexes were already assembled at the endoplasmic reticulum and, after passage through Golgi/TGN to the cell division plane, transformed into fusogenic SNARE complexes. This mode of trafficking might ensure delivery of large stoichiometric quantities of SNARE proteins required for forming the partitioning membrane in the narrow time frame of plant cytokinesis. Such long-distance trafficking of inactive SNARE complexes would also facilitate directional growth processes during cell differentiation. DOI: http://dx.doi. org/10.7554/eLife.25327. 001
Tracking Network Events with Write Optimized Data Structures

Access to network traffic records is an integral part of recognizing and addressing network security breaches. Even with the increasing sophistication of network attacks, basic network events such as connections between two IP addresses play an important role in any network defense. Given the duration of current attacks, long-term data archival is critical but typically very little of the data is ever accessed. Previous work has provided tools and identified the need to trace connections. However, traditional databases raise performance concerns as they are optimized for querying rather than ingestion. The study of write-optimized data structures (WODS) is a new and growing field that provides a novel approach to traditional storage structures (e.g., B-trees). WODS trade minor degradations in query performance for significant gains in the ability to quickly insert more data elements, typically on the order of 10 to 100 times more inserts per second. These efficient, out-of-memory data structures can play a critical role in enabling robust, long-term tracking of network events. In this paper, we present TWIAD, the Write-optimized IP Address Database. TWIAD uses a write-optimized B-tree known as a B" tree to track all IP address connections in a network traffic stream. Our initial implementation focuses on utilizing lower cost hardware, demonstrating that basic long-term tracking can be done without advanced equipment. We tested TWIAD on a modest desktop system and showed a sustained ingestion rate of about 20,000 inserts per second.
Staphylococcus epidermidis Protection Against Staphylococcus aureus Colonization in People Living With Human Immunodeficiency Virus in an Inner-City Outpatient Population: A Cross-Sectional Study

Background. People living with human immunodeficiency virus (PLWH) have been disproportionally affected by methicillin-resistant Staphylococcus aureus (MRSA) colonization and infection, in particular by clones USA300 and USA500. However, the contribution of epidemiological, bacterial, and immunological risk factors to the excess of S aureus in PLWH remain incompletely understood. Methods. In this cross-sectional study, we determined the prevalence and molecular epidemiology of S aureus colonization in 93 PLWH attending an urban human immunodeficiency virus (HIV) clinic. Participants completed a structured interview assessing demographic information and risk factors for MRSA. Swabs were obtained from the nose, throat, and groin and cultured for S aureus and Staphylococcus epidermidis. Results. Most participants had well controlled HIV infection (89, 96% CD4 >200). Thirty-six (39%) individuals were colonized with S aureus at 1 or more body sites, including 6 (6%) with MRSA. Regular gym use was a risk factor for S aureus but not MRSA carriage. In contrast, S epidermidis was present in almost all individuals (n = 84, 90%), predominantly in the nares (n = 66, 71%). Using generalized estimating equation models, we observed that the odds of S aureus colonization were significantly and drastically reduced when S epidermidis was detected (P = .0001). After controlling for site, gender, and age, we identified that the odds of S aureus colonization were 80% less if S epidermidis was present (adjusted odds ratio, 0.20; 95% confidence interval, .09–.45; P < .0001). Conclusions. Taken together, we observed a lower prevalence of S aureus and MRSA colonization than has been previously reported in PLWH. In this cohort, colonization with S epidermidis was protective against S aureus colonization.
Automated neuron tracing using probability hypothesis density filtering

Motivation: The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed. Results: Here we present a new method for tracing neuron centerlines needed for full reconstruction. The method uses a fundamentally different approach than previous methods by considering neuron tracing as a Bayesian multi‐object tracking problem. The problem is solved using probability hypothesis density filtering. Results of experiments on 2D and 3D fluorescence microscopy image datasets of real neurons indicate the proposed method performs comparably or even better than the state of the art. Availability and Implementation: Software implementing the proposed neuron tracing method was written in the Java programming language as a plugin for the ImageJ platform. Source code is freely available for non‐commercial use at https://bitbucket.org/miroslavradojevic/phd. Contact: meijering@imagescience.org Supplementary information: Supplementary data are available at Bioinformatics online.