Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution via recombination: Cell-to-cell contact facilitates larger recombination events in Streptococcus pneumoniae

Homologous recombination in the genetic transformation model organism Streptococcus pneumoniae is thought to be important in the adaptation and evolution of this pathogen. While competent pneumococci are able to scavenge DNA added to laboratory cultures, large-scale transfers of multiple kb are rare under these conditions. We used whole genome sequencing (WGS) to map transfers in recombinants arising from contact of competent cells with non-competent ‘target’ cells, using strains with known genomes, distinguished by a total of ~16,000 SNPs. Experiments designed to explore the effect of environment on large scale recombination events used saturating purified donor DNA, short-term cell assemblages on Millipore filters, and mature biofilm mixed cultures. WGS of 22 recombinants for each environment mapped all SNPs that were identical between the recombinant and the donor but not the recipient. The mean recombination event size was found to be significantly larger in cell-to-cell contact cultures (4051 bp in filter assemblage and 3938 bp in biofilm co-culture versus 1815 bp with saturating DNA). Up to 5.8% of the genome was transferred, through 20 recombination events, to a single recipient, with the largest single event incorporating 29,971 bp. We also found that some recombination events are clustered, that these clusters are more likely to occur in cell-to-cell contact environments, and that they cause significantly increased linkage of genes as far apart as 60,000 bp. We conclude that pneumococcal evolution through homologous recombination is more likely to occur on a larger scale in environments that permit cell-to-cell contact.
Fast back-projection autofocus for linear array SAR 3-D imaging via maximum sharpness

Autofocusing technique is an indispensable step for linear array SAR (LASAR) three-dimensional (3-D) high quality imaging. However, the existing autofocus methods used for LASAR usually suffer from low accuracy or heavy computation burden, due to the non-uniform antenna phase centers (APC) and the huge data of LASAR. In this paper, a novel fast back-projection autofocusing algorithm via maximum sharpness (BP-FMSA), is proposed for LASAR 3-D autofocus imaging. In the scheme, the autofocus model via maximum sharpness back-projection is constructed, and the expression of phase error estimation is derived. Then, the conjugate gradient method is introduced to efficiency estimate the phase errors of LASAR. Moreover, to further improve the computing efficiency of BP-FMSA, only the area consisted by dominant scatterers are selected as the input of sharpness estimation. The effectiveness of the algorithm is demonstrated with both simulation and experimental examples.
Matching Descriptions to Spatial Entities Using a Siamese Hierarchical Attention Network

Spatial entity descriptions are written in natural language based on the observations and understanding of spatial entities and often contain rich semantic information beyond GIS systems. Therefore, entity descriptions must be related to spatial entities in GIS systems. However, most previous studies of this issue were confined to place-type spatial entities, and other types of instance-level spatial entity matching have rarely been studied. In addition, existing matching methods require complex semantic analysis and manual feature engineering for the description text. In this paper, we focus on the matching of semantic similarity between spatial entities with rich text attributes and descriptions. We propose a semantic textual similarity matching model that incorporates a hierarchical recurrent structure with a focus on learning low-dimensional semantic vector representations of spatial entities and the corresponding descriptions. The model minimizes the distance between the vectors of matched pairs and maximizes the distance between the mismatched pairs of samples. The proposed siamese hierarchical attention network is trained and evaluated using a geological survey data set. The results show that the proposed model effectively captures the salient semantic information of spatial entities and the associated descriptions in the matching task and significantly outperforms previous state-of-the-art matching models.
Spoken query for Qur'anic verse information retrieval

Most of information retrieval (IR) systems for Qur'an use text as their input query, whether they use the Alphabetic script or the Arabic script to represent the query. Thus, required IR user to know how to write the query. For searching the Qur'an verses, it is possible that IR user knows how to pronounce the query, but does not have enough knowledge about how to write Arabic letters to represent the query when search for a Qur'an verse. In this case, speech can be an alternative as the input to the IR system. In this work, we develop a spoken query IR based on the Hidden Markov Model acoustic models and the n- gram language model for its automatic speech recognition system. Both models are trained by using all verses of the Qur'an. The Inference Network Model and the well-known Vector Space Model are employed for its IR system. For the speech recognition system, average of word error rate are 7.41% for closed speakers, and 18.53% for open speakers. For the IR system, the best query formulation for the Inference Network is achieved by using input queries consisting of phrase of 2 words with the average value of Mean Reciprocal Rank is 0,922475, while for the Vector Space Model is achieved by using input query consisting of one word with the average value of Mean Reciprocal Rank is 0,9308.
Cyclophilin D regulates neuronal activity‐induced filopodiagenesis by fine‐tuning dendritic mitochondrial calcium dynamics

Recent studies have highlighted the role of mitochondria in dendritic protrusion growth and plasticity. However, the detailed mechanisms that mitochondria regulate dendritic filopodia morphogenesis remain elusive. Cyclophilin D (CypD, gene name: Ppif) controls the opening of mitochondrial permeability transition pore. Although the pathological relevance of CypD has been intensively investigated, little is known about its physiological function in neurons. Here, we have found that genetic depletion of or pharmaceutical inhibition of CypD blunts the outgrowth of dendritic filopodia in response to KCl‐stimulated neuronal depolarization. Further cell biological studies suggest that such inhibitory effect of CypD loss‐of‐function is closely associated with compromised flexibility of dendritic mitochondrial calcium regulation during neuronal depolarization, as well as the resultant changes in intradendritic calcium homeostasis, calcium signaling activation, dendritic mitochondrial motility and redistribution. Interestingly, loss of CypD attenuates oxidative stress‐induced mitochondrial calcium perturbations and dendritic protrusion injury. Therefore, our study has revealed the physiological function of CypD in dendritic plasticity by acting as a fine‐tuner of mitochondrial calcium homeostasis. Moreover, CypD plays distinct roles in neuronal physiology and pathology.
Network Throughput Optimization for Random Access Narrowband Cognitive Radio Internet of Things (NB-CR-IoT)

Narrowband Internet of Things (NB-IoT) is a new technology being implemented into the Long-Term Evolution (LTE) standards to support machine-to-machine communications. Applications of IoT are expected to rapidly increase and to expand into many sectors. Increased number of IoT devices trying to access the medium with typically small data packets will overwhelm the network. Cognitive radios coupled with random access strategies can help alleviate excessive collisions in the network by many IoT access attempts. On the other hand, most IoT devices do not have hardware capabilities to perform sophisticated spectrum sensing methods over long durations. Therefore, it is important to reduce the spectrum sensing overhead while maximizing the NB-IoT network level throughput. In this paper, we derive a unique set of optimal sensing parameters which achieve the maximum throughput of a narrowband cognitive radio IoT (NB-CR-IoT) network. We show that accurate sensing from a single device perspective is not always the best solution for maximizing the throughput in an NB-CR-IoT network.
Coarse to fine global RGB-D frames registration for precise indoor 3D model reconstruction

The conventional approach to register two or more RGB-D frames produced from low cost depth sensors, such as KINECT and Structure Sensor, applies SIFT matched points between color images along with corresponding depth from the depth images. This is known as RGB-D SLAM. This method depends on ICP concept to refine the sensor pose after estimating it from SIFT depth points. In this research, we propose a new registration method and a new description function to add line feature matching in RGB-D frame registration. The qualitative and quantitative assessments of the proposed procedure show a significant improvement in 3D model quality and precision with the proposed new registration method.
Model predictive control of a two-link robot arm

This paper presents a new approach for a model predictive control dynamics of a two-link manipulator robot. This technique consists of linearizing a nonlinear dynamic model of the robot by using a feedback linearization control. Once, the linear model has been obtained, a predictive control approach will be developed. We have introduced a quadratic criterion and these parameters are calculated to have a specific behavior of the closed loop system. Here, the objective is to control the arm robot from an initial configuration to the final configuration using a predictive control approach and it is obtained by minimizing a quadratic criterion. In order to show the efficiency of the proposed method, some simulation results are given.
Matlab applications to generate synthetic electricity load profiles of office buildings and detached houses

In this paper we present two MATLAB applications that generate synthetic electricity load profiles for office buildings and detached houses down to 1-minute resolution. The applications have been developed using App Designer, a MATLAB environment for application development. The applications are based on consumer load models for office buildings and detached houses published in previous research work. The aim of this paper is to present an overview of the application functionalities, code design, assumptions and limitations, and examples of their potential use in power system education and research. To the author's knowledge these are the first applications which allow generating synthetic load profiles for office buildings and houses in practical and intuitive manner where building attributes can be easily configured.
Effect of Unpredictable Interference on MU-MIMO Systems in HetNet

In this paper, we investigate the outage probability of a multiuser multiple-input multiple-output (MU-MIMO) system in the heterogeneous network (HetNet) in the presence of unpredictable interference (UI) from coexisting ad hoc systems. In order to achieve multiuser diversity gain, the MU-MIMO system requires feedback carrying the signal-to-interference-plus-noise ratio (SINR) from all active users to the base station. However, the coexisting ad hoc systems in HetNet can unpredictably initiate their data transmission during the SINR feedback duration. This unpredictable behavior by the coexisting ad hoc systems causes a mismatch between the measured SINR and the instantaneous SINR. In order to investigate the performance of the MU-MIMO system in HetNets, we first categorize the interferences into predictable interference and UI. Based on this categorization, we analyze the outage probability of an MU-MIMO system using a max-SINR scheduler. The theoretical analyses show that the MU-MIMO system cannot achieve the maximum diversity gain, since the UI from the coexisting systems causes beam selection mismatch. Additionally, we show that reducing the interval between the measurement and data transmission prevents performance degradation by reducing the effect of the unpredictable behavior of the coexisting ad hoc systems.
Scalable Minimum-Cost Balanced Partitioning of Large-Scale Social Networks: Online and Offline Solutions

With the remarkable proliferation of intelligent mobile devices and fast growing broadband wireless technology, social networking is undergoing explosive growth in recent years as more and more users access social networks via mobile platforms. It is often expensive or even impossible to deploy a large online social network (OSN) on a single server. A cost-effective approach is horizontal scaling, where the OSN is partitioned and deployed on a set of low-cost servers. In this research, we study the problem of minimum-cost balanced partitioning of OSNs. Our goal is to achieve the best partitioning by minimizing the total inter-server traffic cost and at the same time balancing the load among servers. Given its NP-hardness, we propose new techniques and explore efficient heuristics to address the problem, especially for extremely large OSNs with an enormous volume of social nodes, social connections, and social data. Our key contributions include a localized approach with $O(\delta ^2)$ complexity to explicitly calculate the projected gain in inter-server traffic cost (named Server Change Benefit (SCB)). Built upon this technique, we devise two algorithms that offer online and offline solutions to achieving minimum-cost balanced partitioning of OSNs. The online algorithm is fast and highly efficient to process newly arrival individual nodes. The offline algorithm uses the current online result as a starting point. It further reduces inter-server traffic cost by applying relocation and swapping. It employs a merging process to group the nodes according to the social structure and swap the groups with similar size to further reduce the total inter-server traffic cost. We implement both algorithms and evaluate them based on a variety of real-world OSN datasets from Facebook, Arxiv, Gnutella, Amazon, and Twitter. The simulations demonstrate that the proposed algorithms can significantly reduce the execution time by an average of three folds and at the same time yield supreme performance (i.e., inter-server traffic cost) in comparison with existing solutions.
Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy

Background Convolutional neural networks (CNNs) have been shown to be powerful tools to assist with object detection and—like a human observer—may be trained based on a relatively small cohort of reference subjects. Rapid, accurate organ recognition in medical imaging permits a variety of new quantitative diagnostic techniques. In the case of therapy with targeted radionuclides, it may permit comprehensive radiation dose analysis in a manner that would often be prohibitively time-consuming using conventional methods. Methods An automated image segmentation tool was developed based on three-dimensional CNNs to detect right and left kidney contours on non-contrast CT images. Model was trained based on 89 manually contoured cases and tested on a cohort of patients receiving therapy with 177Lu-prostate-specific membrane antigen-617 for metastatic prostate cancer. Automatically generated contours were compared with those drawn by an expert and assessed for similarity based on dice score, mean distance-to-agreement, and total segmented volume. Further, the contours were applied to voxel dose maps computed from post-treatment quantitative SPECT imaging to estimate renal radiation dose from therapy. Results Neural network segmentation was able to identify right and left kidneys in all patients with a high degree of accuracy. The system was integrated into the hospital image database, returning contours for a selected study in approximately 90 s. Mean dice score was 0.91 and 0.86 for right and left kidneys, respectively. Poor performance was observed in three patients with cystic kidneys of which only few were included in the training data. No significant difference in mean radiation absorbed dose was observed between the manual and automated algorithms. Conclusion Automated contouring using CNNs shows promise in providing quantitative assessment of functional SPECT and possibly PET images; in this case demonstrating comparable accuracy for radiation dose interpretation in unsealed source therapy relative to a human observer.
Adaptive beamforming algorithms performance evaluation for active array radars

The capability to maximize the signal of interest while minimizing possible interferences that could be present in operational environments is crucial for modern active array antenna radars. The use of active array antennas allows to sample signals in the proximity of the antenna by using high speed Analogue to Digital Converters (ADC). The digital signal processing carried out afterwards can find the optimum weights for every receiving channel to maximize the signal of interest while forming nulls in the antenna array pattern to suppress interferences either in the sidelobes or in the main beam. Before the advent of digitally scanned array antennas the possibility to implement digital beamforming algorithms was confined to digital signal processing on formed beams. Today, the design of radar systems equipped with hundreds of receiving channels each with a dedicated ADC, makes it feasible to accomplish adaptive digital beamforming algorithms before the beams' formation. Valuable capabilities for nowadays active array multifunctional radars can be summed up as follows: (i) maximization of the signal of interest while adaptively nulling possible multiple interferences, (ii) low sidelobes for clutter rejection, (iii) good directivity for the main beam lobe and good performances even in presence of phase and amplitude mismatches in the receivers. This article examines and compares the performances of various digital Adaptive Beam Forming (ABF) algorithms to determine which of them could be successfully employed in modern Active Electronically Scanned Array (AESA) radar and performs best in harsh environments such as those usually found where radars operate.
Simulating game playing to solve Max-SAT

The Max-SAT can naturally model many practical optimization problems. In this paper, we propose an incomplete Max-SAT algorithm bgmaxsat, which utilizes boolean game to model and solve Max-SAT problems. Then we improve bgmaxsat by adding a new agents grouping criterion to solve massive instances, called bgmaxsat_m. In order to obtain quality solution quickly, we design two mechanisms to accelerate the game playing. Finally, we evaluate our algorithms against with the state-of-the-art SLS-based algorithms CCLS and Swcca-ms on 87 selected Max-SAT instances. The experimental results show that bgmaxsat finds optimal solutions on 65(65) general instances with competitive CPU time, and bgmaxsat_m finds better solutions on 19(22) massive instances with less CPU time.
Development of bio-shields for Arduino Uno

The main purpose of this project is to design and implement a low cost Arduino bio-shield. Bio-shields are modular circuit boards that can be mounted on top of the Arduino kit in order to enhance its capabilities. Proposed shield can be used for the acquisition of bio-electric signals such ECG, EMG, EEG and EOG with the help of an Arduino Uno. Scope of this project includes the design of bio-amplifiers according to the bio-signal characteristics and practical implementation including fabrication of printed circuit boards. Three bio-shields (ECG, EMG and EOG) have been designed and implemented using surface mounted components.
Interference-aware distributed control of cell discontinuous transmission

As a main enabler for the next generation (5G) cellular networks, network densification faces challenges in inter-cell interference and energy consumption. Cell discontinuous transmission (DTX) can be employed to reduce both energy consumption of base stations (BSs) and inter-cell interference. In this paper we study the control problem of cell DTX in dense small cell networks (DSCNs). We firstly formulate the network energy efficiency optimization problem. Then a centralized heuristic DTX control algorithm is presented. In order to address the issues of complexity and scalability of the centralized solution, an interference-aware distributed DTX control algorithm is proposed. Discussions on algorithm complexity and implementation are provided. The proposed algorithms are evaluated with numerical simulations. Results show that at high load region, the proposed algorithms can not only enhance network capacity by reducing inter-cell interference by up to 60% but also increase network energy efficiency by switching BSs into micro-sleep mode by 67%.
Doppler ambiguity resolution using random slow-time code division multiple access MIMO radar with sparse signal processing

In this article a novel waveform, Random Slow-Time Code Division Multiple Access (ST-CDMA) for MIMO radar is presented and investigated. ST-CDMA means that the waveforms of the different transmitters are orthogonal per burst, while conventional CDMA implies orthogonal waveforms of the different transmitters per pulse. In particular, the Doppler ambiguity performance of the proposed Random ST-CDMA waveform is compared to that of the well-known Doppler Division Multiple Access (DDMA) waveform, which is a specific type of ST-CDMA. Both in combination with conventional processing (based on matched filters) and with Sparse Signal Processing (SSP) the DDMA waveform yields a reduction in unambiguous Doppler range as compared to other MIMO waveforms, such as CDMA. Instead, the Random ST-CDMA waveform together with SSP yields no reduction in unambiguous Doppler Range and improved side lobe suppression.
Classification of alcohol brand logos using convolutional neural networks

In Thailand, intentionally posting pictures with alcohol logos in social media is a violation of the Alcohol Control Act and should face tough legal action by police officer. Thailand also remains in top countries for social media. Hence, we need an automated approach for classification of alcohol brand logos in large numbers of pictures posting in social media. Recently, convolutional neural networks (CNNs) have been widely adopted to recognize logos in images. In this work, a CNNs model is built to classify whether the input image is alcohol or non-alcohol logo. In case of classifying as alcohol logo, its corresponded alcohol brand is provided. Experimental results show that our CNNs model yields better results in terms of precision and recall compared to the all-in-one CNNs model.
Joint cmWave-based multiuser positioning and network synchronization in dense 5G networks

The expected fifth generation (5G) networks allow for highly accurate direction of arrival and time of arrival (ToA) estimation, thus providing a convenient environment for device positioning, if designed properly. However, utilizing ToA measurements for positioning requires a tight synchronization not only between the target devices but also among the network elements. In this paper, we propose a joint positioning and synchronization solution building on the premises of the envisioned cmWave-based 5G ultra-dense networks and time-varying clock models. In addition to device location estimates, also relative clock offsets and skews are estimated and tracked within the proposed extended Kalman filter based solutions, which can be further used by a network operator in synchronizing the active network elements and devices within the network. Based on extensive simulations and numerical evaluations, accurate positioning performance can be achieved while tracking the clock parameters under time-varying clock errors.