Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and Phylogenetic Analysis of Basic Helix-Loop-Helix Genes in the Diamondback Moth

Abstract Basic helix-loop-helix (bHLH) transcription factors play essential roles in regulating eukaryotic developmental and physiological processes such as neuron generation, myocyte formation, intestinal tissue development, and response to environmental stress. In this study, the diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), genome was found to encode 52 bHLH genes. All 52 P. xylostella bHLH (PxbHLH) genes were classified into correspondent bHLH families according to their orthology with bHLHs from fruit fly and other insect species. Among these 52 PxbHLH genes, 19 have been annotated consistently with our classification in GenBank database. The remaining 33 PxbHLH genes are either annotated as general bHLH genes or as hypothetical genes. Therefore, our data provide useful information for updating annotations to PxbHLH genes. P. xylostella has four stem cell leukemia (SCL) genes (one of them has three copies), two Dys genes, two copies of MyoR, Mitf, and Sima genes, and three copies of Sage genes. Further studies may be conducted to elucidate functions of these specific bHLH genes in regulating P. xylostella growth and development.
A Novel Distributed Q-Learning Based Resource Reservation Framework for Facilitating D2D Content Access Requests in LTE-A Networks

With the rapid increase in mobile data traffic (especially video content traffic), the number of content access requests is also increasing. In this context, device-to-device (D2D) communication has been an effective technology in increasing the spectral efficiency and reducing the load by offloading the mobile data traffic in cellular networks. In order to reduce the load on eNodeBs (eNBs), centralized area controller (CAC) has been proposed earlier to take content aware decisions for content access requests. In this paper, we propose to exploit CAC in a distributed manner. Distributed D2D controller (DDC) is responsible for arbitrating and scheduling D2D transfers. Thus, every content access request from a user equipment (UE) is first served by a neighboring DDC and in case the content is not available in the region, the request is forwarded to the eNB. Further, it has been observed that the inclusion of DDCs in the existing D2D architecture reference model requires the addition of new reference points. The same has been highlighted in this paper. Due to the spatiotemporal correlation in the mobile data traffic, a learning algorithm can be employed that determines the number of required resources to fulfill the D2D data rate requirements. Hence, we propose a Q-learning based algorithm that learns the expected number of resource blocks (RBs) required to meet the data rate requirements at each DDC. In addition, a cache content management policy is proposed that exploits the popularity of the contents in order to increase the chances of D2D communication. With extensive simulations, we observe that the proposed Q-learning algorithm indeed learns by reserving near-optimal number of RBs to serve the data rate requirement at each DDC. Further, we observe that the one- and two-hop modes of D2D transfer effectively reduces the load on eNB by transferring a maximum 49% of the required data to the UEs.
Resource allocation in cache-enabled energy-cooperative HetNets

This paper considers the resource allocation in cache-enabled energy-cooperative HetNets, where base stations (BSs) with different cache sizes are powered by both conventional grids and renewable energy sources, and energy can be shared between BSs via the smart grid. Simulation results demonstrate that the proposed joint user association and power control algorithm can significantly enhance the sum data rate and the energy efficiency of the whole network.
SMEAD: A secured mobile enabled assisting device for diabetics monitoring

Wearable health devices, mobile apps and diagnostic tools revolutionize the medical field by introducing new assisting devices for patients in a way to create comfort, communication and augmented intelligence. Internet of Things involved in this transformation to provide an environment where a patient's vital parameters get transmitted by sensor devices via a gateway onto secure cloud-based platforms where it is stored, aggregated and analyzed. It also helps to store data for millions of patients and performs analysis in real time, ultimately promoting an evidence-based medicine system. Privacy and security are concerns in this environment. Based on the latest trends, this paper introduces a new healthcare paradigm named as SMEAD by developing an end-to-end secured system for assisting diabetic patients. It includes wearables to monitor different parameters thus observe and predict the diabetes status of the patient. The proposed system employs a MEDIBOX which is used to configure the dosage required and provides an alert to the users reminding them to take medication on time. In this case, the insulin dosage is maintained at suitable cooling conditions and is continuously monitored using the mentioned system. To keep all the data secure and to enable access to this data by the doctor and other trusted parties, a Blockchain-based disruptive technology is implemented which facilitates cryptographic security and formalized data access through smart contracts for medical communities. In case of an emergency like missing a dosage, abnormal blood sugar levels or any security lapse, an alert is sent to the caretakers via social networks like Twitter, Facebook or WhatsApp using mobile as a gateway which can continuously communicate the data over the internet that could save patients from fatal effects of the disease.
A semi-automatic approach for the extraction of white matter fiber bundles across subjects

Fig. 3 – For 3 data sets exemplary fiber bundles are presented (Fmaj (magenta), Fmin (cyan), IFOR (light brown), UNCR (dark brown), CSTR (yellow), SLFtR (olive)). Bundles without correction are shown in the top row and bundles that were extracted by performing reliabilitybased classification are shown in the bottom row. A semi-automatic approach for the extraction of white matter fiber bundles across subjects Christian Ros, Daniel Güllmar, Martin Stenzel, Hans-Joachim Mentzel, and Jürgen Rainer Reichenbach Medical Physics Group, Department for Diagnostic and Interventional Radiology I, Jena University Hospital, Jena, Thuringia, Germany, Pediatric Radiology, Department for Diagnostic and Interventional Radiology I, Jena University Hospital, Jena, Thuringia, Germany
State estimation using a destination constraint with uncertainty

The problem of state estimation using a destination constraint with uncertainty is investigated. The existing state estimation method with destination constraints assume that the destination is deterministic and known a priori. However in practical applications, the destination may not always be accu­rate. When the destination is contaminated by noise, the direct incorporation of the destination constraint, without consideration of the noise, into the tracking system may not always lead to performance improvement. To address this problem, a state augmentation approach, which augments the destination into the state vector so that the state and the destination can be estimated simultaneously, is proposed. The constraint relationship between the components in the augmented state can be formulated and a pseudo-measurement is constructed. A corresponding filtering method is provided where the unscented Kalman filter (UKF) is used to deal with this nonlinearity. The prior known noisy destination and the noise covariance are incorporated in the initialization of the proposed filter. Monte-Carlo simulations are performed to demonstrate the effectiveness of the proposed filtering algorithm.
Development of management information system for cooperative education in Thailand

The university — industry — government linkage should drive diverse policies with agility leading to the satisfactory of the requirements. However, the amount of information exceeds the processing capacity of the current system. This paper aims to propose a management information system called Cooperative Education Management System (CEM). The process of CEM is to gather the data from university and produce the real time summary report to the executive body. Moreover, CEM also eliminates wastes, unnecessary processes, in the operation with 50% and 64.7 % for reducing the operation time. In addition, the overall satisfaction of CEM is 4.38.
Automatic traffic sign detection and recognition using moment invariants and support vector machine

Automatic traffic sign detection and recognition (TSDR) is one of the most significant areas of object detection. In spite of numerous researches, it has always been a challenging problem. In this paper, an approach for detecting circular and triangular traffic signs is proposed. The performance of the entire system is measured on German traffic sign detection benchmark (GTSDB) and German traffic sign recognition benchmark (GTSRB) dataset. Traffic signs are detected using color segmentation and thresholding method in Hue Saturation Intensity (HSI) color space. Then, the shape of traffic signs is detected using geometric invariant Hu moments. Further, the features are extracted using a technique called HSI-HOG descriptor where features are extracted from each channel of HSI independently. To select the most discriminant features with minimal loss of information, dimensionality reduction technique Principal Component Analysis (PCA) is applied and classification is performed using Support Vector Machine (SVM) technique.
A global maximum power point tracking technique based on current source region detection of I-V curve

This paper proposes a new global MPPT method for PV array under uniform as well as non-uniform insolation. The proposed method first finds out whether the operating voltage lies in the current source region (CSR) or voltage source region (VSR). If the operating voltage lies in CSR, the method skips the settling of PV voltage at the reference voltage and moves to next operating voltage. This reduces the tracking time as PV voltage takes more time to settle when operating voltage lies in CSR. Moreover, the method applies P&O to find the peak whenever operating voltage moves from VSR to CSR as a peak always lies in between them. Finally, it compares the measured power at various local peaks to find the global peak. The performance of the proposed algorithm is further verified in MATLAB/Simulink.
Electromyography and surface muscle-pressure as measures of lower-limb muscle activities during squatting

Assessment of human lower-limb motions and muscle activities are important for improving ergonomics and performance of wearable robotic devices such as prostheses, orthoses and exoskeletons. Human-machine interface plays an important role to ensure the desired comfort levels of the wearer. Moreover, a growing trend for controlling lower-limb robotic wearables based on muscle activity can be recognized. In that context this paper presents a study on lower-limb muscle behavior using two measures, named surface electromyography (sEMG) and surface muscle pressure (SMP). The study was performed during squatting and include statistical data of ten young healthy male subjects. The key lower-limb muscles, such as quadriceps, hamstrings and calf muscles were studied using both sEMG and SMP measures. These results would help for designing ergonomically efficient lower-limb wearables and provide useful insight for developing new and improved control strategies for bionic devices.
Brown adipose tissue density measured by near-infrared time-resolved spectroscopy in Japanese, across a wide age range

Abstract. F18-fluorodeoxyglucose (FDG)–positron emission tomography (PET) along with computed tomography (CT) is a standard method for assessing brown adipose tissue (BAT) activity. We tested the usefulness of near-infrared time-resolved spectroscopy (NIRTRS) as a simple and noninvasive method for evaluating BAT density (BAT-d) by examining the effects of some factors known to influence BAT activity. The total hemoglobin concentration as a parameter of BAT-d was evaluated using NIRTRS in the supraclavicular region in 413 Japanese individuals. The associations were analyzed between BAT-d and sex, age, the percentages of body fat (%BF), visceral fat (VF), and the seasonal ambient temperature (AmT) fluctuations. Age was associated with decreased BAT-d (P < 0.05). There was no sex difference in the BAT-d, except for those in their twenties. Multivariate analyses revealed that %BF and VF were correlated with BAT-d, and the lower AmT (around 4°C or 5°C) for 4 and 6 weeks prior to the measurement day was associated with an increase in the BAT-d. Our NIRTRS results were analogous to those reported with FDG18-PET / CT, indicating the usefulness of NIRTRS. BAT-d might increase during the 4 and 6 weeks after the AmT decreases to lower than 4°C or 5°C.
Reservoir Computing Hardware with Cellular Automata

Elementary cellular automata (ECA) is a widely studied one-dimensional processing methodology where the successive iteration of the automaton may lead to the recreation of a rich pattern dynamic. Recently, cellular automata have been proposed as a feasible way to implement Reservoir Computing (RC) systems in which the automata rule is fixed and the training is performed using a linear regression. In this work we perform an exhaustive study of the performance of the different ECA rules when applied to pattern recognition of time-independent input signals using a RC scheme. Once the different ECA rules have been tested, the most accurate one (rule 90) is selected to implement a digital circuit. Rule 90 is easily reproduced using a reduced set of XOR gates and shift-registers, thus representing a high-performance alternative for RC hardware implementation in terms of processing time, circuit area, power dissipation and system accuracy. The model (both in software and its hardware implementation) has been tested using a pattern recognition task of handwritten numbers (the MNIST database) for which we obtained competitive results in terms of accuracy, speed and power dissipation. The proposed model can be considered to be a low-cost method to implement fast pattern recognition digital circuits.
Solving economic load dispatch using space reduction based PSO

Achieving a faster and efficient technique to solve Economic Load dispatch (ELD) problems becomes a common goal for several researchers is the field of modern power system analysis. Here the main object is to minimize the highest percentage of operating cost (fuel cost) of various generating utilities satisfying customer's peak and base load demand and to assess optimum operation along with financial and electrical performance of a power plant. Out of various modern heuristic techniques POS algorithm has already taken a representative position in solving ELD problems which is capable of providing fast response. In this paper, space reduction based PSO has been implemented which enhances the ability of particles, exploring the solution spaces more effectively providing faster optimization and higher convergence rate. A three unit formal generating with the power balance constraints and ramp rate limits is considered here and compared with other evolutionary algorithms as given in the literature to demonstrate the feasibility and effectiveness of the proposed method.
DBS: A dynamic bandwidth selection MAC protocol for channel bonding in IEEE 802.11ac WLANs

The paper addresses a bandwidth selection problem while channel bonding scheme is used in IEEE 802.11ac wireless local area networks (WLANs) such that a channel with appropriate bandwidth can be utilized by STAs to transmit without collision. The goal of IEEE 802.11ac is to provide very high throughput (VHT) WLANs over 5 GHz band. Among the new technologies specified in IEEE 802.11ac, channel bonding is regarded as a quite useful scheme to accommodate growing traffic demands in WLANs. However, to effectively exploit the efficiency of channel bonding scheme, a well-designed MAC protocol is of crucial importance. As a result, the transmission in a wider bandwidth is analyzed first. Based on the results, two problems, Carrier Sensing Decreasing Problem (CSDP) and Outside Warning Range Problem (OWRP), are conducted in this paper. Moreover, the paper analyzes the relationships among the transmission range, carrier-sensing range, and interference range in case that channel bonding is adopted. Consequently, a dynamic bandwidth selection (DBS) MAC protocol for IEEE 802.11ac WLANs is proposed to avoid collisions. DBS can not only prevent CSDP and OWRP problems but also can increase network performance. Simulation results show that DBS outperforms against other protocols in packet successful rate and network throughput.
Vascular-Derived Vegfa Promotes Cortical Interneuron Migration and Proximity to the Vasculature in the Developing Forebrain

Abstract Vascular endothelial growth factor (Vegfa) is essential for promoting the vascularization of the embryonic murine forebrain. In addition, it directly influences neural development, although its role in the forming forebrain is less well elucidated. It was recently suggested that Vegfa may influence the development of GABAergic interneurons, inhibitory cells with crucial signaling roles in cortical neuronal circuits. However, the mechanism by which it affects interneuron development remains unknown. Here we investigated the developmental processes by which Vegfa may influence cortical interneuron development by analyzing transgenic mice that ubiquitously express the Vegfa120 isoform to perturb its signaling gradient. We found that interneurons reach the dorsal cortex at mid phases of corticogenesis despite an aberrant vascular network. Instead, endothelial ablation of Vegfa alters cortical interneuron numbers, their intracortical distribution and spatial proximity to blood vessels. We show for the first time that vascular-secreted guidance factors promote early-migrating interneurons in the intact forebrain in vivo and identify a novel role for vascular-Vegfa in this process.
Inside a phisher's mind: Understanding the anti-phishing ecosystem through phishing kit analysis

Phishing attacks are becoming increasingly prevalent: 2016 saw more phishing attacks than any previous year on record according to the Anti-Phishing Working Group. At the same time, the growing level of sophistication of cybercriminals must be understood for the development of effective anti-phishing systems, as phishers have extensive control over the content they serve to their victims. By examining two large, real-world datasets of phishing kits and URLs from 2016 through mid-2017, we paint a clear picture of today's anti-phishing ecosystem while inferring the higher-level motives and thought processes of phishers. We analyze the nature of server-side . htaccess filtering techniques used by phishers to evade detection by the security community. We also propose a new generic classification scheme for phishing URLs which corresponds to modern social engineering techniques and reveals a correlation between URL type and compromised infrastructure use. Our analysis identifies measures that can be taken by the security community to defeat phishers' countermeasures and increase the likelihood of a timely response to phishing. We discover that phishers have a keen awareness of the infrastructure used against them, which illustrates the ever-evolving struggle between cybercriminals and security researchers and motivates future work to positively impact online security.
How to Verify a Quantum Computation

We give a new theoretical solution to a leading-edge experimental challenge, namely to the verification of quantum computations in the regime of high computational complexity. Our results are given in the language of quantum interactive proof systems. Specifically, we show that any language in $\mathsf{BQP}$ has a quantum interactive proof system with a polynomial-time classical verifier (who can also prepare random single-qubit pure states), and a quantum polynomial-time prover. Here, soundness is unconditional--i.e., it holds even for computationally unbounded provers. Compared to prior work achieving similar results, our technique does not require the encoding of the input or of the computation; instead, we rely on encryption of the input (together with a method to perform computations on encrypted inputs), and show that the random choice between three types of input (defining a computational run, versus two types of test runs) suffices. Because the overhead is very low for each run (it is linear in the size of the circuit), this shows that verification could be achieved at minimal cost compared to performing the computation. As a proof technique, we use a reduction to an entanglement-based protocol; to the best of our knowledge, this is the first time this technique has been used in the context of verification of quantum computations, and it enables a relatively straightforward analysis.
IBEX: Versatile Readout ASIC With Spectral Imaging Capability and High Count Rate Capability

IBEX is a novel mixed-mode CMOS application-specific integrated circuit (ASIC), developed at DECTRIS Ltd., dedicated to the readout of hybrid photon counting semiconductor pixel detectors. The chip has been strictly designed in a radiation tolerant enclosed transistor layout and is fabricated in a 110-nm CMOS technology with eight metal layers. It consists of a <inline-formula> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula> matrix of <inline-formula> <tex-math notation="LaTeX">$75\times 75\,\,\mu \text {m}^{2}$ </tex-math></inline-formula> pixels, which results in an overall chip size of <inline-formula> <tex-math notation="LaTeX">$19.27\times 19.76$ </tex-math></inline-formula> mm<sup>2</sup> with periphery, supply, and I/O pads included. A so-called merging mode allows for an increased pixel size of <inline-formula> <tex-math notation="LaTeX">$150\times 150\,\,\mu \text {m}^{2}$ </tex-math></inline-formula>. The pixel readout electronics supports electrons and holes collection, and consists of a charge sensitive preamplifier with programmable gain, a shaper, and two comparators that allow for two independent energy thresholds. In the merging mode, the number of energy thresholds is increased up to four. In order to minimize the pixel-to-pixel energy threshold variation, each pixel comparator can be adjusted with a 6-bit trim digital-to-analog converter. The ASIC can operate in a continuous readout mode with two independent 16-bit counters or in a high counting range mode with a single 32-bit counter per energy threshold level. The chip features counter overflow handling and an instant retrigger technology with an adjustable retrigger time for a significantly improved high-rate counting performance. The ASIC offers a selectable external data bus width of 4, 8, or 16-bit. The count rate limit of the readout chip dc-coupled with a silicon sensor lies at around 10 Mcts/s/pix. The measurements show an electronic pixel noise of <inline-formula> <tex-math notation="LaTeX">$89\,\,e^{-}$ </tex-math></inline-formula>rms and the detectable photon energy range between 3 and 160 keV.