Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning Based Offloading for Realtime Applications in Mobile Edge Computing

Energy consumption is one of the most important issues for mobile devices such as smartphones and laptops. For mobile devices that execute multiple computation-intensive or delay-sensitive applications simultaneously, Mobile Edge Computing (MEC) based offloading provides a promising solution to the energy problem. However, blindly offloading all tasks to MEC servers is not the best choice because transferring a simple task to a MEC server via wireless networks might consume more energy than processing the task locally. In addition, Dynamic Voltage and Frequency Scaling (DVFS) could be utilized to reduce the energy consumption associated with locally processed tasks by appropriately lowering CPU frequency. In this paper, we propose a realtime reinforcement learning based offloading scheme, RRLO, which is based on both MEC-based offloading and DVFS-based energy consumption reduction. Technically, RRLO jointly learns the optimal offloading policy and DVFS-based scheduling method. Depending on the workload and network condition, RRLO not only determines whether a task should be offloaded to a MEC server, but also selects the best DVFS method used to schedule local tasks. Our simulation results indicate that RRLO outperforms the existing MEC-based offloading schemes.
A High-Speed Moving Average Integrator in Silicon Photonics for TIA-Less Receivers

A high-speed moving average integrator in silicon photonics is demonstrated. The integrator is monolithically fabricated with an optical receiver front-end operating at 1550 nm. The design consists of an optical splitter, an optical low-loss delay line to implement a 400 ps delay between the two received bit streams, two SiGe photodiodes, and a 200 fF capacitor. The proposed moving average structure can perform four-bit integration at 10 Gbps. The resulting signal at the output of the integrator is the moving average of the received bit streams. The moving average integrator is validated through measurements and simulations at 2.5 and 5 Gbps. The proposed structure facilitates the development of next generation cost-effective and energy-efficient optical transceivers by exploiting optical time delay and moving average operation in silicon photonics. This mitigates the need for multiple clock phase generation and trans-impedance amplification on the electronic chip.
Myth of the molecule: DNA barcodes for species cannot replace morphology for identification and classification

So‐called DNA barcodes have recently been proposed to answer the problem of specimen identification and to quantify global biodiversity. We show that this proposition is wanting in terms of rationale, methodology and interpretation of results. In addition to falling short of all its stated goals, the method abandons the benefits of morphological studies in favor of a limited molecular identification system that would ultimately impede our understanding of biodiversity.
PDP and TPD Flexible MCML and MTCML Ultralow-Power and High-Speed Structures for Wireless and Wireline Applications

This article presents two novel structures for designing current mode logic (CML) digital circuits [MOS CML (MCML) and multiple tail CML (MTCML)] in ultralow-power (ULP) and high-speed (HS) applications. The transistor’s bulk can be used as a digital signal input at a level, where it does not cause a disturbance to the operation of the transistor. Therefore, the number of transistors used in novel structures is reduced and it is possible to improve power delay product (PDP) and time propagation delay (TPD) in some of the tail currents. This technique is obtained through integrating the bulk-driven technique into the CML digital circuits, which are in the form of bulk-driven MCML (BD-MCML) and BD-MTCML. In the conventional CMOS technology, the proposed structures depend on the type of substrate. The BD-MCML structure uses a deep n-well technology and the BD-MTCML uses an n-well technology. The CML gates, such as AND, XOR, and D-latch, are suggested. The systematic use of the proposed structures is discussed in both HS and ULP applications. Finally, an unsigned multiplier is designed in the form of the BD-MTCML with a conventional 180-nm technology. Mixed-signal circuits are Serial/De-serial (SerDes), optical receivers, radio frequency (RF) receivers, and line drivers on the HS approach, and also video processors and A/D and D/A converters on the ULP approach.
OCNN: Point Cloud-Based Convolutional Neural Network for Object Orientation Estimation

Convolutional neural networks for pose estimation based on 2D images are severely affected by imaging quality, illumination changes and occlusion, which greatly reduces their reliability and robustness in cluttered scenes. Hence a point cloud based convolutional neural network for orientation estimation(OCNN) is proposed in this paper. With the network, the 3D orientation is estimated by regressing to the rotation matrix. An orthogonality metric loss is introduced to normalize the output rotation matrix. For objects with rotational symmetry, an algorithm for estimating the plane of symmetry and cutting the object is introduced to eliminate orientation ambiguity. Experimental results on LineMOD dataset indicate the accuracy of the angular error below 5° is 94.1%. Combined with advanced point cloud registration algorithms, the network has the potential for real-time orientation estimation.
Enhanced Secrecy Performance of Multihop IoT Networks With Cooperative Hybrid-Duplex Jamming

As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations.
Learning to Control Neurons using Aggregated Measurements

Controlling a population of neurons with one or a few control signals is challenging due to the severely underactuated nature of the control system and the inherent nonlinear dynamics of the neurons that are typically unknown. Control strategies that incorporate deep neural networks and machine learning techniques directly use data to learn a sequence of control actions for targeted manipulation of a population of neurons. However, these learning strategies inherently assume that perfect feedback data from each neuron at every sampling instant are available, and do not scale gracefully as the number of neurons in the population increases. As a result, the learning models need to be retrained whenever such a change occurs. In this work, we propose a learning strategy to design a control sequence by using population-level aggregated measurements and incorporate reinforcement learning techniques to find a (bounded, piecewise constant) control policy that fulfills the given control task. We demonstrate the feasibility of the proposed approach using numerical experiments on a finite population of nonlinear dynamical systems and canonical phase models that are widely used in neuroscience.
CSI-MIMO: K-nearest Neighbor applied to Indoor Localization

Indoor Localization has attracted interest in both academia and industry for its wide range of applications. In this paper, we propose an indoor localization solution based on Channel State Information (CSI). CSI is a fine-grain measure of the effect of the channel on the transmitted signal. It is computed for each subcarrier and each antenna in the Multiple-Input-Multiple-Output (MIMO) antenna case. It is also becoming a trend for indoor position fingerprinting. By using a K-nearest neighbor learning method a highly accurate indoor positioning is achieved. The input feature is the magnitude component of CSI which is preprocessed to reduce noise and allow for a quicker search. The euclidean distance between CSI is the criteria chosen for measuring the closeness between samples. The method is applied to a CSI dataset estimated at an $8\times 2$ MIMO antenna that is published by the organizers of the Communication Theory Workshop Indoor Positioning Competition. The proposed method is compared with three other methods all based on deep learning approaches and tested with the same dataset. The Knearest neighbor method presented in this paper achieves a Mean Square Error (MSE) of 2.4 cm which outperforms its counterparts.
Enabling monocular depth perception at the very edge

Depth estimation is crucial in several computer vision applications, and a recent trend aims at inferring such a cue from a single camera through computationally demanding CNNs — precluding their practical deployment in several application contexts characterized by low-power constraints. Purposely, we develop a tiny network tailored to microcontrollers, processing low-resolution images to obtain a coarse depth map of the observed scene. Our solution enables depth perception with minimal power requirements (a few hundreds of mW), accurately enough to pave the way to several high-level applications at-the-edge.
Smart Edge Healthcare Data Sharing System

Smart health systems improve the efficiency of healthcare infrastructures and biomedical systems by integrating information and technology into health and medical practices. However, reliability, scalabilty and latency are among the many challenges hindering the realization of next-generation healthcare. In fact, with the exponential increases in the volume of patient data being produced and processed, many healthcare system' are being overwhelmed with the deluge of data they are facing. Many systems have been proposed to improve the system latency and scalability. However, there are concerns related to some of theses systems regarding the increasing levels of required human interaction which impact their efficiency. Recently, machine learning techniques are gaining a lot of interest in health applications as they exhibit fast processing with realtime predictions. In this paper, we propose a new healthcare system to reduce the waiting time in emergency department and improve the network scalability in any distributed system. The proposed model integrates the power of edge computing with machine learning techniques for providing a good quality of healthcare services. The machine learning algorithm is used to generate a classifier that can predict with high levels of accuracy the likelyhood of a patient to have a heart attack using his physiological signals ECG. The proposed system stores the patient data in a centralized database and generates a unique index using a new data-dependent Indexing algorithm that transforms the patient data into unique code to be sent for any medical data exchange. Multiple machine learning algorithms are studied and the best algorithm will be selected based on efficient performance result for the prediction of heart attack problem. simulation results show that the proposed model can effectively detect the abnormal heart beats with 91% using SVM algorithm. We show also that the proposed system outperforms conventional indexing algorithm systems in terms of collisions rate.
KRAS mutation is associated with upregulation of integrin beta-4 expression leading to tumor invasion in colorectal cancer.

576 Background: KRAS mutation ( KRASmut) is associated with aggressive biological behavior and resistance to chemoradiotherapy in colorectal cancer (CRC). The tumor microenvironment is a critical component framing the biological behavior of cancers. We have recently shown that a KRASmut modulates the tumor microenvironment by reducing the expression of extracellular matrix (ECM) genes in CRC. The effect of a KRASmut on integrins, the epithelial cell receptors for ECM proteins, remains largely unknown. Here, we investigated the impact of KRASmut on integrin expression in CRC and the effect of integrin beta 4 (ITGB4) expression on CRC phenotype. Methods: The genomic profile of 79 locally advanced rectal cancers (LARC) was characterized by the MSK-IMPACT DNA assay and RNA sequencing by Hi-Seq platform. The transcriptomic changes associated with KRAS in the LARC cohort was validated in the TCGA colon cancer dataset. Expression of ITGB4 was investigated by immunofluorescence (IF) in 39 colon cancer specimens by counting ITGB4-positive cells in an E-cadherin positive epithelial population. The relationship between KRAS and ITGB4 was also explored by manipulating KRAS expression in human cell lines and genetically engineered mouse models (GEMMs). ITGB4 knockout in HCT116 CRC cell lines and organoids from GEMMs were generated with CRISPR/Cas9. ITGB4 expression was confirmed using qRT-PCR and western blotting. Cell proliferation was assessed with the MTT assay. Cell invasion and migration were assessed in a trans-well system. Results: ITGB4 gene expression was increased in KRASmut compared to KRASwildtype in LARC and TCGA. Increased ITGB4 expression in KRASmut colon cancers was also validated by IF (p = 0.0029). Introduction of KRASmut in HCT116 CRCs and GEMMs increased ITGB4 expression by 1.5 to 2 fold. Knockout of ITGB4 reduced the migration and invasion of HCT116 CRC cells but did not alter proliferation. Conclusions: KRASmutincreases the expression of ITGB4 in CRC. Increased ITGB4 expression is associated with CRC cell invasion and migration. These results inform the biology of tumor progression in KRASmut CRC tumors.
WalkAround: Multipath-assisted Indoor Localization and Mapping Using a Single Receiver

Multipath signals, which are suppressed in conventional localization algorithms, usually relate target location and structure of indoor environment through geometry parameters such as Angle of arrival (AOA) and Time of Flight (TOF). Thus, they can be exploited to locate the target and construct maps, which describe the structure topology of indoor environment. In this paper, we propose WalkAround, a multipath-assisted indoor localization and mapping system using the commodity WiFi signals, which contain phase errors caused by imperfect hardware and non-synchronized clocks. To realize accurate localization without interference of phase errors, we firstly construct a geometry model for jointly estimating the locations of target and scatterers which can be regarded as objects such as wall and furniture, by using TOF differences between the reflection paths and direct path. Then, with the help of AOAs we develop a locations searching algorithm based on Particle Swarm optimization (PSO). After that, we propose a density-based mapping algorithm with the locations of the scatterers and target, which does not need any anchor nodes or landmarks. We have implemented WalkAround in actual indoor environments by using the commodity WiFi devices. Based on the experiment results, the median location error of the target is 1. 49m and the constructed map matches the real structure topology of indoor environment well using only one receiver.
A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization*

In this paper, we present a reinforcement-learning-based distributed approach to wind farm energy capture maximization using yaw-based wake steering. In order to maximize the power output of a wind farm, individual turbines can use yaw misalignment to deflect their wakes away from downstream turbines. Although using model-based methods to achieve yaw misalignment is one option, a model-free method might be better suited to incorporate changing conditions and uncertainty. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multiagent environment that empowers individual turbines to optimize overall wind farm output and react to unforeseen disturbances.
Differentially Private Interval Observer Design with Bounded Input Perturbation

Real-time data processing for emerging systems such as intelligent transportation systems requires estimating variables based on privacy-sensitive data gathered from individuals, e.g., their location traces. In this paper, we present a privacy-preserving interval observer architecture for a multiagent system, where a bounded privacy-preserving noise is added to each participant’s data and is subsequently taken into account by the observer. The estimates published by the observer guarantee differential privacy for the agents’ data, which means that their statistical distribution is not too sensitive to certain variations in any single agent’s signal. A numerical simulation illustrates the behavior of the proposed architecture.
Structure Composing Layer: A New Type of Layer to Deep and Speed up Convolutional Neural Networks

In this paper a new type of layer named Structure Composing Layer (SCLayer) aims to reduce parameters’number and decrease computation cost is proposed. SCLayer combines simple structures into complex structure with less parameters. Compared to convolution layer with filter kernel size 3× 3, SCLayer reduces parameters’ number to 19 of convolution layer’s. Based on VGG-16[1] 1 with SCLayers a nueral network, named Snowflake Net (SfNet), is designed and evaluated on dataset CIFAR-10 and CALTECH-256[3]. Experiment results show that comparing with VGG-16 training time reduces more than 12.9%, classification accuracy increases about 0.18% on CIFAR10, classification accuracy increases about 2.75% on CALTECH-256. During training process nonlinear phenomenon Accuracy Valley is observed. Influence of nonlinear phenomena to models’ generalization is discussed.
The Influence of Competence User Information System on User Satisfaction of New Student Admission Information System in University of Swadaya Gunung Jati

Swadaya Gunung Jati University (Unswagati) as the oldest and largest university in the CIAYUMAJAKUNING area (Cirebon, Indramayu, Majalengka, Kuningan) in recent years has experienced a decline in new student admissions. As one of the steps to anticipate, Unswagati applies online-based new student admissions as the new student admission information system (SIMABAR). But the application of SIMABAR in Unswagati still occurs some problems with the many complaints of prospective new students regarding the use of SIMABAR. The study aims at analyzing and explained the influence of user competence to user satisfaction new student admission information system in the University of Swadaya Gunung Jati. Total populations of this study were 2185 first-year students with a total sample of 338. The sampling technique using stratified proportional random sampling. The analytical method used SEM (Structural Equation Modeling). The results showed that user competence significantly influenced user satisfaction new student admission information system. The better the perception of the quality system, the quality of information, the quality of service, and user competence it will further increase user satisfaction. Keywords— user competence, user satisfaction, student admission
Fructan 1-exohydrolase is associated with flower opening in Campanula rapunculoides.

Fructans, typically reserve carbohydrates, may also fulfil other more specific roles in plants. It has been convincingly demonstrated that fructan hydrolysis contributes to osmoregulation during flower opening in the monocot species Hemerocallis. We report that a massive breakdown of inulin-type fructans in the petals of Campanula rapunculoides L. (Campanulaceae), associated with flower opening, is accompanied by a strong increase in fructan 1-exohydrolase (1-FEH; EC 3.2.1.153) activity and a decrease in sucrose : sucrose 1-fructosyl transferase (1-SST; EC 2.4.1.99) activity. The data strongly suggest that the drastic change in the 1-FEH/1-SST activity ratio causes the degradation of inulin, contributing to the osmotic driving force involved in flower opening. All characterised plant FEHs are believed to be derived from tissues that store fructans as a reserve carbohydrate either temporarily (grasses and cereals) or over a longer term (dicot roots and tubers). Here, we focussed on a physiologically distinct tissue and used a reverse transcriptase-polymerase chain reaction based strategy to clone the 1-FEH cDNA from the Campanula petals. The translated cDNA sequence groups along with other dicot FEHs and heterologous expression revealed that the cDNA encodes a 1-FEH without invertase activity. 1-FEH expression analysis in petals correlates well with 1-FEH activity and inulin degradation patterns in vivo, suggesting that this enzyme fulfils an important role during flower opening.
Robust Dynamic Spectrum Access in Adversarial Environments

Rapid growth of radio traffic in the unlicensed spectrum has led to challenges in securing sufficient resources for reliable communications. This problem is compounded by the emergence of uncooperative and even adversarial users which can interfere with the network fidelity of existing secondary users. In this paper, we propose a DSA policy using a decentralized sample median based exploration-aided UCB (DMA med-E-UCB) and a decentralized sample-median based epsilon greedy (DMA med$- \epsilon-$greedy). Both algorithms are applied in a distributed spectrum sharing cognitive radio network and able to defend against an adversarial attacker with arbitrarily large interference power. We model the proposed spectrum access problem as a multi-armed bandit and show that both algorithms are robust to adversarial attacks. Provided that the attack occurs infrequently, the regret achieved by both algorithms is O$(\log T)$ with high probability, where T is the number of sequential channel access attempts. We show that our algorithms outperform other standard distributed policies and verify our results using an over-the-air software defined-radio testbed.