Miracle-dz/UniFAR-FTUs huggingface dataset:
Bandwidth Compression Protection Against Collapse in Fog-Based Wireless and Optical Networks

Due to the promotion of cloud computing and the massive access of mobile Internet, the cloud radio access network has become a promising solution for the mobile network in 5G communication system. With the increasing of the local applications, plenty of traffic is produced from the centralized cloud computing, which leads to a heavy pressure to the fronthaul network. In order to avoid the traffic flood, fog-based radio over optical fiber networks (F-RoFN) as a promising extend solution for cloud radio access network provides better performances and lower cost taking advantage of fog computing in our previous paper. In the scenarios of analog and digital F-RoFNs, the collapse caused by disaster is an important factor, which threats network survivability and would result in enormous interrupt of users’ services. Since a number of applications need high-quality connections, it is necessary to guarantee the quality of services in case of collapse in F-RoFNs. On the basis of it, this paper proposes a bandwidth compression protection (BCP) algorithm against collapse in the fog-based wireless and optical networks to improve the network performances and guarantee the survivability in case of collapse with analog and digital scenarios. The resource efficiency of the whole network can be promoted with reducing the redundancy appropriately, while BCP algorithm classifies the level of services and further compresses the bandwidth of backup path occupied by the low-level services. The performances of the proposed algorithm are evaluated and compared with the conventional protection algorithm by the means of extensive simulations. The numeric results show that the proposed BCP algorithm can guarantee the network survivability with the lower blocking probability, higher resource utilization, and less average hop than the other algorithm.
A Reduced-Order Enhanced State Observer Control of DC-DC Buck Converter

This paper presents a reduced-order-enhanced state observer (RESO)-based control strategy for the PWM dc–dc buck converter. With the proposed RESO control strategy, the output voltage regulation of the dc–dc buck converter is able to achieve robust characteristics against the external disturbance and the internal parameter variation even without output current measurement. In addition, by incorporating the RESO in the controller, the output voltage regulation can be easily achieved with only a proportional gain to realize a zero steady-state error. Finally, the parameter design is discussed and the effectiveness of the proposed control strategy is verified with an experimental case study.
Representation Learning of Knowledge Graphs with Entity Attributes and Multimedia Descriptions

Representation learning of knowledge graphs encodes both entities and relations into a continuous low-dimensional space. Most existing methods focus on learning representations with structured fact triples indicating relations between entities, ignoring rich additional information of entities including entity attributes and associated multimodal content descriptions. In this paper, we propose a new model to learn knowledge representations with entity attributes and multimedia descriptions (KR-AMD). Specifically, we construct three triple encoders to obtain structure-based entity representation, attribute-based entity representation and multimedia content-based entity representation, and finally generate the knowledge representations for knowledge graphs in KR-AMD. The experimental results show that, by special modeling of entity attributes and text-image descriptions, KR-AMD can significantly outperform state-of-the-art KR models in prediction of entities, attributes and relations, which validates the effectiveness of KR-AMD.
Multiview Consensus Graph Clustering

A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview clustering methods use predefined affinity matrices and the clustering performance highly depends on the quality of graph. We learn a consensus graph with minimizing disagreement between different views and constraining the rank of the Laplacian matrix. Since diverse views admit the same underlying cluster structure across multiple views, we use a new disagreement cost function for regularizing graphs from different views toward a common consensus. Simultaneously, we impose a rank constraint on the Laplacian matrix to learn the consensus graph with exactly <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> connected components where <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is the number of clusters, which is different from using fixed affinity matrices in most existing graph-based methods. With the learned consensus graph, we can directly obtain the cluster labels without performing any post-processing, such as <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means clustering algorithm in spectral clustering-based methods. A multiview consensus clustering method is proposed to learn such a graph. An efficient iterative updating algorithm is derived to optimize the proposed challenging optimization problem. Experiments on several benchmark datasets have demonstrated the effectiveness of the proposed method in terms of seven metrics.
New Wideband T-Shaped Perforated Dielectric Resonator Antenna

New wideband T-shaped perforated dielectric resonator antenna is proposed. First, a rectangular DRA of dimensions 31 mm × 31 mm × 15.5 mm is designed with the dielectric constant of 10.2 at 2.4 GHz for fundamental TE111 mode. It is excited using offset co-axial probe feeding. Second, symmetrical array of square shaped holes of order 5 × 5 with the dimensions of 4 mm × 4 mm × 15.5 mm are drilled along z-axis with the periodicity (distance between two consecutive holes) of 6 mm. The perforated geometry reduces the effective dielectric constant and thus bandwidth improves. Next, two symmetrical slots of size 7.5 mm × 31 mm × 7.5 mm are cut from the perforated geometry. The resonant modes in the proposed geometry at 3.17 GHz and 4.1 GHz, resembles TE111 and TE131 like mode respectively. These modes are responsible for broadside radiation characteristics. The impedance bandwidth of ~45% is achieved in the frequency range 2.9 – 4.3 GHz at the resonant frequency of 3.17 GHz. The maximum gain of 6.1 dBi and efficiency greater than 80% is achieved in the frequency of operation, 2.9 – 4.3 GHz. The proposed geometry is suitable for various wireless communication systems.
Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics

Artificial neural networks that learn to perform Principal Component Analysis (PCA) and related tasks using strictly local learning rules have been previously derived based on the principle of similarity matching: similar pairs of inputs should map to similar pairs of outputs. However, the operation of these networks (and of similar networks) requires a fixed-point iteration to determine the output corresponding to a given input, which means that dynamics must operate on a faster time scale than the variation of the input. Further, during these fast dynamics such networks typically “disable” learning, updating synaptic weights only once the fixed-point iteration has been resolved. Here, we derive a network for PCA-based dimensionality reduction that avoids this fast fixed-point iteration. The key novelty of our approach is a modification of the similarity matching objective to encourage near-diagonality of a synaptic weight matrix. We then approximately invert this matrix using a Taylor series approximation, replacing the previous fast iterations. In the offline setting, our algorithm corresponds to a dynamical system, the stability of which we rigorously analyze. In the online setting (i.e., with stochastic gradients), we map our algorithm to a familiar neural network architecture and give numerical results showing that our method converges at a competitive rate. The computational complexity per iteration of our online algorithm is linear in the total degrees of freedom, which is in some sense optimal.
IoT Environment to Train Service Dogs

Dogs have been trained to serve different purpose: guidance, alert, rescuing people, therapy, among other services. And it is expensive to have dogs with these abilities because training is an individualized process. The challenge is to build devices to automate the training so that they can serve a larger number of people. How trainers can perform actions remotely over the internet? How can they build devices for dogs to exchange messages with other devices that recognize the dog’s behavior? This paper will explain why it is necessary to have IoT devices (reactive and smart objects) to assist the training of dogs and the evolution of dog’s devices: from monitoring to smart environment. This includes the description of requirements for the smart environment, as well as the association with the concepts and proposes of IoT protocols and hardware devices.
Towards a BIPM On-Site Comparison Program for AC Voltages Based on the Differential Sampling Technique

BIPM has conducted a few pilot studies to investigate the possibility of extending the protocol of the BIPM program of on-site DC Josephson Voltage Standard comparisons (BIPM.EM-KI0.b) to AC voltages. On-site indirect comparisons have been performed for AC low frequency sinewaves, at 1 V and 6.5 V rrns, using the new BIPM Programmable Josephson Voltage Standard and the differential sampling method. A relative agreement within a few parts in 107 can typically be achieved using a commercial calibrator as a transfer standard. The noise of the phase locking process appears to be the limiting factor. We have been investigating other AC generators as possible candidates for an accurate transfer standard.
Nonlinear integro-differential operator regression with neural networks

This note introduces a regression technique for finding a class of nonlinear integro-differential operators from data. The method parametrizes the spatial operator with neural networks and Fourier transforms such that it can fit a class of nonlinear operators without needing a library of a priori selected operators. We verify that this method can recover the spatial operators in the fractional heat equation and the Kuramoto-Sivashinsky equation from numerical solutions of the equations.
Bilu-Linial stability, certified algorithms and the Independent Set problem

We study the Maximum Independent Set (MIS) problem under the notion of stability introduced by Bilu and Linial (2010): a weighted instance of MIS is $\gamma$-stable if it has a unique optimal solution that remains the unique optimum under multiplicative perturbations of the weights by a factor of at most $\gamma\geq 1$. The goal then is to efficiently recover the unique optimal solution. In this work, we solve stable instances of MIS on several graphs classes: we solve $\widetilde{O}(\Delta/\sqrt{\log \Delta})$-stable instances on graphs of maximum degree $\Delta$, $(k - 1)$-stable instances on $k$-colorable graphs and $(1 + \varepsilon)$-stable instances on planar graphs. For general graphs, we present a strong lower bound showing that there are no efficient algorithms for $O(n^{\frac{1}{2} - \varepsilon})$-stable instances of MIS, assuming the planted clique conjecture. We also give an algorithm for $(\varepsilon n)$-stable instances. As a by-product of our techniques, we give algorithms and lower bounds for stable instances of Node Multiway Cut. Furthermore, we prove a general result showing that the integrality gap of convex relaxations of several maximization problems reduces dramatically on stable instances. Moreover, we initiate the study of certified algorithms, a notion recently introduced by Makarychev and Makarychev (2018), which is a class of $\gamma$-approximation algorithms that satisfy one crucial property: the solution returned is optimal for a perturbation of the original instance. We obtain $\Delta$-certified algorithms for MIS on graphs of maximum degree $\Delta$, and $(1+\varepsilon)$-certified algorithms on planar graphs. Finally, we analyze the algorithm of Berman and Furer (1994) and prove that it is a $\left(\frac{\Delta + 1}{3} + \varepsilon\right)$-certified algorithm for MIS on graphs of maximum degree $\Delta$ where all weights are equal to 1.
Classifying Protein Specific Residue Structures Based on Graph Mining

We proposed a 3-D protein structure using a simple and connected graph, where nodes indicate amino acids and edges represent contact distances between amino acids. Based on these graph structures, we present a graph mining algorithm to determine the crucial subgraphs in these graphs, which can be applied to classify protein structural families. The proposed algorithm was compared with BLAST, BLAT, and DALI. Moreover, an experiment was conducted, in which characteristic sub-structural patterns were found in several protein families within the Protein Data Bank.
Research agenda for life‐threatening cancer

OBJECTIVES The aim of this study was to identify future research agendas that reflect the concerns and unexplored areas of interest for patients with life-threatening cancer, their relatives and the clinical specialists during the cancer trajectory. METHODS Six focus group discussions were conducted addressing two different cancer trajectories: primary malignant brain tumour and acute leukaemia. For each of the two cancer trajectories, separate FGIs were carried out with patients, relatives and clinical specialists to identify important concerns, challenges and uncertainties. The FGIs were video/audio-recorded, transcribed and thematically analysed within and across FGI groups to construct research topics. Finally, the literature was reviewed for existing evidence concerning the identified research topic(s) to strengthen the suggested research agendas. RESULTS New research agendas related to high-grade glioma brain tumour and acute leukaemia with corresponding research questions were formulated within the topics of supportive care/palliation, education/information, rehabilitation, complementary and alternative therapy and organization of health care. CONCLUSION User involvement in identifying research agendas has the potential to improve quality of care for patients and caregivers across the cancer trajectory, while minimizing the gap in research between the healthcare user and healthcare provider.
Process Automation of Statistical Uncontrollability Prediction for Parameters of Dynamic Biomedical Systems

The development of Russian Northern regions for mineral resources production and refining cause an increase in the number of temporary residents and a change of traditional living of various groups residing permanently on the territory in indigenous Northern peoples. Living in the climate of the Northern territory is known to be exposed to the risk of various clinical conditions. For this reason, it is important to study particular medical aspects related to risk evaluation for disease development and determination of various natural and human factors. In addition to this determination of factors, another pressing issue is to develop methods, ways, models, algorithms as well as automation information systems capable to determine such relations and connections of medical and biological data collected during experiments. This will allow one to apply the study results for the practical use to prevent and treat various diseases.
PvdRT‐OpmQ and MdtABC‐OpmB efflux systems are involved in pyoverdine secretion in Pseudomonas putida KT2440

Fluorescent pseudomonads produce and secrete a siderophore termed pyoverdine to capture iron when it becomes scarce. The molecular basis of pyoverdine secretion is only partially understood. Here, we investigate the role of the putative PvdRT-OpmQ and MdtABC-OpmB efflux systems in pyoverdine secretion in the soil bacterium Pseudomonas putida KT2440. Expression from the respective promoters is stimulated by iron limitation albeit to varying degrees. Deletion of pvdRT-opmQ leads to reduced amounts of pyoverdine in the medium and decreased growth under iron limitation. Deletion of mdtABC-opmB does not affect growth. However, when both systems are deleted, strong effects on growth and pyoverdine secretion (yellow colony phenotype, less pyoverdine in medium, more pyoverdine in the periplasm) are observed. Overexpression of pvdRT-opmQ causes the opposite effect. These results provide first evidence for an involvement of the multidrug efflux system MdtABC-OpmB in pyoverdine secretion. In addition, the PvdRT-OpmQ system was shown to contribute to pyoverdine secretion in P. putida KT2440, extending previous investigations on its role in Pseudomonas species. Since the double deletion mutant still secrets pyoverdine, at least one additional efflux system participates in the transport of the siderophore. Furthermore, our results suggest a contribution of both efflux systems to ampicillin resistance.
Study of Accuracy and Stability of Sr Lattice Clocks at LNE-SYRTE

In this paper, we describe the operation of two optical lattice clocks operated at LNE-SYRTE. They feature an accuracy down to $2.1 \times 10^{-17}$, and each clock has a fractional frequency stability of $7\times 10^{-16}/\sqrt{\tau}$. We propose the updated accuracy budget of these clocks, with specific evaluations of the lattice-induced effect, as well as the characterization of a spurious systematic effect due to electric cross-talk between the clocks cycle and the clock laser that was recently observed. Reliably operating these two clocks led to the first contribution of an optical clock to TAI. Finally, we report on a non-destructive detection scheme aiming at improving the clock stability.
MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance

Sparse coding refers to the pursuit of the sparsest representation of a signal in a typically overcomplete dictionary. From a Bayesian perspective, sparse coding provides a maximum a posteriori estimate of the unknown vector under a sparse prior. In this paper, we suggest enhancing the performance of sparse coding algorithms by a deliberate and controlled contamination of the input with random noise, a phenomenon known as stochastic resonance. The proposed method adds controlled noise to the input and estimates a sparse representation from the perturbed signal. A set of such solutions is then obtained by projecting the original input signal onto the recovered set of supports. We present two variants of the described method, which differ in their final step. The first is a provably convergent approximation to the minimum mean square error (MMSE) estimator, relying on the generative model and applying a weighted average over the recovered solutions. The second is a relaxed variant of the former that simply applies an empirical mean. We show that both methods provide a computationally efficient approximation to the MMSE estimator, which is typically intractable to compute. We demonstrate our findings empirically and provide a theoretical analysis of our method under several different cases.
Abnormal T Cell Frequencies, Including Cytomegalovirus-Associated Expansions, Distinguish Seroconverted Subjects at Risk for Type 1 Diabetes

We analyzed T cell subsets from cryopreserved PBMC obtained from the TrialNet Pathway to Prevention archives. We compared subjects who had previously seroconverted for one or more autoantibodies with non-seroconverted, autoantibody negative individuals. We observed a reduced frequency of MAIT cells among seroconverted subjects. Seroconverted subjects also possessed decreased frequencies of CCR4-expressing CD4 T cells, including a regulatory-like subset. Interestingly, we found an elevation of CD57+, CD28–, CD127–, CD27– CD8 T cells (SLEC) among seroconverted subjects that was most pronounced among those that progressed to disease. The frequency of these SLEC was strongly correlated with CMV IgG abundance among seroconverted subjects, associated with IA-2 levels, and most elevated among CMV+ seroconverted subjects who progressed to disease. Combined, our data indicate discrete, yet profound T cell alterations are associated with islet autoimmunity among at-risk subjects.
Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD

Large-scale machine learning training, in particular distributed stochastic gradient descent, needs to be robust to inherent system variability such as node straggling and random communication delays. This work considers a distributed training framework where each worker node is allowed to perform local model updates and the resulting models are averaged periodically. We analyze the true speed of error convergence with respect to wall-clock time (instead of the number of iterations), and analyze how it is affected by the frequency of averaging. The main contribution is the design of AdaComm, an adaptive communication strategy that starts with infrequent averaging to save communication delay and improve convergence speed, and then increases the communication frequency in order to achieve a low error floor. Rigorous experiments on training deep neural networks show that AdaComm can take $3 \times$ less time than fully synchronous SGD, and still reach the same final training loss.
Load Sharing in Medium Voltage Islanded Microgrids With Advanced Angle Droop Control

Demand for converter-fed distributed energy resources are rapidly growing in microgrids and these energy sources are mainly responsible for sharing loads in islanded mode of operation. In high voltage lines, sharing real and reactive load power requirements can be achieved by conventional droop control methods, nevertheless strong coupling between active and reactive power in medium voltage lines with low X/R ratio degrades rating-based load sharing among converters. A transformation matrix which incorporates the line impedance effect in droop parameter calculation by modifying droop coefficients can be used to overcome this drawback. However, inclusion of power decoupling term in reference voltage calculation, has found to be more powerful to overcome coupling effects in load power sharing. By utilizing lesser steady state frequency deviation of angle droop in comparison with frequency droop, this paper adopts angle droop in real power loop. Angle droop concept along with power decoupling factor has an added advantage of improved reactive power sharing with surplus accuracy in real power distribution, which in turn, allows harmonic power distribution based on converter rating under different load conditions. Moreover, discrete state feedback control together with third harmonic injection-based pulse width modulation scheme provides additional robustness to individual converters.