Miracle-dz/UniFAR-FTUs huggingface dataset:
Localized Surface Plasmon Resonance of Reduced Graphene Oxide/Ag Hybrid for Gas Sensing Application

Localized surface plasmon resonance of metallic nanoparticles has been widely employed for gas sensing applications. Since the response of bare nanoparticles is usually weak, it is possible to improve their responses by utilizing the high gas adsorption capability of 2-D materials. In this paper, we developed an optical gas sensor by decorating reduced graphene oxide (rGO) sheets with Ag nanoparticles for the detection of ethanol vapor at the room temperature. We showed that the gas sensitivity of bare Ag nanoparticles can be enhanced by utilizing rGO sheets. We observed improved responses for structures in which rGO was fabricated above the nanoparticles (rGOup/Ag) compared with the structures with rGO below the nanoparticles (Ag/rGOdown). Gas sensitivity was improved 1.47 and 2.75 times, respectively, in the Ag/rGOdown and rGOup/Ag structures compared with the bare Ag nanoparticles. Our results demonstrate that the adsorption of ethanol molecules on the Ag nanoparticles/rGO hybrids leads to not only a plasmonic shift occurring in bare Ag nanoparticles but also an intensity change in the extinction spectrum. In other words, alteration of rGO conductivity due to the gas adsorption leads to an intensity variation in optical spectrum, which can be utilized for improving gas sensing. Finally, we performed a finite-element analysis by assuming the electron exchange between Ag nanoparticles/rGO hybrids and target gas through redox reactions to assess the validity of observed experimental results. Our numerical results confirmed the observed experimental results.
Convergence Analysis of Signed Nonlinear Networks

This paper analyzes the convergence properties of signed networks with nonlinear edge functions. We consider diffusively coupled networks comprised of maximal equilibrium-independent passive (MEIP) dynamics on the nodes, and a general class of nonlinear coupling functions on the edges. The first contribution of this paper is to generalize the classical notion of signed networks for graphs with scalar weights to graphs with nonlinear edge functions using notions from the passivity theory. We show that the output of the network can finally form one or several steady-state clusters if all edges are positive and, in particular, all nodes can reach an output agreement if there is a connected subnetwork spanning all nodes and strictly positive edges. When there are nonpositive edges added to the network, we show that the tension of the network still converges to the equilibria of the edge functions if the relative outputs of the nodes connected by nonpositive edges converge to their equilibria. Furthermore, we establish the equivalent circuit models for signed nonlinear networks, and define the concept of equivalent edge functions, which is a generalization of the notion of the effective resistance. We finally characterize the relationship between the convergence property and the equivalent edge function, when a nonpositive edge is added to a strictly positive network comprised of nonlinear integrators. We show that the convergence of the network is always guaranteed, if the sum of the equivalent edge function of the previous network and the new edge function is passive.
Pattern Recognition on Usage of Operational Clothing in Canadian Armed Forces

The Canadian Department of National Defence has been issuing Operational Clothing at no cost to Canadian Armed Forces (CAF) members based on tear and wear through clothing stores for many years. A significant change will be introduced: CAF is considering using annual point allocations and quantity caps to manage the expenditure of Operational Clothing though an e-business. To build such a point system, this research studied usage patterns of Operational Clothing and identified the differentiating factors affecting the usage patterns based on ten years of massive individual clothing transaction data and individual personnel data. Not only traditional statistical tests but also diagnostic analytics have been used for the analysis. The results of this study will inform decision making on the establishment of the new point system for managing expenditure of the Operational Clothing for CAF.
A 1.9MW SVM Processor with On-Chip Active Learning for Epileptic Seizure Control

This work presents the first support vector machine (SVM) processor that supports on-chip active learning for seizure detection. Alternating direction method of multipliers (ADMM), minimum-redundancy maximum-relevance (mRMR), and low-rank approximation are utilized to reduce the computational complexity and memory storage by 99.4% and 90.4%, respectively. Hardware complexity is reduced by 87% through folded architecture with reconfigurable processing elements. The chip achieves the best detection performance with a 96.1% accuracy and a 0.34% false alarm rate in 0.71s with 1.9mW. It also achieves a 153,310x higher energy efficiency than a high-end CPU for SVM training.
0.5V-VIN, 165-MA/MM2 Fully-Integrated Digital LDO Based on Event-Driven Self-Trisuerina Control

In recent digital low-drop-out (LDO) regulator designs, event-driven (ED) control achieved significantly better scaling in the output capacitor and voltage droop over Time-Driven (TD) one. The state-of-the-art ED LDO design, however, still has several drawbacks including long settling time and low current density. This paper presents a new LDO based on novel self-triggering ED control and initialization. The 0.5V digital LDO in a 65 nm achieves the best-in-class performance in the droop voltage, output capacitor size, settling time, and current density over the prior ED and TD LDOs.
Electromagnetic Performance Analysis of Hybrid Excited Seg-mental Rotor Flux Switching Machine.

Due to high torque density, simple rotor structure and good heat dissipation, the electrically and permanent magnet (PM) excited segmental rotor flux switching machines (SRFSMs) have attracted much attentions in recent years [1], [2]. The machine topology [3], analysis method [4], and torque ripple reduction method [5] are studied in the literatures. In order to reduce the excitation loss of electrically excited SRFSM and increase the magnetic field regulation capacity of PM excited SRFSM, the new hybrid excited SRFSM is proposed and the analytical subdomain model is derived to calculate the magnetic field and electromagnetic performance. The magnetic field regulation capacity is verified with the results of finite element method (FEM) and prototype machine experiment.
Optimal Offloading and Resource Allocation in Mobile-Edge Computing with Inter-User Task Dependency

In this paper, we consider a two-user mobile-edge computing (MEC) network, where each wireless device (WD) has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and execution time. The problem is challenging due to the combinatorial nature of the offloading decision among all tasks and the strong coupling with resource allocation among subsequent tasks. When the offloading decision is given, we obtain the closed-form expressions of the offloading transmit power and local CPU frequencies and propose an efficient method to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decision follows an one-climb policy, based on which a reduced-complexity algorithm is proposed to obtain the optimal offloading decision in polynomial time. Numerical results validate the effectiveness of our proposed methods.
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier

We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.
Design and Comparison of Radial Flux Magnetic Levitation Systems Based on Conventional and Buried Permanent Magnet Types Rotors

This paper presents design, analysis and comparison of a radial flux 8-slot magnetic levitation system for two different rotor structures. The magnet-less rotor or conventional and buried-magnet rotor topologies are investigated in detail. 1 Dimensional (lD) magnetic equivalent circuit (MEC) analyses are performed to demonstrate the fundamentals of levitation for two rotor types. 2D finite element analyses (FEA) are also carried out to compare the two structures and to illustrate the benefits of permanent magnet (PM) rotor. Magnetic levitation prototypes with two different rotor structures are built and tested. Results obtained from MEC, FEA and experimental system illustrate that there exists good agreement. It is concluded that buried-magnet rotor type magnetic levitation system has more benefits as opposed to conventional rotor type levitation system design in terms of current and losses.
Revisiting neural relation classification in clinical notes with external information

Recently, segment convolutional neural networks have been proposed for end-to-end relation extraction in the clinical domain, achieving results comparable to or outperforming the approaches with heavy manual feature engineering. In this paper, we analyze the errors made by the neural classifier based on confusion matrices, and then investigate three simple extensions to overcome its limitations. We find that including ontological association between drugs and problems, and data-induced association between medical concepts does not reliably improve the performance, but that large gains are obtained by the incorporation of semantic classes to capture relation triggers.
Single Event Effect Mitigation Techniques in Readout ASICs for Particle Detectors in High Energy Physics

This paper discusses Single Event Effect (SEE) mitigation techniques in readout ASICs for particle detectors in High Energy Physics (HEP) experiments. High radiation environment of such experiments results in necessity to use the SEE mitigation techniques to preserve the proper functionality of the ASIC in radiation environment. The constraints on the readout ASIC imposed by experimental conditions, mainly on power consumption, limit the range of techniques which can be actually applied. In practice an optimum set of SEE mitigation techniques needs to be found resulting in the ASIC with the required level of immunity at the given power consumption. Here we concentrate on two most popular techniques i.e. guard bands and cell redundancy. Implementation of these techniques in an example very complex readout ASIC for HEP experiment is discussed.
DSM: a specification mining tool using recurrent neural network based language model

Formal specifications are important but often unavailable. Furthermore, writing these specifications is time-consuming and requires skills from developers. In this work, we present Deep Specification Miner (DSM), an automated tool that applies deep learning to mine finite-state automaton (FSA) based specifications. DSM accepts as input a set of execution traces to train a Recurrent Neural Network Language Model (RNNLM). From the input traces, DSM creates a Prefix Tree Acceptor (PTA) and leverages the inferred RNNLM to extract many features. These features are then forwarded to clustering algorithms for merging similar automata states in the PTA for assembling a number of FSAs. Next, our tool performs a model selection heuristic to approximate F-measure of FSAs, and outputs the one with the highest estimated F-measure. Noticeably, our implementation of DSM provides several options that allows users to optimize quality of resultant FSAs. Our video demonstration on the performance of DSM is publicly available at https://goo.gl/Ju4yFS.
Design of an Intelligent Wireless Communication Module Based on ARM

To transmit information quickly and accurately and improve work efficiency, reduced instruction set computer (RISC) processors and technologies related to advanced RISC machines (ARM) were applied. The design of an intelligent wireless communication module based on ARM was completed using the wireless communication module for wireless communication, wireless control, wireless meter reading, industrial data acquisition, and other applications. Results showed that the designed smart wireless communication module had certain feasibility. In life communication, the transmission and reception of information were completed quickly and intelligently. This feature expanded the geographical scope of application and enhanced the expansion. Therefore, the smart wireless communication module has the advantages of accuracy, high efficiency, and humanization. Its utilization can bring convenience in people’s lives and communication.
Implementation of Taylor models in CORA 2018

Tool Presentation: Computing guaranteed bounds of function outputs when their input variables are bounded by intervals is an essential technique for many formal methods. Due to the importance of bounding function outputs, several techniques have been proposed for this problem, such as interval arithmetic, affine arithmetic, and Taylor models. While all methods provide guaranteed bounds, it is typically unknown to a formal verification tool which approach is best suitable for a given problem. For this reason, we present an implementation of the aforementioned techniques in our MATLAB tool CORA so that advantages and disadvantages of different techniques can be quickly explored without having to compile code. In this work we present the implementation of Taylor models and affine arithmetic; our interval arithmetic implementation has already been published. We evaluate the performance of our implementation using a set of benchmarks against Flow* and INTLAB. To the best of our knowledge, we have also evaluated for the first time how a combination of interval arithmetic and Taylor models performs: our results indicate that this combination is faster and more accurate than only using Taylor models.
Analysis and Design of a Multi-Mode Wake-Up Receiver Based on Direct Envelope Detection in Wireless Sensor Networks

In this paper, a multi-mode ultra-low power wake-up receiver based on direct envelope detection in HF/UHF band is analyzed and presented. Narrow-band LC matching network with passive voltage gain, active common source envelope detector with high conversion gain and baseband modules with signal amplification are clarified, separately. In order to balance the power consumption and sensitivity, multi-mode with power detection (PD), signal detection (SD), and high sensitivity signal detection (HSSD) are adopted. The PD mode is used to monitor the incident radio power, while in SD/HSSD modes, the wake-up sequences of 100/10 kb/s are detected with different power consumption and sensitivity. The presented wake-up receiver prototype is fabricated in a 95-nm CMOS process with an area of 0.6*0.3 mm2. Measurement results show that the wake-up receiver achieves a sensitivity of −45/−42 dBm, −43/−40 dBm and −58/−55 dBm while dissipating 120 nW, 600 nW, and 1 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> in PD mode, SD mode, and HSSD mode, respectively, while working in the HF/UHF band, separately.
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness

Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.
Feature-Rich Ego-Network Circles in Mobile Phone Graphs: Tie Multiplexity and the Role of Alters

A well-known result in social science states that, although people maintain a large number of social relationships, in practice, their interactions are concentrated mostly on a small portion of their neighbors. According to Dunbar's hypothesis, this phenomenon is due to a limited human cognitive capacity to handle too many social relationships, together with a few time for socializing. These constraints result in an organization of people's ego-network into four or five groups depending on the strength of the interactions, the so called Dunbar's circles. The verification of the Dunbar's hypothesis, the identification of social circles and their characterization are still open questions, although researchers have found evidence of its validity on different social networks, from small offline networks to online social networks, such as Twitter and Facebook. In addition, little is known about the semantic aspects of the circles, i.e. who are the members of each circle. In this paper, we cope with these issues by analyzing a mobile phone graph where people's interactions are expressed by both voice calls and text messages. We firstly compare two methods for the identification of the circles, which rely on the different definitions of strength proposed in literature. Both methods confirm the subdivision of ego-networks into four or five circles, each characterized by a specific interaction strength. Then, we validate the Dunbar's hypothesis and, by leveraging some powerful features of the mobile phone dataset in use, we provide a first semantic characterization of social circles. We show, for instance, that people maintain relationships with the members of the closest circles by combining calls and texts. In addition, by detecting home and work locations of each individual, we highlight that a semantic aspect, such as the role of the alters, impacts on the ego-network circles. In particular, family members and workmates are located in different circles: the former mainly form the closest circle, i.e. the support clique, while the latter are distributed among the outer circles.
DC-link Ripple Current Reduction in Back-to-back converters with DPWM

The DC-link capacitor is the most vulnerable element of a voltage conversion system. The ripple current, flowing through it, causes thermal processes, which shorten the lifespan of the component. When a discontinuous pulse width modulation (DPWM) is applied to a back-to-back converter, the DC-link ripple current increases at the intervals with the different clamping directions of both converters i.e. the first converter’s reference voltage is attached to the positive bus of the DC-link, while the reference voltage of the second converter is connected to the opposite bus or vice versa. Alternatively, the ripple current is lower when the clamping states of both inverters are the same. This paper proposes a reduction method of the DC-link capacitor ripple current of the back-to-back converters using the DPWM, considering all the clamping states. The effectiveness of the proposed method is verified by simulation.
A Generalized Fractional Program for Maximizing Content Popularity in Online Social Networks

In this paper, we consider a “generalized” fractional program in order to solve a popularity optimization problem in which a source of contents controls the topics of her contents and the rate with which posts are sent to a time line. The objective of the source is to maximize its overall popularity in an Online Social Network (OSN). We propose an efficient algorithm that converges to the optimal solution of the Popularity maximization problem.
Analyzing Preferential Attachment in Peer-to-Peer BITCOIN Networks

Due to the anonymous and remote nature of Electronic Commerce (E-commerce), reviews of products and vendors left by previous customers have emerged as an integral part of most online transactions. The reviews may influence the decision of customers buying the product since E-commerce websites/services do not allow customers to validate and inspect products in-store. In this paper, we analyze data from two BITCOIN marketplaces which include transactions between marketplace users and the ratings of those transactions given by those users. In this analysis we create a synthetic network model with similar topological properties as the networks of the interactions of both marketplaces. The results of our analysis show an interesting phenomenon in which user ratings, which range from −10 to 10, converge to a value of approximately two as the number of a user's transactions increase. Finally, we suggest future work on our synthetic model to improve its agreement with the transaction networks in order to better understand how reviews influence user decisions on transactions.
A simulated annealing approach to the student-project allocation problem

We describe a solution to the student-project allocation problem using simulated annealing. The problem involves assigning students to projects, where each student has ranked a fixed number of projects in order of preference. Each project is offered by a specific supervisor (or supervisors), and the goal is to find an optimal matching of students to projects taking into account the students' preferences, the constraint that only one student can be assigned to a given project, and the constraint that supervisors have a maximum workload. We show that when applied to a real dataset from a university physics department, simulated annealing allows the rapid determination of high quality solutions to this allocation problem. The quality of the solution is quantified by a satisfaction metric derived from empirical student survey data. Our approach provides high quality allocations in a matter of minutes that are as good as those found previously by the course organizer using a laborious trial-and-error approach. We investigate how the quality of the allocation is affected by the ratio of the number of projects offered to the number of students and the number of projects ranked by each student. We briefly discuss how our approach can be generalized to include other types of constraints and discuss its potential applicability to wider allocation problems.We describe a solution to the student-project allocation problem using simulated annealing. The problem involves assigning students to projects, where each student has ranked a fixed number of projects in order of preference. Each project is offered by a specific supervisor (or supervisors), and the goal is to find an optimal matching of students to projects taking into account the students' preferences, the constraint that only one student can be assigned to a given project, and the constraint that supervisors have a maximum workload. We show that when applied to a real dataset from a university physics department, simulated annealing allows the rapid determination of high quality solutions to this allocation problem. The quality of the solution is quantified by a satisfaction metric derived from empirical student survey data. Our approach provides high quality allocations in a matter of minutes that are a.