Miracle-dz/UniFAR-FTUs huggingface dataset:
A 60 GHz low noise variable gain amplifier with small noise figure and IIP3 variation in a 40-nm CMOS technology

This paper presents the design and measurement of a low noise variable gain amplifier (VGA) over the frequency band of 57 GHz and 64 GHz, using 40-nm CMOS technology. The design applies an inductive degeneration technique for simultaneously noise and power matching, a layout gate inductance for gain frequency extension, and current steering technique for gain tuning. The comparison of a low noise amplifier (LNA) and a low noise VGA is done, in terms of noise figure and linearity (IIP3). The LNA achieves 6.7 dB gain and 4.3 dB noise figure (NF) at 60 GHz, while consuming 12 mA from a 1.1 V supply. The IIP3 of the LNA is −12.8 dBm. The VGA has an S21 of 6.67 dB (maximum) and 5.1 dB NF at 58 GHz. The gain tuning range is 6 dB and the NF deviation is 0.8 dB at 60 GHz and the IIP3 is −7.66 dBm at the maximum gain state. The supply voltage of the VGA is 1.1 V and the DC current is 11 mA.
Adaptive Real-Time Predictive Collaborative Content Discovery and Retrieval in Mobile Disconnection Prone Networks

Emerging mobile environments motivate the need for the development of new distributed technologies which are able to support dynamic peer to peer content sharing, decrease high operating costs, and handle intermittent disconnections. In this paper, we investigate complex challenges related to themobile disconnection tolerant discovery of content that may be stored in mobile devices and its delivery to the requesting nodes in mobile resource-constrained heterogeneous environments. We propose a new adaptive real-time predictive multi-layer caching and forwarding approach, CafRepCache, which is collaborative, resource, latency, and content aware. CafRepCache comprises multiple multi-layer complementary realtime distributed predictive heuristics which allow it to respond and adapt to time-varying network topology, dynamically changing resources, and workloads while managing complex dynamic tradeoffs between them in real time. We extensively evaluate our work against three competitive protocols across a range of metrics over three heterogeneous real-world mobility traces in the face of vastly different workloads and content popularity patterns. We show that CafRepCache consistently maintains higher cache availability, efficiency and success ratios while keeping lower delays, packet loss rates, and caching footprint compared to the three competing protocols across three traces when dynamically varying content popularity and dynamic mobility of content publishers and subscribers. We also show that the computational cost and network overheads of CafRepCache are only marginally increased compared with the other competing protocols. INDEX TERMS Mobile disconnection tolerant networks, content discovery and retrieval, content caching, latency awareness, congestion awareness.
Applying S-transform and SVM to evaluate insulator's pollution condition based on leakage current

The flashover of polluted insulators is a serious threat to the safe operation of overhead power lines. The leakage current on surface of insulators is closely related to the developing process of pollution flashover, affected by many factors, such as wetting degree, system voltage and structures of insulators. At the same time, the leakage current is easy to be monitored on-line continuously. Hence, as one of on-line monitoring methods for polluted insulators. Based on artificial pollution tests under different relative humidity (RH) and salt deposit density (SDD), a method incorporates S-transform and support vector machine (SVM) for evaluating safety condition of polluted insulators is proposed. Firstly, the approach utilizes S-transform to extract the amplitude Ih, phase θ and total harmonic distortion (THD) of leakage current. Then the Ih, θ and THD are as characteristic parameters to put into SVM, the output parameters of SVM are fuzzifled into four fuzzy subsets for evaluating safety condition of polluted insulators. Lastly, the performance of SVM is validated by 8 pairs of testing samples, and the influences of SDD and RH on the performance of insulators are discussed. The results show that, the leakage current becomes inductive when strong local arcs occur, decreases with the increase of RH, and Ih has no relation with SDD when the RH is low but increases with the increase of SDD when RH is high. THD of leakage current increases slowly with the increase of RH when the pollution is light, but decreases firstly and then increases with the increase of RH when the pollution is middle or heavy, THD take the minimum at the RH of about 80%.
A Distributed Power Control of Series-Connected Module-Integrated Inverters for PV Grid-Tied Applications

To enhance the redundancy and reliability for a distributed generation system, a grid-tied photovoltaic (PV) generation system based on series-connected module-integrated inverters (SC-MIIs) is presented in this paper. In the grid-tied SC-MII system, each PV panel is interfaced with an MII with independent maximum power point tracking to harvest maximum solar energy. The outputs of MIIs are at ac line frequency and are connected in series to fulfill the voltage requirement in the utility grid. Since the high step-up power conversion stage in the conventional microinverter is avoided, the grid-tied SC-MII system is easier to implement and features high efficiency. Meanwhile, a distributed control strategy for SC-MIIs is proposed, in which the active power sharing among the MIIs depends on the individual maximum power available from PV panels, while the reactive power of the system can be regulated by any of the MIIs according to the utility grid command. Simulation and experimental results verify the feasibility and effectiveness of the proposed system and its corresponding control strategy.
A Supervised Learning Approach to Build a Recommendation System for User-Generated Content in a Casual Game

There is a growing tendency in games that made use of usergenerated content, artifacts created for a game by the players instead of game developers. This type of content may allow games to have an extension of playable hours and then a greater longevity. However, allowing users to provide their own content may result in low-quality game experience to other players, caused by the difficulty to find a content that delight the user base that will consume the user-generated content. In this paper, we present an approach to creating pleasant content recommendations to the users. We propose the use of Supervised Learning to build a model capable of predicting positive ratings from a user over a specific content and use this information to build the recommendation system. The recommendation system is capable to handle each user individually and create their own content recommendation list. To validate and measure our strategy performance, we collected user ratings of stages created by others user in a mobile puzzle game called Mr. Square, during a period of 5 months. Our results show that we can improve the percentage of positively rated content, and then the user satisfaction, to 96.2%, while the current strategy implemented in Mr. Square holds a satisfaction of only 70.1%.
Evaluation of algorithms for multipath route selection over the Internet

Resilience as well as other objectives like censorship-resistance demand the existence of multiple diverse paths between two hosts on the Internet. On Internet level, this requires the use of an overlay approach. In this paper, we study this problem on the basis of a data set obtained from traces between Internet hosts on PlanetLab. We study a variety of path selection algorithms, including adapted versions of Suurballe's algorithm. We find that these can outperform the single-hop relay approach that is more commonly proposed for Internet multipath. Our analysis framework imports traceroute data and it generates graph representations of the overlay and the underlay. We can reduce the granularity of information that the algorithms can access. Topology-aware algorithms like Suurballe profit from better information. We also support a variety of evaluation metrics.
Simulation research on the development of vacuum flashover through outgassing and discharge

A particle-in-cell and Monte-Carlo collision (PIC-MCC) model is constructed in order to emulate the development of surface flashover on insulator in vacuum, which involves secondary electron emission avalanche (SEEA), outgassing, and ionization. It is found that multipactor first expands from cathode to anode, subsequently, an ionization avalanche due to increased local pressure occurs near anode and leads to gas breakdown. The spatial distribution of electrons is presented in both saturated secondary electron emission (SSEE) stage and the initiation of gas breakdown stage. Time evolution of electron, ion, desorbed gas molecule and surface charge are investigated in detail. Also the influence of applied voltage and insulator length on flashover are studied carefully. Simulation results show that higher applied voltage enhances the electron collisions on insulator and the speed of pressure build-up. Meanwhile, longer insulator is more vulnerable under the same applied electric field on account that electrons are further accelerated in a greater distance, possessing higher energy and triggering consecutive ionizations more rampantly.
Efficient and Secure Top-k Queries With Top Order-Preserving Encryption

Top-k queries can retrieve the most relevant tuples from massive datasets and have wide implementations, such as PageRank, healthcare analytics, and decision making. The increasing demands of outsourcing large datasets to public clouds with privacy concern expect new techniques to securely perform top-k queries on encrypted data on the cloud servers. Order-preserving encryption (OPE) can be used for answering top-k queries correctly and naturally. However, it is over qualified since it unnecessarily leaks too much information (i.e., orders of non-top-k values). In this paper, we propose a mutable top OPE (TOPE) to first enable top-1 (min or max) queries on encrypted data with minimized information leakage. Then, we extend this TOPE to support top-k queries in general. With TOPE, the ciphertexts of top-k values are still the top-k in the ciphertext domain, while the ciphertexts of non-top-k values are in meaningless order. In addition, we rigorously define and prove the security of TOPE with indistinguishability under top-ordered chosen-plaintext attacks. We implement our scheme on synthetic and real datasets to show its effectiveness and efficiency. The search performance of top-k queries on massive TOPE ciphertexts with our scheme is almost as fast as on the plaintexts.
OTF: Optical Torus-based fault-tolerant DCN

Data center performance is a function of three features; bandwidth, latency, and reliability. Increasing bandwidth and reducing power consumption can be accessed through optical technology adopted in recent designs. However, the problem of fault tolerance in optical interconnection networks has been raised less. So, in this paper, we propose a fault-tolerant, scalable, and high-performance optical architecture which takes advantages of novel passive photonic switches and routing strategy in order to provide non-blocking and low-power data communication intra data centers. Due to the passive optical components utilized in the proposed optical Torus, OTF, in comparison to WaveCube with the same topology, it results in less power consumption, transmission latency, and cost. Moreover, the proposed fault-tolerant method, which can be adopted in any arbitrary network, leads to throughput increment 40% more than the WaveCube at the presence of network failure, with the same wiring complexity and less latency and power consumption.
Lifted Marginal MAP Inference

Lifted inference reduces the complexity of inference in relational probabilistic models by identifying groups of constants (or atoms) which behave symmetric to each other. A number of techniques have been proposed in the literature for lifting marginal as well MAP inference. We present the first application of lifting rules for marginal-MAP (MMAP), an important inference problem in models having latent (random) variables. Our main contribution is two fold: (1) we define a new equivalence class of (logical) variables, called Single Occurrence for MAX (SOM), and show that solution lies at extreme with respect to the SOM variables, i.e., predicate groundings differing only in the instantiation of the SOM variables take the same truth value (2) we define a sub-class {\em SOM-R} (SOM Reduce) and exploit properties of extreme assignments to show that MMAP inference can be performed by reducing the domain of SOM-R variables to a single constant.We refer to our lifting technique as the {\em SOM-R} rule for lifted MMAP. Combined with existing rules such as decomposer and binomial, this results in a powerful framework for lifted MMAP. Experiments on three benchmark domains show significant gains in both time and memory compared to ground inference as well as lifted approaches not using SOM-R.
Gateway selection based on game theory in internet of things

In the rapid development of wireless communication network technology, the application of Internet of Things (IoT) is increasing day by day. Because the number of end devices is increasing, there is one of important issues about gateway selection for end devices. Therefore, it is important to select the most effective gateway for the application of Internet of Things. Gateway Selection strategy will be a more significant topic at the moment. In recent years, game theory in the network resource allocation or simulation of resource competition has a wide range of applications, not only can clearly understand the game model, but also that the game's equilibrium. In this paper, we focus on non-cooperative dynamic game with end devices for the game players and IoT gateways for the selection strategy. The utility function is derived from the characteristics of players and IoT gateways. Finally, we compare with the maximum received signal strength indicator to meet the overall demands of the end devices and the IoT gateway maximum load difference are superior to the traditional way.
Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages

Machine translation from polysynthetic to fusional languages is a challenging task, which gets further complicated by the limited amount of parallel text available. Thus, translation performance is far from the state of the art for high-resource and more intensively studied language pairs. To shed light on the phenomena which hamper automatic translation to and from polysynthetic languages, we study translations from three low-resource, polysynthetic languages (Nahuatl, Wixarika and Yorem Nokki) into Spanish and vice versa. Doing so, we find that in a morpheme-to-morpheme alignment an important amount of information contained in polysynthetic morphemes has no Spanish counterpart, and its translation is often omitted. We further conduct a qualitative analysis and, thus, identify morpheme types that are commonly hard to align or ignored in the translation process.
A Dependency Perspective on RST Discourse Parsing and Evaluation

Computational text-level discourse analysis mostly happens within Rhetorical Structure Theory (RST), whose structures have classically been presented as constituency trees, and relies on data from the RST Discourse Treebank (RST-DT); as a result, the RST discourse parsing community has largely borrowed from the syntactic constituency parsing community. The standard evaluation procedure for RST discourse parsers is thus a simplified variant of PARSEVAL, and most RST discourse parsers use techniques that originated in syntactic constituency parsing. In this article, we isolate a number of conceptual and computational problems with the constituency hypothesis. We then examine the consequences, for the implementation and evaluation of RST discourse parsers, of adopting a dependency perspective on RST structures, a view advocated so far only by a few approaches to discourse parsing. While doing that, we show the importance of the notion of headedness of RST structures. We analyze RST discourse parsing as dependency parsing by adapting to RST a recent proposal in syntactic parsing that relies on head-ordered dependency trees, a representation isomorphic to headed constituency trees. We show how to convert the original trees from the RST corpus, RST-DT, and their binarized versions used by all existing RST parsers to head-ordered dependency trees. We also propose a way to convert existing simple dependency parser output to constituent trees. This allows us to evaluate and to compare approaches from both constituent-based and dependency-based perspectives in a unified framework, using constituency and dependency metrics. We thus propose an evaluation framework to compare extant approaches easily and uniformly, something the RST parsing community has lacked up to now. We can also compare parsers’ predictions to each other across frameworks. This allows us to characterize families of parsing strategies across the different frameworks, in particular with respect to the notion of headedness. Our experiments provide evidence for the conceptual similarities between dependency parsers and shift-reduce constituency parsers, and confirm that dependency parsing constitutes a viable approach to RST discourse parsing.
Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network

This paper presents a wearable inertial sensor network and its associated activity recognition algorithm for accurately recognizing human daily and sport activities. The proposed wearable inertial sensor network is composed of two wearable inertial sensing devices, which comprise a microcontroller, a triaxial accelerometer, a triaxial gyroscope, an RF wireless transmission module, and a power supply circuit. The activity recognition algorithm, consisting of procedures of motion signal acquisition, signal preprocessing, dynamic human motion detection, signal normalization, feature extraction, feature normalization, feature reduction, and activity recognition, has been developed to recognize human daily and sport activities by using accelerations and angular velocities. In order to reduce the computational complexity and improve the recognition rate simultaneously, we have utilized the nonparametric weighted feature extraction algorithm with the principal component analysis method for reducing the feature dimensions of inertial signals. All 23 participants wore the wearable sensor network on their wrist and ankle to execute 10 common domestic activities in human daily lives and 11 sport activities in a laboratory environment, and their activity recordings were collected to validate the effectiveness of the proposed wearable inertial sensor network and activity recognition algorithm. Experimental results showed that our approach could achieve recognition rates for the 10 common domestic activities of 98.23% and 11 sport activities of 99.55% by the 10-fold cross-validation strategy, which have successfully validated the effectiveness of the proposed wearable inertial sensor network and its activity recognition algorithm.
Ambient Hidden Space of Generative Adversarial Networks

Generative adversarial models are powerful tools to model structure in complex distributions for a variety of tasks. Current techniques for learning generative models require an access to samples which have high quality, and advanced generative models are applied to generate samples from noisy training data through ambient modules. However, the modules are only practical for the output space of the generator, and their application in the hidden space is not well studied. In this paper, we extend the ambient module to the hidden space of the generator, and provide the uniqueness condition and the corresponding strategy for the ambient hidden generator in the adversarial training process. We report the practicality of the proposed method on the benchmark dataset.
In Vitro and Ex Vivo Chemopreventive Action of Mauritia flexuosa Products

Mauritia flexuosa (Arecaceae), known as “Buriti,” is a Brazilian palm tree with high economic potential for local communities. Herein, we investigated the phytochemistry profile and antioxidant potential of M. flexuosa fruits and determined the bioaccessibility of phenolic compounds. Peels revealed upper values for phenols, flavonoids, carotenoids, tannins, and ascorbic acid when compared to the pulps and endocarps. All samples showed capacity to scavenger free radicals (0.5, 1.0, 2.0, 4.0, and 8.0 mg/mL) but peels presented higher scavenger action in all methods explored. Phenolic compounds identified by HPLC displayed reduced bioaccessibility after in vitro simulated gastrointestinal digestion for pulp (38.7%), peel (18.7%), and endocarp (22.3%) extracts (P < 0.05). Buriti fruits also protected rat blood cells against lysis induced by peroxyl radicals. We demonstrated the promising chemopreventive potentialities of M. flexuosa fruits and their by-products and peels with higher quantities of bioactive compounds and phenolic substances before and after in vitro bioaccessibility investigation. In Brazil, these parts are discarded or underused, mainly as feed for ruminant animals. Consequently, it is extremely important to explore nutritional characteristics of these by-products for human/livestock foods and to install biofriendly techniques and sustainable biotechnology handling of natural resources.
Emotional Differences in Young and Older Adults: Films as Mood Induction Procedure

Film clips are proven to be one of the most efficient techniques in emotional induction. However, there is scant literature on the effect of this procedure in older adults and, specifically, the effect of using different positive stimuli. Thus, the aim of the present study was to examine emotional differences between young and older adults and to know how a set of film clips works as mood induction procedure in older adults, especially, when trying to elicit attachment-related emotions. To this end, we use this procedure to analyze differences in subjective emotional response between young and older adults. A sample of 57 older adults and 83 young adults watched a film set previously validated in young population. Their responses were studied in an individual laboratory session to elicit 6 target emotions (disgust, fear, sadness, anger, amusement and tenderness) and neutral state. Self-reported emotional experience was measured using the Self-Assessment Manikin (SAM). Our results show that film clips are capable of evoking positive and negative emotions in older adults. Furthermore, older adults experienced more intensely negative emotions than young adults, especially in response to disgust and fear clips. They also reported higher arousal than young adults, especially in the case of sadness, anger and tenderness clips. Nevertheless, the older adults recovered more easily from the effects of the emotion induction. The young adults reported higher arousal ratings than older adults in response to amusement film clips. On the other hand, this study reflects the importance of controlling the baseline state to study the real strength of mood induction. Overall, current data suggests significant differences occur in emotional response in adult age and that film clips are an effective tool for studying positive and negative emotions in aging research.