Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative optimization of photonic crystal nanocavity designs by using deep neural networks

Abstract Devices based on two-dimensional photonic-crystal nanocavities, which are defined by their air hole patterns, usually require a high quality (Q) factor to achieve high performance. We demonstrate that hole patterns with very high Q factors can be efficiently found by the iteration procedure consisting of machine learning of the relation between the hole pattern and the corresponding Q factor and new dataset generation based on the regression function obtained by machine learning. First, a dataset comprising randomly generated cavity structures and their first principles Q factors is prepared. Then a deep neural network is trained using the initial dataset to obtain a regression function that approximately predicts the Q factors from the structural parameters. Several candidates for higher Q factors are chosen by searching the parameter space using the regression function. After adding these new structures and their first principles Q factors to the training dataset, the above process is repeated. As an example, a standard silicon-based L3 cavity is optimized by this method. A cavity design with a high Q factor exceeding 11 million is found within 101 iteration steps and a total of 8070 cavity structures. This theoretical Q factor is more than twice the previously reported record values of the cavity designs detected by the evolutionary algorithm and the leaky mode visualization method. It is found that structures with higher Q factors can be detected within less iteration steps by exploring not only the parameter space near the present highest-Q structure but also that distant from the present dataset.
Universality of the least singular value for the sum of random matrices

We consider the least singular value of $M = R^* X T + U^* YV$, where $R,T,U,V$ are independent Haar-distributed unitary matrices and $X, Y$ are deterministic diagonal matrices. Under weak conditions on $X$ and $Y$, we show that the limiting distribution of the least singular value of $M$, suitably rescaled, is the same as the limiting distribution for the least singular value of a matrix of i.i. d. gaussian random variables. Our proof is based on the dynamical method used by Che and Landon to study the local spectral statistics of sums of Hermitian matrices.
Interference Exploitation 1-Bit Massive MIMO Precoding: A Partial Branch-and-Bound Solution With Near-Optimal Performance

In this paper, we focus on 1-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI). For both PSK and QAM signaling, we firstly formulate the optimization problem that maximizes the CI effect subject to the requirement of the 1-bit transmit signals. We then mathematically prove that, when employing the CI formulation and relaxing the 1-bit constraint, the majority of the transmit signals already satisfy the 1-bit formulation. Building upon this important observation, we propose a 1-bit precoding approach that further improves the performance of the conventional 1-bit CI precoding via a partial branch-and-bound (P-BB) process, where the BB procedure is performed only for the entries that do not comply with the 1-bit requirement. This operation allows a significant complexity reduction compared to the fully-BB (F-BB) process, and enables the BB framework to be applicable to the complex massive MIMO scenarios. We further develop an alternative 1-bit scheme through an ‘Ordered Partial Sequential Update’ (OPSU) process that allows an additional complexity reduction. Numerical results show that both proposed 1-bit precoding methods exhibit a significant signal-to-noise ratio (SNR) gain for the error rate performance, especially for higher-order modulations.
Fallback Strategies in Operation Control of Microgrids with Communication Failures

This paper proposes a model predictive control (MPC)-based energy management system to address communication failures in an islanded microgrid (MG). The energy management system relies on a communication network to monitor and control power generation in the units. A communication failure to a unit inhibits the ability of the MPC to change the power of the unit. However, this unit is electrically connected to the network and ignoring this aspect could have adverse effect in the operation of the microgrid. Therefore, this paper considers an MPC design that includes the electrical connectivity of the unit during communication failures. This paper also highlights the benefit of including the lower layer control behaviour of the MG to withstand communication failures. The proposed approaches are validated with a case study.
Multi-Frame Content Integration with a Spatio-Temporal Attention Mechanism for Person Video Motion Transfer

Existing person video generation methods either lack the flexibility in controlling both the appearance and motion, or fail to preserve detailed appearance and temporal consistency. In this paper, we tackle the problem of motion transfer for generating person videos, which provides controls on both the appearance and the motion. Specifically, we transfer the motion of one person in a target video to another person in a source video, while preserving the appearance of the source person. Besides only relying on one source frame as the existing state-of-the-art methods, our proposed method integrates information from multiple source frames based on a spatio-temporal attention mechanism to preserve rich appearance details. In addition to a spatial discriminator employed for encouraging the frame-level fidelity, a multi-range temporal discriminator is adopted to enforce the generated video to resemble temporal dynamics of a real video in various time ranges. A challenging real-world dataset, which contains about 500 dancing video clips with complex and unpredictable motions, is collected for the training and testing. Extensive experiments show that the proposed method can produce more photo-realistic and temporally consistent person videos than previous methods. As our method decomposes the syntheses of the foreground and background into two branches, a flexible background substitution application can also be achieved.
Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays

The brain performs intelligent tasks with extremely low energy consumption. This work takes its inspiration from two strategies used by the brain to achieve this energy efficiency: the absence of separation between computing and memory functions and reliance on low-precision computation. The emergence of resistive memory technologies indeed provides an opportunity to tightly co-integrate logic and memory in hardware. In parallel, the recently proposed concept of a Binarized Neural Network, where multiplications are replaced by exclusive NOR (XNOR) logic gates, offers a way to implement artificial intelligence using very low precision computation. In this work, we therefore propose a strategy for implementing low-energy Binarized Neural Networks that employs brain-inspired concepts while retaining the energy benefits of digital electronics. We design, fabricate, and test a memory array, including periphery and sensing circuits, that is optimized for this in-memory computing scheme. Our circuit employs hafnium oxide resistive memory integrated in the back end of line of a 130-nm CMOS process, in a two-transistor, two-resistor cell, which allows the exclusive NOR operations of the neural network to be performed directly within the sense amplifiers. We show, based on extensive electrical measurements, that our design allows a reduction in the number of bit errors on the synaptic weights without the use of formal error-correcting codes. We design a whole system using this memory array. We show on standard machine learning tasks (MNIST, CIFAR-10, ImageNet, and an ECG task) that the system has inherent resilience to bit errors. We evidence that its energy consumption is attractive compared to more standard approaches and that it can use memory devices in regimes where they exhibit particularly low programming energy and high endurance. We conclude the work by discussing how it associates biologically plausible ideas with more traditional digital electronics concepts.
Optimal charging guidance strategies for electric vehicles by considering dynamic charging requests in a time-varying road network

Electric vehicles (EVs) have enjoyed increasing adoption because of the global concerns about the petroleum dependence and greenhouse gas emissions. However, their limited driving range fosters the occurrence of charging requests deriving from EV drivers in urban road networks, which have significant uncertain characteristic from time dimension in the real-world situation. To tackle the challenge brought by the dynamic charging requests, this study is devoted to proposing optimal strategies to provide guidance for EV charging. The time-varying characteristic of road network is further involved in the problem formulation. Based on the charging request information, we propose two charging guidance strategies from different perspectives. One of the strategies considers the travel demands of EV drivers and uses the driving distance as the optimization criterion. In contrast, the other strategy focuses on the impacts of EV number on the charging station operation and service satisfaction. The reachable charging stations with minimum vehicle number are selected as the optimal ones. More importantly, both the strategies have the ability to ensure the reachability of selected charging stations in a time-varying road network. In addition, we conduct simulation examples to investigate the performance of the proposed charging guidance strategies. Besides, the insights and recommendations on application scenarios of the strategies are introduced according to the simulation results under various parameter scenarios.
Feasibility of Acousto-Electric Tomography.

In acousto-electric tomography the goal is to reconstruct the electric conductivity in a domain from electrostatic boundary measurements of corresponding currents and voltages, while the domain is penetrated by a time-dependent acoustic wave. We explicitly model the phenomena, and we propose a complete inversion framework for acousto-electric tomography in two steps: First the interior power density is obtained from boundary measurements by solving a linear, ill-posed problem; second the interior conductivity is reconstructed from the power density by solving a non-linear, fairly well-posed problem. We perform numerical experiments on synthetic data with realistically chosen parameters. We investigate how feasibility of reconstructing the electrical conductivity from boundary measurements depends on the acousto-electric coupling constant and measurement noise. Our findings are positive, and indicate that AET is indeed feasible for interesting applications in for example medical imaging. Finally, we consider a limited angle setup and show that the conductivity is well reconstructed near the measurement boundary.
Personal VAD: Speaker-Conditioned Voice Activity Detection

In this paper, we propose "personal VAD", a system to detect the voice activity of a target speaker at the frame level. This system is useful for gating the inputs to a streaming on-device speech recognition system, such that it only triggers for the target user, which helps reduce the computational cost and battery consumption, especially in scenarios where a keyword detector is unpreferable. We achieve this by training a VAD-alike neural network that is conditioned on the target speaker embedding or the speaker verification score. For each frame, personal VAD outputs the probabilities for three classes: non-speech, target speaker speech, and non-target speaker speech. Under our optimal setup, we are able to train a model with only 130K parameters that outperforms a baseline system where individually trained standard VAD and speaker recognition networks are combined to perform the same task.
Sentence Specified Dynamic Video Thumbnail Generation

With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences. Conventional video thumbnails are generated once purely based on the visual characteristics of videos, and then displayed as requested. Hence, such video thumbnails, without considering the users' searching intentions, cannot provide a meaningful snapshot of the video contents that users concern. In this paper, we define a distinctively new task, namely sentence specified dynamic video thumbnail generation, where the generated thumbnails not only provide a concise preview of the original video contents but also dynamically relate to the users' searching intentions with semantic correspondences to the users' query sentences. To tackle such a challenging task, we propose a novel graph convolved video thumbnail pointer (GTP). Specifically, GTP leverages a sentence specified video graph convolutional network to model both the sentence-video semantic interaction and the internal video relationships incorporated with the sentence information, based on which a temporal conditioned pointer network is then introduced to sequentially generate the sentence specified video thumbnails. Moreover, we annotate a new dataset based on ActivityNet Captions for the proposed new task, which consists of 10,000+ video-sentence pairs with each accompanied by an annotated sentence specified video thumbnail. We demonstrate that our proposed GTP outperforms several baseline methods on the created dataset, and thus believe that our initial results along with the release of the new dataset will inspire further research on sentence specified dynamic video thumbnail generation. Dataset and code are available at https://github.com/yytzsy/GTP
From User Tasks to User Services: Placing the Functional Requirements for Bibliographic Records Models Into a Larger Framework

ABSTRACT This discussion illustrates how the functional requirements for bibliographic records-family user tasks inform services to all users. The user tasks are placed into a larger context called the want-find-get-manage framework. In combination with the user tasks, want-find-get-manage facilitates a programmatic approach to the catalog specifically and to services in general. By doing so, traditional bibliographic content and services may become more effective while opening avenues for new resources and services, such as those built around born-digital content and non-bibliographic metadata. As a result, libraries become full partners with users, enabling innovation, learning, and growth.
Control strategy of grid-connected photovoltaic generation system based on GMPPT method

There are multiple local maximum power points when photovoltaic (PV) array runs under partial shading condition (PSC). However, the traditional maximum power point tracking (MPPT) algorithm might be easily trapped in local maximum power points (MPPs) and cannot find the global maximum power point (GMPP). To solve such problem, a global maximum power point tracking method (GMPPT) is improved, combined with traditional MPPT method and particle swarm optimization (PSO) algorithm. Under different operating conditions of PV cells, different tracking algorithms are used. When the environment changes, the improved PSO algorithm is adopted to realize the global optimal search, and the variable step incremental conductance (INC) method is adopted to achieve MPPT in optimal local location. Based on the simulation model of the PV grid system built in Matlab/Simulink, comparative analysis of the tracking effect of MPPT by the proposed control algorithm and the traditional MPPT method under the uniform solar condition and PSC, validate the correctness, feasibility and effectiveness of the proposed control strategy.
Outlier Detection on Mixed Type Data by Using AVF and Z-Score Algorithm

Outliers are objects that deviate from other objects in general. It needs special attention because it can interfere to the data distribution and make the discovery of important information from the data set become more difficult. Many methods have been developed to detect the outliers. However, these methods focus only on the categorical data or numerical data separately. In real life, facts are described by using mixed type data. In this paper, the outlier detection was carried out on mixed type data by using AVF algorithm and Z-Score algorithm. The Z-Score algorithm was used to detect outlier in the numerical data. The AVF algorithm was used to detect outlier in the categorical data. By combining the outlier rankings from the AVF algorithm and the Z-Score algorithm, some outliers in the sales transaction data in UGM Pharmacy could be found. The detected outliers were peculiarities in the pattern of the sale transactions in UGM Pharmacy from the 1 st to 16 th of April 2016.
No-Reference Image Quality Assessment Based on Edges

Image quality assessment is a crucial topic in the field of image processing. In this paper, we propose an edge-based no-reference image quality assessment method. The following factor is applied to assess image quality, namely, improved blur measurement. In the improved blur measurement method, we propose an algorithm that improves the accuracy in measuring image blurs and attains effective execution speed in time complexity. Experimental results reveal that using the proposed approach helps attain satisfactory image quality assessment results.
Collaborative Partnership for Development of Mechatronics Engineering Education of the Future

A collaborative partnership between Royal Melbourne Institute of Technology (RMIT) University and industry partner SAGE Didactic has been developed. This partnership is born for the shared commitment to “Advancing the technical skills of today’s and tomorrow’s workforce”. This ambitious commitment engages with students in the early stages of high school with the view to exciting them about the critical role engineering trades, paraprofessional, professional and postgraduates contribute to society. To enable early student excitement to be qualified and nurtured, the collaborative partnership develops a full set of laboratory systems and support courseware that provides a robust educational experience to students, school teachers, trade and professional participants. The expected outcomes are an increased interest in the aforesaid study areas, a reduction in the drop out rate in the first and second years of higher education and progressively an expansion in the skills a graduate will present.
Novel cooperative antipredator tactics of an ant specialized against a snake

Eusocial insects can express surprisingly complex cooperative defence of the colony. Brood and reproductive castes typically remain in the nest and are protected by workers' various antipredator tactics against intruders. In Madagascar, a myrmicine ant, Aphaenogaster swammerdami, occurs sympatrically with a large blindsnake, Madatyphlops decorsei. As blindsnakes generally specialize on feeding on termites and ants brood by intruding into the nest, these snakes are presumably a serious predator on the ant. Conversely, a lamprophiid snake, Madagascarophis colubrinus, is considered to occur often in active A. swammerdami nests without being attacked. By presenting M. colubrinus, M. decorsei and a control snake, Thamnosophis lateralis, at the entrance of the nest, we observed two highly specialized interactions between ants and snakes: the acceptance of M. colubrinus into the nest and the cooperative evacuation of the brood from the nest for protection against the ant-eating M. decorsei. Given that M. colubrinus is one of the few known predators of blindsnakes in this area, A. swammerdami may protect their colonies against this blindsnake by two antipredator tactics, symbiosis with M. colubrinus and evacuation in response to intrusion by blindsnakes. These findings demonstrate that specialized predators can drive evolution of complex cooperative defence in eusocial species.
Electrospinning repellents in polyvinyl alcohol-nanofibres for obtaining mosquito-repelling fabrics

Recently, the use of repellents for preventing the transmission of mosquito-borne diseases is getting increasingly more attention. However, most of the current repellents are volatile in nature and must be frequently re-applied as their efficacy is only limited to a short period of time. Therefore, a slow release and abrasion-resistant mechanism is needed for prolonging the protection time of the repellents. The focus of this study is on the direct micro-encapsulation of repellents from an emulsion and integration of already encapsulated repellents into nanofibres via electrospinning. Different repellents were electrospun in polyvinyl alcohol (PVA) nanofibrous structures, namely p-menthane-3,8-diol micro-capsules, permethrin, chilli and catnip oil. The repellents were successfully incorporated in the nanofibres and the tensile properties of the resulting samples did not have a significant change. This means that the newly created textiles were identical to current PVA nanofibrous textiles with the added benefit of being mosquito repellent. Principally, all incorporated repellents in the nanofibrous structures showed a significantly reduced number of mosquito landings compared to the control. Consequently, the currently described method resulted in a new and very effective repelling textile material that can be used in the prevention against mosquito-associated diseases.
Assortative mating frames establishment in a young island bird population

Successful island colonizations are key events to understand range dynamic processes, but studying a young population right after it reaches establishment is a rare opportunity in natural systems. The genetic structure of a recently established population may offer unique insights into its colonization history and demographic processes that are important for a successful colonization. Here, we studied the population genetics of a recently established island population of Eurasian blackbirds (Aves: Turdus merula) located on the island of Heligoland in the German North Sea. Using microsatellites, we genotyped the majority of the island population, including the nestlings, over a 4-year period between 2004 and 2007. We also genotyped high numbers of migrants on stopover and mainland individuals, as they are potential founders of the island population. We identified two genetic clusters that comply with the migrating and mainland birds. While most of the island birds belong to the mainland cluster, some breeding individuals and a low fraction of the offspring belong to the genetic cluster found in migrating individuals with almost no admixture between the two, pointing to assortative mating acting on the island population. We did not find any evidence for founder events and detected deviations from the Hardy–Weinberg equilibrium that disappeared in cohorts of older age that coincide with a lower number of siblings in older cohorts. The observed genetic patterns unravel a complex colonization history to which migratory and mainland birds have contributed and which is characterized by assortative mating. Further research will be directed towards habitat selection and phenotypic differences as potential drivers of assortative mating in this island population.