Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution feeder loss computation by artificial neural network

This paper proposes an artificial neural network (ANN) based feeder loss calculation model for distribution system analysis. In this paper, the functional-link network model is examined to form the artificial neural network architecture to derive the various loss calculation models for feeders with different configuration. Such an artificial neural network is a feedforward network that uses a standard back-propagation algorithm to adjust weights on the connection path between any two processing elements (PEs). Feeder daily load curve in each season are derived by field test data. A three-phase load flow program is executed to create the training sets with exact loss calculation results. A sensitivity analysis is executed to determine the key factors including power factor, feeder loading primary conductors, secondary conductors, and transformer capacity as the variables for components located at the input layer. By using an artificial neural network with pattern recognition ability, this study has developed seasonal and yearly loss calculation models for overhead and underground feeder configurations. Two practical feeders with both overhead and underground configurations in the Taiwan Power Company distribution system are selected for computer simulation to demonstrate the effectiveness and accuracy of the proposed models. Compared with models derived by the conventional regression technique, results indicate that the proposed models provide more efficient tools to the district engineer for feeder loss calculation
On Impact of Semantically Similar Apps in Android Malware Datasets

Malware authors often reuse program segments from other applications (apps) to perform similar kind of malicious activities, such as information stealing and sending SMS to premium rate numbers. Consequently, multiple semantically similar malware samples may exist within a malware family or dataset. The presence of such apps, unbeknownst to many researchers, may inadvertently influence the evaluation of their Machine Learning (ML) models. In this paper, we investigate the impact of semantically similar apps on the performance measures of ML-based Android malware detectors. Through experiments on the Drebin dataset, we assessed the performance of distinct ML models based on opcode, permissions and API call features of both malware and goodware applications, with and without semantically similar apps. Our findings show that after removing exact duplicate apps from the dataset, the malware detection rate (True Positive Rate) of opcode-based ML models decreased from 0.94 to 0.85, permission-based ML models decreased from 0.94 to 0.90 and API call-based ML models decreased from 0.95 to 0.91. To address this issue, we propose the use of the Euclidean distance metric to identify and eliminate the similar features before evaluating malware detection mechanisms. Implementing this recommendation can enhance the accuracy and reliability of ML-based Android malware detectors.
Forward Compatible Training for Representation Learning

In visual retrieval systems, updating the embedding model requires recomputing features for every piece of data. This expensive process is referred to as backﬁlling. Recently, the idea of backward compatible training (BCT) was proposed. To avoid the cost of backﬁlling, BCT modiﬁes training of the new model to make its representations compatible with those of the old model. However, BCT can sig-niﬁcantly hinder the performance of the new model. In this work, we propose a new learning paradigm for representation learning: forward compatible training ( F CT ). In F CT , when the old model is trained, we also prepare for a future unknown version of the model. We propose learning side-information, an auxiliary feature for each sample which facilitates future updates of the model. To develop a powerful and ﬂexible framework for model compatibility, we combine side-information with a forward transformation from old to new embeddings. Training of the new model is not modiﬁed, hence, its accuracy is not degraded. We demonstrate signiﬁcant retrieval accuracy improvement compared to BCT for various datasets: ImageNet-1k (+ 18 . 1% ), Places-365 (+ 5 . 4% ), and VGG-Face2 ( +8 . 3% ). F CT obtains model compatibility when the new and old models are trained across different datasets, losses, and architectures.
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering

Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams. Compared with Visual Question Answering (VQA), TQA contains a large number of uncommon terminologies and various diagram inputs. It brings new challenges to the representation capability of language model for domain-specific spans. And it also pushes the multimodal fusion to a more complex level. To tackle the above issues, we propose a novel model named MoCA, which incorporates multi-stage domain pretraining and multimodal cross attention for the TQA task. Firstly, we introduce a multi-stage domain pretraining module to conduct unsupervised post-pretraining with the span mask strategy and supervised pre-finetune. Especially for domain post-pretraining, we propose a heuristic generation algorithm to employ the terminology corpus. Secondly, to fully consider the rich inputs of context and diagrams, we propose cross-guided multimodal attention to update the features of text, question diagram and instructional diagram based on a progressive strategy. Further, a dual gating mechanism is adopted to improve the model ensemble. The experimental results show the superiority of our model, which outperforms the state-of-the-art methods by 2.21% and 2.43% for validation and test split respectively.
Near ultraviolet photonic integrated lasers based on silicon nitride

Low phase noise lasers based on the combination of III–V semiconductors and silicon photonics are well established in the near-infrared spectral regime. Recent advances in the development of low-loss silicon nitride-based photonic integrated resonators have allowed them to outperform bulk external diode and fiber lasers in both phase noise and frequency agility in the 1550 nm-telecommunication window. Here, we demonstrate for the first time a hybrid integrated laser composed of a gallium nitride-based laser diode and a silicon nitride photonic chip-based microresonator operating at record low wavelengths as low as 410 nm in the near-ultraviolet wavelength region suitable for addressing atomic transitions of atoms and ions used in atomic clocks, quantum computing, or for underwater LiDAR. By self-injection locking of the Fabry–Pérot diode laser to a high-Q (0.4 × 106) photonic integrated microresonator, we reduce the optical phase noise at 461 nm by a factor greater than 100×, limited by the device quality factor and back-reflection.
CAN: Feature Co-Action for Click-Through Rate Prediction

Feature interaction has been recognized as an important problem in machine learning, which is also very essential for click-through rate (CTR) prediction tasks. In recent years, Deep Neural Networks (DNNs) can automatically learn implicit nonlinear interactions from original sparse features, and therefore have been widely used in industrial CTR prediction tasks. However, the implicit feature interactions learned in DNNs cannot fully retain the complete representation capacity of the original and empirical feature interactions (e.g., cartesian product) without loss. For example, a simple attempt to learn the combination of feature A and feature Bas the explicit cartesian product representation of new features can outperform previous implicit feature interaction models including factorization machine (FM)-based models and their variations. In this paper, we propose a Co-Action Network (CAN) to approximate the explicit pairwise feature interactions without introducing too many additional parameters. More specifically, giving feature A and its associated feature B, their feature interaction is modeled by learning two sets of parameters: 1) the embedding of feature A, and 2) a Multi-Layer Perceptron (MLP) to represent feature B. The approximated feature interaction can be obtained by passing the embedding of feature A through the MLP network of feature B. We refer to such pairwise feature interaction as feature co-action, and such a Co-Action Network unit can provide a very powerful capacity to fitting complex feature interactions. Experimental results on public and industrial datasets show that CAN outperforms state-of-the-art CTR models and the cartesian product method. Moreover, CAN has been deployed in the display advertisement system in Alibaba, obtaining 12\% improvement on CTR and 8\% on Revenue Per Mille (RPM), which is a great improvement to the business.
Gapless spin liquid behavior in a kagome Heisenberg antiferromagnet with randomly distributed hexagons of alternate bonds

We demonstrate that the new single crystal of YCu$_3$[OH(D)]$_{6.5}$Br$_{2.5}$ (YCOB) is a kagome Heisenberg antiferromagnet (KHA) without evident orphan spins ($\ll$ 0.8\%). The site mixing between polar OH$^-$ and non-polar Br$^-$ causes local distortions of Cu-O-Cu exchange paths, and gives rise to 70(2)\% of randomly distributed hexagons of alternate bonds ($\sim$ $J_1-\Delta J$ and $J_1+\Delta J$) and the rest of almost uniform hexagons ($\sim$ $J_1$) on the kagome lattice. Simulations of the random exchange model with $\Delta J$/$J_1$ = 0.7(1) show good agreement with the experimental observations, including the weak upturn seen in susceptibility and the slight polarization in magnetization. Despite the average antiferromagnetic coupling of $J_1$ $\sim$ 60 K, no conventional freezing is observed down to $T$ $\sim$ 0.001$J_1$, and the raw specific heat exhibits a nearly quadratic temperature dependence below 1 K $\sim$ 0.02$J_1$, phenomenologically consistent with a gapless (spin gap $\leq$ 0.025$J_1$) Dirac quantum spin liquid (QSL). Our result sheds new light on the theoretical understanding of the randomness-relevant gapless QSL behavior in YCOB, as well as in other relevant materials.
Learning Graph Representations for Influence Maximization

As the field of machine learning for combinatorial optimization advances, traditional problems are resurfaced and readdressed through this new perspective. The overwhelming majority of the literature focuses on small graph problems, while several real-world problems are devoted to large graphs. Here, we focus on two such problems: influence estimation, a #P-hard counting problem, and influence maximization, an NP-hard problem. We develop Glie, a Graph Neural Network (GNN) that inherently parameterizes an upper bound of influence estimation and train it on small simulated graphs. Experiments show that Glie provides accurate influence estimation for real graphs up to 10 times larger than the train set. More importantly, it can be used for influence maximization on considerably larger graphs, as the predictions ranking is not affected by the drop of accuracy. We develop a version of Cost Effective Lazy Forward optimization with Glie instead of simulated influence estimation, surpassing the benchmark for influence maximization, although with a computational overhead. To balance the time complexity and quality of influence, we propose two different approaches. The first is a Q-network that learns to choose seeds sequentially using Glie’s predictions. The second defines a provably submodular function based on Glie’s representations to rank nodes fast while building the seed set. The latter provides the best combination of time efficiency and influence spread, outperforming SOTA benchmarks.
The GHS and other correlation inequalities for the two-star model

We consider the two-star model, a family of exponential random graphs indexed by two real parameters, h and α , that rule respectively the total number of edges and the mutual dependence between them. Borrowing tools from statistical mechanics, we study diﬀerent classes of correlation inequalities for edges, that naturally emerge while taking the partial derivatives of the (ﬁnite size) free energy. In particular, if α, h ≥ 0 , we derive ﬁrst and second order correlation inequalities and then prove the so-called GHS inequality. As a consequence, under the above conditions on the parameters, the average edge density turns out to be an increasing and concave function of the parameter h , at any ﬁxed size of the graph. Some of our results can be extended to more general classes of exponential random graphs.
Robust tuning of power system stabilizers in multimachine power systems

Summary form only given as follows. This paper demonstrates the robust tuning of power systems stabilizers for power systems, operating at different loading conditions. A classical lead-lag power system stabilizer is used to demonstrate the technique. The problem of selecting the stabilizer parameters is converted to a simple optimization problem with an eigenvalue-based objective function, which is solved by a tabu search algorithm. The objective function allows the selection of the stabilizer parameters to optimally place the closed-loop eigenvalues in the left-hand side of a vertical line in the complex s-plane. The effectiveness of the stabilizers tuned using the suggested technique, in enhancing the stability of power systems, is confirmed through eigenvalue analysis and simulation results.
LQG MEAN FIELD GAMES WITH A MARKOV CHAIN AS ITS COMMON NOISE

This paper studies Mean Field Games with a common noise given by a continuous time Markov chain under a quadratic cost structure. The theory reveals the Markovian structure of the random equilibrium measure flow, which can be used to characterize the equilibrium via a finite dimensional system. The counterpart is the N-player game characterized via an ODE system, whose dimension has a polynomial growth in the number of players. The convergence of the N-player game towards Mean Field Games is proved by explicitly embedding the N-player game into one specific probability space.
Multilingual training for Software Engineering

Well-trained machine-learning models, which leverage large amounts of open-source software data, have now become an interesting approach to automating many software engineering tasks. Several SE tasks have all been subject to this approach, with performance gradually improving over the past several years with better models and training methods. More, and more diverse, clean, labeled data is better for training; but constructing good-quality datasets is time-consuming and challenging. Ways of augmenting the volume and diversity of clean, labeled data generally have wide applicability. For some languages (e.g., Ruby) labeled data is less abundant; in others (e.g., JavaScript) the available data maybe more focused on some application domains, and thus less diverse. As a way around such data bottlenecks, we present evidence suggesting that human-written code in different languages (which performs the same function), is rather similar, and particularly preserving of identifier naming patterns; we further present evidence suggesting that identifiers are a very important element of training data for software engineering tasks. We leverage this rather fortuitous phenomenon to find evidence that available multilingual training data (across different languages) can be used to amplify performance. We study this for 3 different tasks: code summarization, code retrieval, and function naming. We note that this data-augmenting approach is broadly compatible with different tasks, languages, and machine-learning models.
O C ] 6 D ec 2 02 1 Optimal and H ∞ Control of Stochastic Reaction Networks

Stochastic reaction networks is a powerful class of models for the representation a wide variety of population models including biochemistry. The control of such networks has been recently considered due to their important implications for the control of biological systems. Their optimal control, however, has been relatively few studied until now. The continuous-time finite-horizon optimal control problem is formulated first and explicitly solved in the case of unimolecular reaction networks. The problems of the optimal sampled-data control, the continuous H∞ control, and the sampled-data H∞ control of such networks are addressed next. The results in the unimolecular case take the form of nonstandard Riccati differential equations or differential Lyapunov equations coupled with difference Riccati equations, which can all be solved numerically by backward-in-time integration.
DIRECT ASSESSMENT OF STUDENT OUTCOMES: A SYSTEMATIC APPROACH

This paper presents a systematic approach to direct assessment of student learning that is adopted by an Information Technology program accredited by ABET. The focus is on the systematic process for regularly assessing and evaluating the extent to which the direct assessment of the student outcomes are being attained. This paper also describes how the results of the process are being utilized to effect continuous improvement of the program.
Application of Agile Methods in Embedded Systems Development: A Systematic Review

With the rapid development of technology, the application area of embedded systems continues to broaden. This makes the development of embedded systems facing increased pressure of quality, cost and cycle time. On the other hand, agile methods have been more and more adopted in traditional software projects. Many studies indicated that agile methods have significant values to adapt to changing requirements, increasing the productivity and the quality of the final product. Therefore, the application of agile methods in embedded systems development naturally has drawn attentions from researchers and practitioners. This paper applies systematic reviews to systematically understand the application status of agile methods in embedded system development. Through reviewing and analyzing 49 literatures since Agile Manifesto has been announced, this study tries to answer the following three questions: 1) In different types of embedded systems development, what is the overall application status of agile methods; 2) What characteristics of embedded systems are suitable to apply agile methods; 3) How to improve current agile methods (practices) to better adapt to the development of embedded systems. The study shows that, although there are some differences in the degree of application, agile methods have been applied in the
Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation

Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net.
Multifunctional Coatings of Titanium Implants Toward Promoting Osseointegration and Preventing Infection: Recent Developments

Titanium and its alloys are dominant material for orthopedic/dental implants due to their stable chemical properties and good biocompatibility. However, aseptic loosening and peri-implant infection remain problems that may lead to implant removal eventually. The ideal orthopedic implant should possess both osteogenic and antibacterial properties and do proper assistance to in situ inflammatory cells for anti-microbe and tissue repair. Recent advances in surface modification have provided various strategies to procure the harmonious relationship between implant and its microenvironment. In this review, we provide an overview of the latest strategies to endow titanium implants with bio-function and anti-infection properties. We state the methods they use to preparing these efficient surfaces and offer further insight into the interaction between these devices and the local biological environment. Finally, we discuss the unmet needs and current challenges in the development of ideal materials for bone implantation.
Predictor of Obese Mothers and Stunted Children in the Same Roof: A Population-Based Study in the Urban Poor Setting Indonesia

Stunting leads to the poor cognitive development, increases the risk of child mortality, and elevates the risk of non-communicable diseases. This study aimed to determine the magnitude of double burden of malnutrition (DBM) in the urban poor setting in Indonesia and investigate its predictors. This was a cross-sectional study involving 436 mothers proportionally chosen from 16 integrated health posts in Surabaya, Indonesia. The households were categorized into the two groups based on the body mass index (BMI) of mother and the height-for-age z-score (HAZ) of child; households without DBM and household with DBM. Energy, carbohydrate, protein, and fat intake were obtained using 24-h food recall and socioeconomic status was measured using a structured questionnaire. Data on socioeconomic status were educational level of mother and occupation, household income, and food expenditure. The prevalence of household with DBM was 27.5%; 12.4% pair stunted children and normal weight mother; 45.6% pair of overweight/obese mother and normal height children. The logistic regression analysis showed significant differences in the education level and occupation of mother, protein intake of the children, and fat intake of the mother between households with and without DBM. This study offers an important insight to improve the knowledge of mother related to the protein intake of children to reduce stunting risk and fat intake of mother to prevent over-nutrition.