Miracle-dz/UniFAR-FTUs huggingface dataset:
Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants

Abstract Motivation Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown. Results We identify and systematically evaluate determinants of performance—including network properties, experimental design choices and data processing—by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets. Availability and implementation Code is available at http://github.com/bagherilab/networkinference/. Supplementary information Supplementary data are available at Bioinformatics online.
Design & Analysis of Grid Connected Permanent Magnet Synchronous Generator Using Fuzzy Logic Controller

A comprehensive control of a wind turbine system connected to an industrial plant is discussed in this paper where an algorithm has been developed allowing a control structure that utilizes a four-leg inverter connected to the grid side, to inject the available energy, as well as to work as an active power filter, mitigating load current disturbances and enhancing power quality. A four-wire system is considered with three-phase and single-phase linear and nonlinear loads. During the connection of the wind turbine, the utility side controller is designed to compensate the disturbances caused in presence of reactive, non-linear and/or unbalanced single- and intra-phase loads, in addition to providing active and reactive power as required. When there is no wind power available, the controller is intended to improve the power quality using the DC-link capacitor with the power converter attached to the grid. The main difference of the proposed methodology with respect to others in the literature is that the proposed control structure is based on the Conservative Power Theory decompositions. This choice provides decoupled power and current references for the inverter control, offering very flexible, selective and powerful functionalities. Real time software benchmarking has been conducted in order to evaluate the performance of the proposed control algorithm for full real-time implementation. The control methodology is implemented and validated in hardware-in-the-loop (HIL) based on Opal-RT and a TI DSP. The results corroborated our power quality enhancement control, and allowed to exclude passive filters, contributing to a more compact, flexible and reliable electronic implementation of a smart-grid based control.
Identification of TRPV4 as a Regulator of Adipose Oxidative Metabolism, Inflammation and Energy Homeostasis by a Chemical Biology Approach

PGC1α is a key transcriptional coregulator of mitochondrial biogenesis, oxidative metabolism and thermogenesis. We developed a quantitative high throughput screen to identify small molecules that can induce PGC1α expression in adipocytes. Small molecules antagonizing the TRPVs (Transient Receptor Potential Vanilloid), a family of ion channels, induced PGC1α expression in adipocytes. In particular, inhibition of TRPV4 increased expression of PGC1α, UCP1 and cellular respiration; conversely, chemical activation of TRPV4 repressed this pathway. Blocking TRPV4 in cultured adipocytes also reduced the expression of multiple proinflammatory genes that are involved in the development of insulin resistance. These effects of TRPV4 were mediated by the activation of ERK1/2. Finally, mice with a null mutation for TRPV4 showed higher energy expenditure with no change in movement or food intake, and were protected from diet-induced obesity, adipose inflammation and insulin resistance. This study links TRPV4 to robust pathways that offer therapeutic potential in obesity and related metabolic diseases.
Managing Constraints in Role Based Access Control

Role-based access control (RBAC) is the most popular access control model currently adopted in several contexts to define security management. Constraints play a crucial role since they can drive the selection of the best representation of the organization’s security policies when migrating towards an RBAC system. In this paper, we examine different types of constraints addressing both theoretical aspects and practical considerations. On one side, we define the constrained role mining problem for each constraint type, showing its complexity. On the other hand, we present efficient heuristics adapted to each class of constraints, all derived from the specialization of a general approach for role mining. We show that our techniques improve over previous proposals, offering a complete set of experimentations obtained after the application of the heuristics to standard real-world datasets.
Sub-THz and THz Signal Generation Using Photonic and Electronic Techniques (Invited Paper)

To enable the use of the sub-THz and THz region of the electromagnetic spectrum from 100GHz to 10THz for next generation high-speed wireless communication and other emerging applications, it is important to generate the signal in this frequency range with high output power and high efficiency in a compact and cost-effective way. This paper reviews different photonic and electronic techniques for sub-THz and THz signal generation with implementation examples and discusses the future trends.
Stator Winding Temperature Estimation of IPMSM Based on a High-frequency Voltage Signal Injection

An Interior Permanent Magnet Synchronous Machine, IPMSM, has been widely adopted as a traction motor of electric/hybrid vehicles because of high torque density and wide operating speed region. In the operation of IPMSM, a winding temperature should be monitored to maximize the operating area of IPMSM while avoiding winding insulation breakdown. This paper proposes a stator winding temperature estimation method of IPMSM by using high-frequency (HF) resistance at low operation speed. A relation between HF resistance and the stator winding temperature is analyzed and verified in an experimental setup where the stator winding temperatures and the magnet temperatures can be measured. Based on the relation, the stator temperature estimation method has been devised in this paper. Experimental results, including online estimation, show that the proposed method can be utilized to estimate the stator winding temperature.
Approximate Triple Modular Redundancy: A Survey

In recent years, approximate computing (AC) has attracted attention owing to its tradeoff between the exactness of computations and performance gains. AC has also been probed for the technique of Triple modular redundancy (TMR). TMR is a well-known fault masking methodology, with associated overheads, widely used in systems of different nature and at different levels. E.g.: layout-level, gate-level, HW-module level, software. At hardware level, through exploitation of AC the 200% area overhead problem due to triplication of the original modules in TMR can be reduced. By approximating the modules of TMR while ensuring that at least two of the approximate modules do not differ from the original module for every input vector, the facilitation of fault masking can lead to overhead reduction. Hence, approximate TMR (ATMR) aims to achieve cost-effective reliability. Nevertheless, due to the extensive search space, computational complexity, and principal fault masking function of ATMR, designing an ATMR is a challenging task. An ATMR technique must be scalable so that it can be easily adopted by circuits having large number of inputs and the extraction of ATMR modules remains computationally inexpensive. Compared with TMR, due to the inclusion of approximations, ATMR is more vulnerable to errors, and hence, the design technique must ensure awareness of input-criticality. To the best of the authors’ knowledge, none of the existing survey articles on AC has reported on ATMR. Therefore, in this work, ATMR design techniques are thoroughly surveyed and qualitatively compared. Moreover, design considerations and challenges for designing ATMR are discussed.
Residential Load Forecasting Using Recurrent Neural Networks

In Electrical systems, load forecasting is very important as it has implications on flexibility, smooth operation, and economical aspects as well. The residential load depends on household size, weather season, numbers of load, number of occupants and their behavior, types of devices, etc. Thus, making its accurate forecasting a very difficult job. In this research, machine learning and deep learning-based Recurrent Neural Networks (RNN) algorithms are used for the day-ahead load forecasting of an Estonian household. A data set based on measured load values of an Estonian household is used in the development of this forecasting model. The simulation results indicate that the RNN based algorithm gives better forecasting based on lower Root Mean Square Error (RMSE) value.
Constant Modulus Discrete Phase Radar Waveforms Design Subject to Multi-Spectral Constraints

This paper deals with constant modulus waveform design in spectrally dense environments assuming a discrete phase code alphabet. The goal is to optimize the radar detection performance while rigorously controlling the injected interference energy within each shared band and enforcing a similarity constraint to manage some relevant signal features. To tackle the resulting NP-hard optimization problem, an iterative procedure characterized by a polynomial computational complexity, is introduced leveraging the coordinate descent method. Numerical results are provided to show the effectiveness of the technique.
Collision Analysis to Motor Dashcam Videos With YOLO and Mask R-CNN for Auto Insurance

In traditional insurance policies, the insured drivers have to go through a complicated procedure after an accident then wait a long time for human appraisers’ evaluation of insurance claims. In this scenario, the insured drivers can’t receive immediate financial assistance, while the financial agencies put much effort into evaluations. To address these problems, we propose a novel approach to evaluating insurance claims of accidents automatically by artificial intelligence (AI). This paper applied noted deep learning models of YOLO and Mask R-CNN to detect collisions in motor dashcam videos. This paper incorporates both of their strengths into our system. YOLO is applied to quickly detect if an accident happened. Mask R-CNN is applied to examine if a collision happened and which object was hit by the driver’s motorcycle. In this paper, collision analysis was conduct with motor dashcam videos. The analysis results show our approach can effectively detect different types of collisions for further auto insurance.
Information Support for the Task of Estimation the Quality of Functioning of the Electricity Distribution Power Grids with Renewable Energy Source

The problem of information support of the task of estimating the quality of functioning of the distribution power grids (DPG) with renewable energy sources (RES) is considered. The analysis of information support of this task is carried out. In this article is presented a two-levels monitoring and control system architecture, within the SmartGrid concept. This system implements active end-consumers behavior, optimum integration of distributed generation (DG), power storage systems of various types and capacities into electricity grids. Proposed two-levels monitoring and control system architecture will allow increasing the reliability and quality of electricity.
Towards a Believable Decoy System: Replaying Network Activities from Real System

Recently cyber deception has emerged as a promising defense approach for detecting and defeating advanced persistent threat. By leveraging deceptive decoys, the defenders seek to proactively engage with the attackers and entice them away from the protected server infrastructure. The effectiveness of such decoy-based deception largely relies on the decoy fidelity. In this paper, we observe that realistic server system inevitably experiences wearoff from service request processing and regular maintenance, resulting in characteristic access pattern, running states, and system artifacts. Accordingly, we identify two deception evasion attacks, namely, traffic fingerprinting and system fingerprinting, which enable sophisticated adversaries to accurately distinguish decoys from real servers. To protect web server decoys against those evasion attacks, we develop Mirage, a seamless real-time network traffic replay framework to generate network traffic and system artifacts on the decoy server based on the normal clients’ interactions with the real server. Mirage works as a TLS-capable reverse proxy that transparently replays real traffic towards decoys. To resolve the inconsistent states between the real and decoy servers, we integrate a decoy client emulator into the reverse proxy to maintain the stateful data features and caching logic of a decoy session. Moreover, we employ format preserving encryption technique to obfuscate sensitive data before being sent to the decoy server. Implementations and evaluations of a prototype demonstrate that Mirage can effectively mitigate deception evasion attacks with acceptable performance overhead.
Patterns and Mechanisms for Age-dependent Reproduction and Survival in Birds

SYNOPSIS. The majority of bird species exhibit age-dependent survival and reproduction. In almost all cases, first time breeders or young individuals perform at a lower and slower rate than older individuals. This review highlights the importance of age-dependent effects and urges further study of the proximate and ultimate mechanisms involved. Age effects show substantive variation across life history stage, breeding season, year, cohort, habitat types and environmental conditions both within and across taxons. In some populations or years, age effects disappear. Despite the variable patterns, age effects show amazing persistence in a variety of extreme ecological and environmental conditions. Experimental manipulations of food, predation pressure and breeding experience do not usually remove age effects. Age-dependent effects may be maintained or accumulate through the breeding season and profoundly influence fitness, or they may be washed out or reversed by events later in the breeding season. Recently, we have amassed substantive information about the patterns of variation in age-dependent reproduction and survival among individuals, but we have made little progress toward understanding the proximate mechanisms responsible for this variation. Proximate and ultimate processes are inextricably linked, and thus the study of age-dependence is highly relevant to the further development of life history theory. Studies of age-dependent performance have general relevance to population management and conservation issues as age sub-structuring may contribute substantially to annual or inter-populational variation in reproductive success.
KIN SELECTION AND SOCIAL INSECTS

Social insects so dominate many terrestrial habitats (Wilson 1990) that they can hardly escape the attention of biologists, but even if they were rare, they would still attract special interest because of the intricate cooperation within their societies. William Morton Wheeler (1911) described the social insect colony as an organism (or as a higher-level organism or superorganism) because of the degree to which individuals appear to operate as a unit that is dedicated to the perpetuation and reproduction of the colony as a whole. The reinvention of the organism at a higher level has occurred at a number of crucial junctures in the history of life (Maynard Smith and Szathmary 1995). For example, the eukaryotic cell arose from several prokaryotic ancestors (Margulis 1970), and multicellular plants, animals, and fungi arose from single-celled ancestors (Buss 1987). Because insect societies are macroscopic, and because they span the entire range from solitary individuals to essentially superorganismal colonies, they offer an accessible model for how such transitions can happen.
Maximal α-Leakage and its Properties

Maximal α-leakage is a tunable measure of information leakage based on the quality of an adversary’s belief about an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the quality of a belief, ranging from log-loss at $\alpha=1$ to the probability of error at $\alpha=\infty$. We review its definition and main properties, including extensions to $\alpha\lt 1$, robustness to side information, and relationship to Rènyi differential privacy.
Prioritizing power factor in power density assessments of doubly fed induction generator alternatives

Doubly fed induction generators (DFIGs) are one of the most common wind turbine generators. In this paper, it is shown that evaluating DFIG alternatives like rotor-tied DFIGs and brushless doubly fed machines (BDFMs) without proper power factor considerations, can lead to inaccurate power density estimations. Finite element analysis (FEA) models of selected DFIG alternatives designed in available literature sources, are simulated as grid connected machines. The significant variations of power output with grid side power factor of these machine are highlighted. This denotes the need for more nuanced evaluation of the power density of these machines, especially when considering grid code requirements. It is therefore proposed that the power densities of DFIG alternatives be obtained at conditions within permissible grid code conditions.
Fixed-Frequency Zero Voltage Switching CurrentFed (L) (LC) Resonant DC-DC Converter

Current-fed load resonant converters offer soft-switching against wide variations in load and input fluctuations unlike quasi resonant and impulse resonant converter. However, their modulation characteristics and efficiency are highly influenced by employed load dependent resonant tank. Therefore, parallel LC resonant tank-based converters exhibit compromised part load efficiency due to variable switching frequency operation and load independent circulating currents. On the other hand, series resonant tank-based converters such as current-fed LLCSRC resonant converter operates with constant switching frequency. However, their parallel inductor pulls down part load efficiency due to unnecessary conduction loses. Therefore, this paper proposes simple modification in LLC-SRC resonant tank, leads to proposed current-fed (L)(LC) resonant converter, to eliminate additional conduction loses in converter. Proposed converter inherits all merits of LLC-SRC converter with improved efficiency. This paper presents analysis, design and verify proposed theory with simulations.
A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations

Vulnerable nature of price forecasts, such as an unpredictability of future and numbers of socio-economic factors that affect market stability, often makes investment risky. Earlier studies in Finance suggested that constructing a portfolio can promise risk-spread gains. While Fund Standardization improved the traditional theories by reducing the computational complexity and by associating every interaction in the portfolio, such a method still cannot become a winning strategy because it does not measure the current value or the relative price of each asset. Inspired by the works of finding returns per risk, we attempt to design an optimal portfolio by searching products that have potential to grow further. More specifically, we first analyze risk-adjusted returns in the previous periods and use their inertia as a momentum. However, because historic movements alone do not fully elucidate future changes nor guarantee positive returns, we scored the relative values of each stock to make more informed estimations. Using the Capital Asset Pricing Model, we measured the values of each stock and determined those undervalued. In this study, we applied a Genetic Algorithm to optimize portfolios while incorporating the momentum strategy and the asset valuations. The proposed GA model was tested in two separate markets, S&P500 and KOSPI200, and projected greater profits than that from both the previous method with momentum method and the market indexes. From the experimental results, the proposed CAPM+ method was found to be very effective in financial data analysis and to lay a groundwork for a sustainable investment execution.
Power Flow Control using Modular Multilevel Converter for 3-phase AC/AC Conversion

Modular multilevel converter (MMC) has been demandable to the researcher for its scalability and high voltage capability with low harmonic content. Having a completely modular design with cascaded full-bridge cells, MMC can reduce semiconductor losses. The transformer-less MMC is to realize lightweight and downsizing with the attractive performance for high-voltage, high-power conversion and also more helpful for the reduction of global warming. At the time of operation, it is very important to control input and output current, output voltage, circulating current and capacitor voltage. Average control and voltage balancing of the capacitor is important for improving the stability and controllability. The control scheme is explained for grid-connected three-phase MMC. The effectiveness is verified by the proposed control method in some typical conditions.
Vehicle localization using laser scanner

The laser scanner gives the data which makes possible to localize moving vehicles in the environment. Concept of algorithms and its tests by simulation and using real time data are are main subjects of the paper. Although there are many algorithms in this application huge amount of data and iterative operation requires high computing power. The presentation is concerned on searching a methods to decrease number of iterations necessary to find vehicle position and direction of its body in the reference to surroundings. The scan to scan analysis results in estimation or rotation velocity and angle. This method is proposed as initialization stage of ICP or feature points method. The reduction of the computation time is proved by experiments.
A Simple and Effective Compensation Method for Inverter Nonlinearity

In this paper, a simple and effective compensation method is proposed for inverter nonlinearity. First, a new measuring method of inverter voltage drop using two phases is proposed to obtain the curve of correlation between phase current and inverter voltage drop. Since the curve is obtained through experimental measurement under different currents, the effect of junction temperature on inverter voltage drop is also considered in this curve. Then, a look-up table based compensation method is proposed to eliminate the adverse impact of inverter nonlinearity on motor control. The compensation accuracy can be greatly improved by utilizing the current references and angle offset in the compensation method. Experimental verification is presented to prove the validity of the proposed method.