Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex Differences in Rotenone Sensitivity Reflect the Male-to-Female Ratio in Human Parkinson's Disease Incidence.

There is a critical need to include female subjects in disease research; however, in Parkinson's disease, where the male-to-female incidence is about 1.5-to-1, the majority of preclinical research is conducted in male animals. The mitochondrial complex I inhibitor, rotenone, is selectively toxic to dopaminergic neurons, and reproduces several neuropathological features of Parkinson's disease, including α-synuclein pathology. Rotenone has been primarily utilized in male Lewis rats; however, pilot studies in age-matched female Lewis rats revealed that our usual dose (2.8 mg/kg/day intraperitoneal [i.p.]) did not cause dopaminergic neurodegeneration. Therefore, we compared rotenone-treated males (2.8 mg/kg/day, i.p.) to females at increasing doses (2.8 mg/kg/day, 3.2 mg/kg/day, 3.6 mg/kg/day, and 1.6 mg/kg bis in die, i.p.). Female rats receiving 3.2 mg/kg, and 3.6 mg/kg rotenone displayed significant loss of dopaminergic neurons in the substantia nigra as assessed by stereology, which was accompanied by a loss of striatal dopaminergic terminals. Even at these higher doses, however, females showed less inflammation, and less accumulation of α-synuclein and transferrin, possibly as a result of preserved autophagy. Thus, the bias toward increased male incidence of human Parkinson's disease is reflected in the rotenone model. Whether such sex differences will translate into differences in responses to mechanism-driven therapeutic interventions remains to be determined.
Analytic Loss Minimization: Theoretical Framework of a Second Order Optimization Method

In power engineering, the Y b u s is a symmetric N × N square matrix describing a power system network with N buses. By partitioning, manipulating and using its symmetry properties, it is possible to derive the K G L and Y G G M matrices, which are useful to define a loss minimisation dispatch for generators. This article focuses on the case of constant-current loads and studies the theoretical framework of a second order optimization method for analytic loss minimization by taking into account the symmetry properties of Y b u s . We define an appropriate matrix functional of several variables with complex elements and aim to obtain the minimum values of generator voltages.
Peapods: OS-Independent Memory Confidentiality for Cryptographic Engines

Cryptography is widely adopted in computer systems to protect the confidentiality of sensitive information. The security relies on the assumption that cryptography keys are never leaked, which may be broken by the memory disclosure attacks, e.g., the Heartbleed and coldboot attacks. Various schemes are proposed to defend against memory disclosure attacks, e.g., performing the cryptographic computations in registers, or adopting the hardware features (e.g., Intel TSX and Intel SGX) to ensure that the plaintext of the cryptography key never appears in memory. However, these schemes are still not widely deployed due to the following limitations: (a) Most of the schemes are deployed in the OS kernel and require the root (or administrator) privileges of the host; and (b) They require the programmers to integrate these protection schemes in the implementation of different cryptography algorithms on different platforms. In this paper, we propose a tool implemented in Clang/LLVM, named Peapods, which provides the user-mode protection for cryptographic keys in software engines. It introduces one qualifier and three intrinsics for the programmers to specify the sensitive variables and code fragments to be protected, making it easier to be deployed. Peapods adopts transactional memory to protect cryptographic keys, while it is OS-independent and does not require the cryptographic computation performed in the OS kernel. Peapods supports the automatic protection between transactions for better performance. We have implemented the prototype of Peapods. Evaluation results demonstrate that Peapods achieves the design goals with a modest overhead (less than 10%).
Dynamic Decoupling Based Robust Synchronous Control for a Hydraulic Parallel Manipulator

In this paper, a novel dynamic decoupling-based robust synchronous control scheme is proposed for the high-precision motion trajectory tracking control of a hydraulic parallel manipulator. The presented controller is derived by effectively integrating joint disturbance rejection technique with a cross-coupling control (CCC) approach. The parallel manipulator is first considered as a multi-axis system with modeling uncertainties and strong nonlinear coupling dynamics. For each axis, two extended state observers are synthesized to estimate and reject both the matched uncertainties and unmatched uncertainties (e.g., external disturbances and unmodeled nonlinearities), and then the decoupled dynamic model of the system in joint space can be established. A synchronization error is developed to represent the coordination degree of multiple actuators and then coupled with the position error to form a coupling position error. Subsequently, based on the system decoupled dynamic model, a synchronous controller with the feedback of coupling position error is carried out to coordinate the motions of actuators by combining the CCC strategy with robust control via backstepping method. The proposed controller can theoretically guarantee the simultaneous convergence of both position tracking errors and synchronization errors. The stability of the system is verified via the Lyapunov method. The comparative experiment results are obtained to verify the high tracking performance nature of the proposed control strategy.
Book Reviews

This well-written and well-organised book comprises nine chapters. Its themes are established in the Introduction. They are: a critique of neoclassical methodology and pricing theory; the formulation and development of a realist approach to economics; the development of Post Keynesian economics; and the development of a cybernetic systems theory approach to economics, speci cally pricing. Chapter 2 outlines a research methodology described as ‘realist’. The subsequent chapters apply this methodology to pricing theory. Chapter 3 is a realist review of the literature on pricing and price—a distinction drawn out by the author. This allows him to survey a range of perspectives, focusing on Post Keynesian theory. Chapter 4 surveys existing econometric evidence. Together, Chapters 4 and 5 discuss the feasibility and role of econometrics in a realist research methodology. New econometric results are presented in Chapter 5. Chapters 6 to 8 review the theory and empirical evidence on pricing, drawing upon Downward’s preceding arguments, which advocate the use of a wide range of empirical methods (including econometrics). Chapter 8 presents new results from an extensive survey conducted by the author, and inferences on pricing are drawn from the survey results. The book has, therefore, essentially two foci: the identi cation and development of a realist research agenda, and the pricing behaviour of  rms. Downward makes a signi cant contribution to both topics. Realism, particularly the variant known as Critical Realism (CR), is a growing force in economics, as evidenced by a recent issue of the Journal of Post Keynesian Economics (Vol. 22, no. 1, 1999) that tied it closely to Post Keynesianism. Arguably, the book’s main contribution is to present a realist approach to methodology, synthesising several strands of literature: CR, associated most closely with Tony Lawson; the work of Uskali Mäki; the Babylonian approach of Sheila Dow; and Keynes’s methodological approach. The extensive treatment of Mäki is signi cant, particularly Downward’s combination of Mäki with Lawson. This is controversial given Lawson’s (1997) view of Mäki as an interpreter of realisms—and neoclassical realism in particular—rather than as advancing a position himself. The combination of Dow and Lawson is less controversial , as Dow (1999) would attest. CR is clearly central to Downward’s account, but his inclusion of the other approaches helps him to clarify and develop his methodology, particularly on questions such as the use of econometrics. The book outlines a realist ontology, based on a structured and layered reality combined with the concept of open systems. The open systems concept has taken on increasing signi cance recently, as some Post Keynesians argue that it should be a unifying tenet of Post Keynesian methodology. Downward
Particle Swarm Optimization Based Support Vector Machine (P-SVM) for the Segmentation and Classification of Plants

With the rapid growth in urbanization and population, it has become an earnest task to nurture and grow plants that are both important in sustaining the nature and the living beings needs. In addition, there is a need for preserving the plants having global importance both economically and environmentally. Locating such species from the forest or shrubs having human involvement is a time consuming and costly task to perform. Therefore, in this paper, a novel method is presented for the segmentation and classification of the seven different plants, named Guava, Jamun, Mango, Grapes, Apple, Tomato, and Arjun, based on their leaf images. In the first phase, both real-time images and images from the crowdAI database are collected and preprocessed for noise removal, resizing, and contrast enhancement. Then, in the second phase, different features are extracted based on color and texture. The third phase includes the segmentation of images using a k-means algorithm. The fourth phase consists of the training of support vector machine, and finally, in the last phase, the testing is performed. Particle swarm optimization algorithm is used for selecting the best possible value of the initialization parameter in both the segmentation and classification processes. The proposed work achieves higher experimental results, such as sensitivity = 0.9581, specificity = 0.9676, and accuracy = 0.9759, for segmentation and classification accuracy = 95.23 when compared with other methods.
Delay-Aware Secure Computation Offloading Mechanism in a Fog-Cloud Framework

Fog-Cloud framework is being regarded as a more promising technology to provide performance guarantee for IoT applications, which not only have higher requirements on computation resources, but also are delay and/or security sensitive. In this framework, a delay and security-sensitive computation task is usually divided into several sub-tasks, which could be offloaded to either fog or cloud computing servers, referred to as offloading destinations. Sub-tasks may exchange information during their processing and then have requirement on transmission bandwidth. Different destinations produce different completion delays of a sub-task, affecting the corresponding task delay. The existing offloading approaches either considered only a single type of offloading destinations or ignored delay and/or security constraint. This paper studies a computation offloading problem in the fog-cloud scenario where not only computation and security capabilities of offloading destinations may be different, but also bandwidth and delay of links may be different. We first propose a joint offloading approach by formulating the problem as a form of Mixed Integer Programming Multi-Commodity Flow to maximize the fog-cloud provider's revenue without sacrificing performance and security requirements of users. We also propose a greedy algorithm for the problem. Extensive simulation results under various network scales show that the proposed computation offloading mechanism achieves higher revenue than the conventional single-type computation offloading under delay and security constraints.
pyLLE: a Fast and User Friendly Lugiato-Lefever Equation Solver

The Lugiato-Lefever Equation (LLE), first developed to provide a description of spatial dissipative structures in optical systems, has recently made a significant impact in the integrated photonics community, where it has been adopted to help understand and predict Kerr-mediated nonlinear optical phenomena such as parametric frequency comb generation inside microresonators. The LLE is essentially an application of the nonlinear Schrodinger equation (NLSE) to a damped, driven Kerr nonlinear resonator, so that a periodic boundary condition is applied. Importantly, a slow-varying time envelope is stipulated, resulting in a mean-field solution in which the field does not vary within a round trip. This constraint, which differentiates the LLE from the more general Ikeda map, significantly simplifies calculations while still providing excellent physical representation for a wide variety of systems. In particular, simulations based on the LLE formalism have enabled modeling that quantitatively agrees with reported experimental results on microcomb generation (e.g., in terms of spectral bandwidth), and have also been central to theoretical studies that have provided better insight into novel nonlinear dynamics that can be supported by Kerr nonlinear microresonators. The great potential of microresonator frequency combs (microcombs) in a wide variety of applications suggests the need for efficient and widely accessible computational tools to more rapidly further their development. Although LLE simulations are commonly performed by research groups working in the field, to our knowledge no free software package for solving this equation in an easy and fast way is currently available. Here, we introduce pyLLE, an open-source LLE solver for microcomb modeling. It combines the user-friendliness of the Python programming language and the computational power of the Julia programming language.
Deep Learning Application in Power System with a Case Study on Solar Irradiation Forecasting

Power systems are developing day by day due to the inclusion of latest digital technologies. Due to the increasing complexities in power systems and collection of high volume of data, Deep Learning (DL) techniques are becoming most suitable technologies for its future development and success. Due to high performance computing with decreased computational cost, availability of huge amount of data, and better algorithms, DL has entered into its new developmental stage. This article introduces state of the art of application of Deep Learning in power systems, and presents a novel case study on the solar irradiance forecasting required for PV generation. The case study is prediction of hourly, daily and total solar irradiation forecasting for a year ahead using Long-Short Term Memory (LSTM). Year ahead data is important from the point of view of installation planning and market.
Free-Head Appearance-Based Eye Gaze Estimation on Mobile Devices

Eye gaze tracking plays an important role in human-computer interaction applications. In recent years, many research have been performed to explore gaze estimation methods to handle free-head movement, most of which focused on gaze direction estimation. Gaze point estimation on the screen is another important application. In this paper, we proposed a two-step training network, called GazeEstimator, to improve the estimation accuracy of gaze location on mobile devices. The first step is to train an eye landmarks localization network on 300W-LP dataset [1], and the second step is to train a gaze estimation network on GazeCapture dataset [2]. Some processing operations are performed between the two networks for data cleaning. The first network is able to localize eye precisely on the image, while the gaze estimation network use only eye images and eye grids as inputs, and it is robust to facial expressions and occlusion.Compared with state-of-the-art gaze estimation method, iTracker, our proposed deep network achieves higher accuracy and is able to estimate gaze location even in the condition that the full face cannot be detected.
A Compact, Low-Profile Dual-Band Patch Filtering Antenna for Off-Body Communications

A compact dual-band filtering antenna operating in the 5.0-GHz WiFi and 5.8-GHz ISM bands for wearable applications is investigated. The proposed filtering antenna is composed of a rectangular patch that etched with two pairs of rectangular slots and two notch ring slots. In addition, a set of curved parasitic patches with metallic pins are placed inside the two notch ring slots. Four rectangular slots and the curved parasitic patches generate three radiation nulls at the edges of lower and upper bands, and in the middle of the two working bands, leading to an enhanced suppression level in the stop-band. The proposed filtering antenna has the dimensions of 41.5 mm×33.9 mm×1.524 mm with a simulated in-band gain of about 6.5 dBi. Due to the merit radiation patterns with the front-to-back ratios of more than 15 dB, the proposed filtering antenna is suitable for the off-body communications.
Statistical arbitrage in the US equities market

We study model-driven statistical arbitrage in US equities. Trading signals are generated in two ways: using Principal Component Analysis (PCA) or regressing stock returns on sector Exchange Traded Funds (ETFs). In both cases, the idiosyncratic returns are modelled as mean-reverting processes, which leads naturally to ‘contrarian’ strategies. We construct, back-test and compare market-neutral PCA- and ETF-based strategies applied to the broad universe of US equities. After accounting for transaction costs, PCA-based strategies have an average annual Sharpe ratio of 1.44 over the period 1997 to 2007, with stronger performances prior to 2003. During 2003–2007, the average Sharpe ratio of PCA-based strategies was only 0.9. ETF-based strategies had a Sharpe ratio of 1.1 from 1997 to 2007, experiencing a similar degradation since 2002. We also propose signals that account for trading volume, observing significant improvement in performance in the case of ETF-based signals. ETF-strategies with volume information achieved a Sharpe ratio of 1.51 from 2003 to 2007. The paper also relates the performance of mean-reversion statistical arbitrage strategies with the stock market cycle. In particular, we study in detail the performance of the strategies during the liquidity crisis of the summer of 2007, following Khandani and Lo [Social Science Research Network (SSRN) working paper, 2007].
Analysis on Beam Quality of Solid-State Tube MOPA System With Zigzag Beam Path

All-solid-state tube lasers (ASSTLs) with zigzag beam path combine the attractive properties of rod, slab, and disk lasers. However, the analysis on the beam quality of tube lasers is rarely reported. In order to explore how and how seriously the static errors including fabricating and alignment errors degrade the beam quality of a tube laser, a theoretical model of a nonideal ASSTLs constructed in master oscillator power amplifier (MOPA) configuration has been built up by taking the static errors into consideration. On the basis, the influence of the fabricating and alignment errors on the static beam quality of the ASSTLs was analyzed in detail, and the annular aberration of the output beam was discussed as well. Simulation results indicate that the static beam quality of tube lasers strongly depends on the fabricating and alignment errors. These static errors would severely degrade the output beam quality of the ASSTLs and even result in coma and defocus aberrations. This work may provide valuable references for the design, processing, and applications of ASSTLs.
Design of A Dual-polarization Full-Corporate-Fed Waveguide Slot 4x4 Subarray for Ka-band

A 4x4-element waveguide slot antenna sub-array for Ka-band is proposed in this paper to achieve high-efficiency and high-gain, wideband characteristics of antenna in the millimeter-wave band. This full-corporate-fed slot antenna can avoid long-line effect-which is a limit to the bandwidth of the antenna. It is also an effective way to improve the uniformity of two kinds of polarizations. The simulated bandwidth for VSWR lower than 1.5 is 9% (horizontal port) and 5.7% (vertical port) respectively. The isolation is more than 50dB over the bandwidth, and 19.2dBi is obtained at the design frequency. The array has an 18.8dBi gain and 6.6%, 5.1% bandwidth in measurement. The 4x4-element can b e used as an important unit to compose the whole array to realize high gain.
ASoVS: Abstractive Summarization of Video Sequences

In today’s age, a massive amount of videos are produced every day, which contains audio, visual, and textual data. This constant increase is due to the ease of recording service in portable devices, such as mobile phones, tablets, or cameras. The major challenge is to understand the visual semantics and convert it into a condensed format, such as caption or summary to save storage space, enables users to index, navigate, and help gain information in less time. We propose an innovative joint end-to-end solution, Abstractive Summarization of Video Sequences, which uses the deep neural network to generate the natural language description and abstractive text summarization of an input video. This provides a text-based video description and abstractive summary, enabling users to discriminate between relevant and irrelevant information according to their needs. Furthermore, our experiments show that the joint model can attain better results than the baseline methods in separate tasks with informative, concise, and readable multi-line video description and summary in a human evaluation.
A DC-Reactor-Based Solid-State Fault Current Limiter for HVdc Applications

Expansion of high-voltage dc (HVdc) systems to multi-terminal HVdc (MT-HVdc) systems/grids considerably increases the short-circuit levels. In order to protect the emerging MT-HVdc systems/grids against fault currents, proper dc fault current limiters (FCLs) must be developed. This paper proposes an innovative high inductance solid-state dc-reactor-based FCL (HISS-DCRFCL) to be used in HVdc applications. In fact, during the HISS-DCRFCL normal operation, its inductance value is extremely low, and its value becomes considerably high during the fault period, which decreases the fault current amplitude. The proposed HISS-DCRFCL performance is analyzed by MATLAB/Simulink and the simulation results are verified and confirmed by laboratory experimental results using a scaled-down laboratory prototype setup.
Mission-Based Energy Consumption Prediction of Multirotor UAV

Unmanned aerial vehicle (UAV) is lately one of many popular research topics. High variety of its usage makes it attentive to be studied on its construction or the control. However, without knowing the energy that will be consumed in each mission, the available flight duration will be unknown and the usages of this vehicle will be limited. A mission-based black box modeling of UAV’s energy consumption prediction was proposed in this paper. The setup consists of ArduPilot with Mission Planner Firmware installed to a custom built hexarotor. The method consists of three consecutive steps: data collection, data preprocessing, and regression. To collect the required data, flight patterns that contained several types of movements were defined, where the flight data log that contained missions, GPS, and battery, was collected. The preprocessing included the movement separation and also included the acceleration and the deceleration of horizontal movement. Finally, the regression was done using the Elastic Net Regression from Sklearn. The model was then tested on two flight patterns to simulate a surveillance application of a UAV and could predict with 98.773% mean of energy accuracy of the missions that started from the takeoff and ended with the return to launch command.