Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of SNCA rs894278 on Resting-State Brain Activity in Parkinson’s Disease

The pathogenesis of Parkinson’s disease (PD) is not well established. The rs894278 polymorphism of SNCA has been associated with PD. We performed this study to investigate the relationship between rs894278 and PD status on resting-state brain activity, by analyzing the amplitude of low-frequency fluctuation (ALFF). A total of 81 PD patients and 64 healthy controls were recruited. Disease severity and PD stage were evaluated in PD patients using the unified Parkinson’s disease rating scale (UPDRS) and the Hoehn and Yahr (HY) scale, while the cognitive function of all participants was assessed using the mini-mental state examination (MMSE). All participants were genotyped for the rs894278 SNP and underwent a resting state functional magnetic resonance imaging scan. We found that the ALFF values of PD patients in the lingual gyrus and left caudate were lower than those of HCs; and the ALFF values for the right fusiform of participants with G allele were lower than those of participants without G allele. And we further revealed higher ALFF values in bilateral fusiform in rs894278-G carriers than in rs894278-G non-carriers in the PD group and lower ALFF values in bilateral fusiform in rs894278-G carriers than in rs894278-G non-carriers in the HC group. Our findings show that rs894278 and PD status interactively affect the brain activity of PD patients and HCs, and changes in the brain connectomes may play a key role in the pathogenesis of PD. Thus, our work sheds light on the mechanism underlying PD pathogenesis.
Optimal Adversarial Attack on Autoregressive Models

We investigate optimal adversarial attacks against time series forecast made by autoregressive models. In our setting, the environment evolves according to a potentially nonlinear dynamical system. A linear autoregressive model observes the current environment state and predicts its future values. But an adversary can modify the environment state and hence indirectly manipulate the autoregressive model forecasts. The adversary wants to drive the forecasts towards some adversarial targets while minimizing environment modification. We pose this attack problem as optimal control. When the environment dynamics is linear, we provide a closed-form solution to the optimal attack using Linear Quadratic Regulator (LQR). Otherwise, we propose an approximate attack based on Model Predictive Control (MPC) and iterative LQR (iLQR). Our paper thus connects adversarial learning with control theory. We demonstrate the advantage of our methods empirically.
Nonconvex Rectangular Matrix Completion via Gradient Descent Without ℓ₂,∞ Regularization

The analysis of nonconvex matrix completion has recently attracted much attention in the community of machine learning thanks to its computational convenience. Existing analysis on this problem, however, usually relies on <inline-formula> <tex-math notation="LaTeX">$\ell _{2,\infty }$ </tex-math></inline-formula> projection or regularization that involves unknown model parameters, although they are observed to be unnecessary in numerical simulations. In this paper, we extend the analysis of the vanilla gradient descent for positive semidefinite matrix completion in the literature to the rectangular case, and more significantly, improve the required sampling rate from <inline-formula> <tex-math notation="LaTeX">$O(\mathrm {poly}(\kappa )\mu ^{3}~r^{3} \log ^{3}~n/n )$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(\mu ^{2}~r^{2} \kappa ^{14} \log n/n )$ </tex-math></inline-formula>. Our technical ideas and contributions are potentially useful in improving the leave-one-out analysis in other related problems.
Power Control for Sum Spectral Efficiency Optimization in MIMO-NOMA Systems With Linear Beamforming

This paper considers a multiple-input multiple-output non-orthogonal multiple access (NOMA) downlink transmission system with different linear beamforming techniques, where the base station uses each cluster to serve a pair of users. In the considered NOMA cluster, we first derive the performance analysis of the system that uses a proposed user paring method, which exploits the different large-scale channel qualities of users to allocate the transmit power of the strong and weak users in each pair, to ensure that both users in each pair can contribute the best on the system performance. We further formulate a sum spectral efficiency (SE) maximization with a subject to the limited transmit power budget, which is emphasized to be non-convex. We have, then, proposed a framework that solves the above non-convex problem into two steps: lower bound this non-convex problem by a geometric program by using the arithmetic mean-geometric mean inequality and, then, employ the successive optimization approach to find the local Karush–Kuhn–Tucker point. Numerical results manifest that a NOMA-based network with zero forcing (ZF) beamforming gives the highest sum SE, while regularized ZF brings benefits to the SE of weak users.
Module Placement under Completion-Time Uncertainty in Micro-Electrode-Dot-Array Digital Microfluidic Biochips

Digital microfluidic biochips (DMFBs) are an emerging technology that are replacing traditional laboratory procedures. With the integrated functions which are necessary for biochemical experiments, DMFBs are able to achieve automatic experiments. Recently, DMFBs based on a new architecture called micro-electrode-dot-array (MEDA) have been demonstrated. Compared with conventional DMFBs which sensors are specifically located, each microelectrode is integrated with a sensor on MEDA-based biochips. Benefiting from the advantage of MEDA-based biochips, real-time reaction-outcome detection is attainable. However, to the best of our knowledge, synthesis algorithms proposed in the literature for MEDA-based biochips do not fully utilize the real-time detection since completion-time uncertainties have not yet been considered. During the execution of a biochemical experiment, operations may finish earlier or delay due to variability and randomness in biochemical reactions. Such uncertainties also have effects when allocating modules for each fluidic operation and placing them on a biochip since a biochip with a fixed size area restricts the number and the size of these modules. Thus, in this paper, we proposed the first operation-variation-aware placement algorithm that fully utilizes the real-time detection since completion-time uncertainties have been considered. Simulation results demonstrate that with the proposed approach, it leads to reduced time-to-result and minimizes the chip size while not exceeding completion time compared to the benchmarks.
Task-Dependent Differences in Operant Behaviors of Rats With Acute Exposure to High Ambient Temperature: A Potential Role of Hippocampal Dopamine Reuptake Transporters

Behavioral or cognitive functions are known to be influenced by thermal stress from the change in ambient temperature (Ta). However, little is known about how increased Ta (i.e., when the weather becomes warm or hot) may affect operant conditioned behavior and the neural substrates involved. The present study thus investigated the effects of high Ta on operant behaviors maintained on a fixed-ratio 1 (FR1) and a differential reinforcement for low-rate responding 10 s (DRL 10-s) schedule of reinforcement. The rats were randomly assigned to three groups receiving acute exposure to Ta of 23°C, 28°C, and 35°C, respectively, for evaluating the effects of high Ta exposure on four behavioral tests. Behavioral responses in an elevated T-maze and locomotor activity were not affected by Ta treatment. Regarding operant tests, while the total responses of FR1 behavior were decreased only under 35°C when compared with the control group of 23°C, those of DRL 10-s behavior were significantly reduced in both groups of 28°C and 35°C. Distinct patterns of inter-response time (IRT) distribution of DRL behavior appeared among the three groups; between-group differences of behavioral changes produced by high Ta exposure were confirmed by quantitative analyses of IRT data. Western blot assays of dopamine (DA) D1 and D2 receptor, DA transporter (DAT) and brain-derived neurotrophic factor (BDNF) were conducted for the sample tissues collected in six brain areas from all the subjects after acute high Ta exposure. Significant Ta-related effects were only revealed in the dorsal hippocampus (dHIP). In which, the DAT levels were increased in a Ta-dependent fashion that was associated with operant behavior changes under high Ta exposure. And, there as an increased level of D1 receptors in the 28°C group. In summary, these data indicate that the performance of operant behavior affected by the present high Ta exposure is task-dependent, and these changes of operant behaviors cannot be attributed to gross motor function or anxiety being affected. The regulation of dHIP DAT may be involved in this operant behavioral change under high Ta exposure.
Intelligent EMG Pattern Recognition Control Method for Upper-Limb Multifunctional Prostheses: Advances, Current Challenges, and Future Prospects

Upper-limb amputation imposes significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. The use of intelligent learning algorithm for electromyogram-pattern recognition (EMG-PR)-based control in upper-limb prostheses is considered as an important clinical option. Though the existing EMG-PR prostheses could discriminate multiple degrees of freedom (DOF) limb movements, their transition to clinically viable option is still being challenged by some confounding factors. Toward realizing a clinically viable multiple DOF prostheses, this paper first explored the principles and dynamics of the existing intelligently driven EMG-PR-based prostheses control scheme. Then, investigations on core issues including variation in muscle contraction force, electrode shift, and subject mobility affecting the existing EMG-PR prosthetic control scheme were reported. For instance, variation in muscle contraction force and subject mobility led to degradation in the performance of the EMG-PR controlled prostheses with approximately 17.00% and 8.98% error values, respectively, which are still challenging issues among others. Thus, this paper reports core issues and best practices with respect to intelligent EMG-PR controlled prosthesis, the major challenges in implementing adaptively robust control scheme and provides future research directions that may result in the clinical realization of intuitively dexterous multiple DOF EMG-PR-based prostheses in the near future.
Presence and Cybersickness in Virtual Reality Are Negatively Related: A Review

In order to take advantage of the potential offered by the medium of virtual reality (VR), it will be essential to develop an understanding of how to maximize the desirable experience of “presence” in a virtual space (“being there”), and how to minimize the undesirable feeling of “cybersickness” (a constellation of discomfort symptoms experienced in VR). Although there have been frequent reports of a possible link between the observer’s sense of presence and the experience of bodily discomfort in VR, the amount of literature that discusses the nature of the relationship is limited. Recent research has underlined the possibility that these variables have shared causes, and that both factors may be manipulated with a single approach. This review paper summarizes the concepts of presence and cybersickness and highlights the strengths and gaps in our understanding about their relationship. We review studies that have measured the association between presence and cybersickness, and conclude that the balance of evidence favors a negative relationship between the two factors which is driven principally by sensory integration processes. We also discuss how system immersiveness might play a role in modulating both presence and cybersickness. However, we identify a serious absence of high-powered studies that aim to reveal the nature of this relationship. Based on this evidence we propose recommendations for future studies investigating presence, cybersickness, and other related factors.
Effectiveness Evaluation Model of Moving Target Defense Based on System Attack Surface

Evaluation of moving target defense (MTD) effectiveness has become one of the fundamental problems in current studies. In this paper, an evaluation model of MTD effectiveness based on system attack surface (SAS) is proposed to extend this model covering enterprise-class topology and multi-layered moving target (MT) techniques. The model is focused on the problem of incorrect performance assessment caused by inaccurately characterizing the process of attacking and defending. Existing evaluation models often fail to describe MTD dynamically in a process. To deal with this static view, offensive and defensive process based on a player’s move is presented. Besides, it converts all the attack and defense actions into the process, and interactivities are evaluated by system view extended attack surface model. Previously, the proposed attack surface models are not concerned about the links between nodes and vulnerabilities affected by topologies. After comprehensively analyzing the impact of interactions in the system, a SAS model is proposed to demonstrate how resources of the system are affected by the actions of attackers and defenders, thus ensuring the correctness of parameters for SAS in measuring MT technology. Moreover, by generating a sequence of those shifting parameters, a nonhomogeneous hierarchical hidden Markov model is used to find the possible sequence of attacking states by introducing the partial Viterbi algorithm. Also, a sequence of attacking states is defined to illustrate how adversaries are handled by MT technologies and how much additional consumption costs are increased by the system resource reconfiguration. Finally, the simulation of the proposed approach is given in a case study to demonstrate the feasibility and validity of the proposed effectiveness evaluation model in a systematic and dynamic view.
Understanding the Cross-Talk of Redox Metabolism and Fe-S Cluster Biogenesis in Leishmania Through Systems Biology Approach

Leishmania parasites possess an exceptional oxidant and chemical defense mechanism, involving a very unique small molecular weight thiol, trypanothione (T[SH]2), that helps the parasite to manage its survival inside the host macrophage. The reduced state of T[SH]2 is maintained by NADPH-dependent trypanothione reductase (TryR) by recycling trypanothione disulfide (TS2). Along with its most important role as central reductant, T[SH]2 have also been assumed to regulate the activation of iron-sulfur cluster proteins (Fe/S). Fe/S clusters are versatile cofactors of various proteins and execute a much broader range of essential biological processes viz., TCA cycle, redox homeostasis, etc. Although, several Fe/S cluster proteins and their roles have been identified in Leishmania, some of the components of how T[SH]2 is involved in the regulation of Fe/S proteins remains to be explored. In pursuit of this aim, a systems biology approach was undertaken to get an insight into the overall picture to unravel how T[SH]2 synthesis and reduction is linked with the regulation of Fe/S cluster proteins and controls the redox homeostasis at a larger scale. In the current study, we constructed an in silico kinetic model of T[SH]2 metabolism. T[SH]2 reduction reaction was introduced with a perturbation in the form of its inhibition to predict the overall behavior of the model. The main control of reaction fluxes were exerted by TryR reaction rate that affected almost all the important reactions in the model. It was observed that the model was more sensitive to the perturbation introduced in TryR reaction, 5 to 6-fold. Furthermore, due to inhibition, the T[SH]2 synthesis rate was observed to be gradually decreased by 8 to 14-fold. This has also caused an elevated level of free radicals which apparently affected the activation of Fe/S cluster proteins. The present kinetic model has demonstrated the importance of T[SH]2 in leishmanial cellular redox metabolism. Hence, we suggest that, by designing highly potent and specific inhibitors of TryR enzyme, inhibition of T[SH]2 reduction and overall inhibition of most of the downstream pathways including Fe/S protein activation reactions, can be accomplished.
Robust Model Predictive Control for a Class of Discrete-Time Markovian Jump Linear Systems With Operation Mode Disordering

For a class of discrete-time Markovian jump linear systems subject to operation mode disordering, a robust model predictive control method can be proposed to solve this issue. A bijective mapping scheme between the original random process and a new random process is studied to cope with the problem of operation mode disordering. At each sampling time, the original “min–max” optimization problem is transformed into a convex optimization problem with linear matrix inequalities so that the complexity of solving the optimization problem can be greatly reduced. The sufficient stability condition of the Markovian jump linear systems can be achieved by using the Lyapunov stability theory. Moreover, a state feedback control law is obtained, which minimizes an infinite prediction horizon performance cost. Furthermore, the cases of uncertain and unknown transition probabilities are also considered in this paper. The simulation results show that the proposed method can guarantee the optimal control performance and the stability of Markovian jump linear systems.
Torsional Vibration Simulation of The Crankshaft Based on AVL Excite Designer

This paper discusses about torsional vibration simulation of the crankshaft which is one of the important component of internal combustion engine. Torsional vibration simulation of the crankshaft is an important stage during development of the crankshaft design. In this study, a four-cylinder, four-stroke inline gasoline engine's crankshaft, with bore and stroke 86 mm, was modelled using Solidworks and AVL Excite Designer. In the first steps design, three-dimentional model of crankshaft was built using Solidworks software which is used to obtain value of moment of inertia of each component of cranktrain and was inputed to AVL Excite Designer. In the next steps, in AVL Excite Designer, shaft and torsional modeller as a actual crankshaft. In this simulation external loading was taken from data base AVL Excite Designer with variables load. The calculations reaction torque, torque of each element on the crankshaft, critical speed, moment of inertia, and shear stress of the crankshaft was resulted. The results obtained are used as valuable reference in the optimization and improvement to the crankshaft design.
Structured Prediction Networks through Latent Cost Learning

Structured prediction provides a flexible modeling framework to deal with several relevant problems. Sequences, Trees, Disjoint Intervals and Matching are some useful examples of the type of structures we would like to predict. An elegant learning scheme for this prediction setting is the Structured Perceptron algorithm, which is sure to converge under some linear separability conditions. The framework integrates a very simple Structured layer on top of a latent costs network. Our key contribution is a novel loss function that incorporates structural information and simplifies learning. The effectiveness of this framework is illustrated with sequence prediction problems. We explore LSTM neural network architectures to model the latent costs layer, since our experiments concern NLP tasks. We perform basic experiments with Chunking in English. The SPN predictor outperforms its CRF equivalent. Our initial findings strongly indicate that SPN is a versatile framework with a powerful learning strategy.
Enhancing the SNR of BOTDR by Gain-Switched Modulation

A scheme utilizing gain-switched modulation to enhance the signal-to-noise ratio (SNR) of Brillouin optical time domain reflectometer (BOTDR) is proposed and evaluated. The modulation mechanism of the gain switch is analyzed and how it improves the SNR of BOTDR is revealed. The experimental results show that the gain switch can produce optical pulses with the extinction ratio of 51.26 dB, nearly 16 dB higher than that of a conventional electro-optical modulator (EOM). The root-mean-square error of the measured Brillouin frequency shift at 9.941 km of the sensing fiber decreases from 2.49 to 0.78 MHz, and the fluctuation range minishes from 7.84 to 2.76 MHz. Meanwhile, the measurement distance of BOTDR utilizing the gain-switched modulation can be extended from 10.75 to 27.5 km with the 1-m spacial resolution under the same measurement condition compared to the conventional EOM.
General Regression Neural Networks for the Concurrent, Timely and Reliable Identification of Detector Malfunctions and/or Nuclear Reactor Deviations from Steady-State Operation

The analysis and understanding of the neutron flux (NF) signals of nuclear reactors (NRs) is imperative for ensuring safe and optimal (expressed in terms of minimal fuel use for maximal energy production) on-line NR operation. The NF perturbations are of particular interest, as they provide detailed information concerning the instantaneous changes in NR operation/status. In this piece of research, general regression artificial neural networks (GRNNs) are proposed for concurrently identifying NR deviations from steady-state operation as well as neutron detector (ND) malfunctions in a timely, reliable and efficient manner. On the one hand, the use of (a) raw, minimalistic NF signals and (b) complementary signal encodings – derived from pertinent and limited in size ND configurations – of the problem space, renders the proposed approach timely/efficient, modular as well as flexible. On the other hand, the GRNN characteristics of (i) transparency of construction, (ii) low computational (time/space) complexity of training and testing, (iii) accuracy, consistency and good generalization in the identification of the cause(s) behind deviating-from-normal NR behaviour and (iv) efficient operation and partial only GRNN retraining following modification of the training set, support the use of the proposed methodology. It is envisaged that, by appropriately combining the responses derived from different GRNNs, both accuracy and sensitivity of deviation detection as well as of malfunction localization shall be further improved at minimal additional computational load.
Drift-Aware Methodology for Anomaly Detection in Smart Grid

Energy efficiency and sustainability are important factors to address in the context of smart cities. In this sense, smart metering and nonintrusive load monitoring play a crucial role in fighting energy thefts and for optimizing the energy consumption of the home, building, city, and so forth. The estimated number of smart meters will exceed 800 million by 2020. By providing near real-time data about power consumption, smart meters can be used to analyze electricity usage trends and to point out anomalies guaranteeing companies’ safety and avoiding energy wastes. In literature, there are many proposals approaching the problem of anomaly detection. Most of them are limited because they lack context and time awareness and the false positive rate is affected by the change in consumer habits. This research work focuses on the need to define anomaly detection method capable of facing the concept drift, for instance, family structure changes; a house becomes a second residence, and so forth. The proposed methodology adopts long short term memory network in order to profile and forecast the consumers’ behavior based on their recent past consumptions. The continuous monitoring of the consumption prediction errors allows us to distinguish between possible anomalies and changes (drifts) in normal behavior that correspond to different error motifs. The experimental results demonstrate the suitability of the proposed framework by pointing out an anomaly in a near real-time after a training period of one week.