Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Excitotoxic and Immunotoxic Lesions of the Posterior Parietal Cortex on Attention

Basal forebrain corticopetal cholinergic neurons are necessary for normal attentional processing. However, the interactions of acetylcholine with processing mediated by particular cortical regions remain unclear. The posterior parietal cortex has been implicated in models of attention, including the ability to selectively attend to target stimuli when distracting stimuli are presented. In the present experiment, rats were trained to perform a two-lever attention task that required discrimination of visual signals and trials when no signal was presented. Animals then received infusions of the cholinotoxin, 192IgG-saporin, the excitotoxin, n-methyl-D-aspartate, or vehicle into the posterior parietal cortex (n=9/group). Postsurgically, rats were tested for 30 sessions in the same task trained before surgery followed by 30 sessions with the houselight flashed one sec prior to a signal or non-signal. Lesions did not differentially affect performance in the task tested immediately following surgery. However, when the houselight was flashed prior to the signal or non-signal, both lesion groups were differentially affected compared to sham-lesioned animals. Sham-lesioned animals showed a decrease in the latency to press a lever following lever extension when the houselight was flashed compared to sessions when it was not flashed. However, cholinotoxic lesioned animals did not show this effect. Furthermore, exploratory analyses revealed an elevated omission rate for excitotoxic lesioned animals compared to sham-lesioned animals during sessions when the houselight was flashed. The present data are discussed in regards to the posterior parietal cortex and its cholinergic afferents from the basal forebrain and the role they play in maintaining attentional performance when task irrelevant stimuli are presented. EFFECTS OF EXCITOTOXIC AND IMMUNOTOXIC LESIONS OF THE POSTERIOR PARIETAL CORTEX ON ATTENTION
Tilburg University Lobbying and asymmetric information Potters

Informational lobbying the use by interest groups of their (alleged) expertise or private information on matters of importance for policymakers in an attempt to persuade them to implement particular policies is often regarded as an important means of influence. This paper analyzes this phenomenon in a game setting. On the one hand, the interest group is assumed to have private information which is relevant to the policymaker, whilst, on the other hand, the policymaker is assumed to be fully aware of the strategic incentives of the interest group to (mis)report or conceal its private information. It is shown that in a setting of partially conflicting interests a rationale for informational lobbying can only exist if messages bear a cost to the interest group and if the group's preferences carry information in the 'right direction'. Furthermore, it is shown that it is not the content of the message as such, but rather the characteristics of the interest group that induces potential changes in the policymaker's behavior. In addition, the model reveals some interesting results on the relation between, on the one hand, the occurrence and impact of lobbying and, on the other hand, the cost of lobbying, the stake which an interest group has in persuading the policymaker, the similarity between the policymaker's and the group's preferences, and the initial beliefs of the policymaker. Moreover, we relate the results to some empirical findings on lobbying. Much o f the pressure placed upon government and its agencies takes the form o f freely provided "'objective" studies showing the important outcomes to be expected f rom the enactment o f particular policies (Bartlett, 1973: 133, his quotation marks). The analysis here is vague. What is needed is an equilibrium model in which lobbying activities have influence. Incomplete information ought to be the key to building such a model that wouM explain why lobbying occurs (information, collusion with decision makers, and so on) and whether lobbying expenses are socially wasteful. (Tirole, 1989: Ch. 1.3, p. 77, Rentseeking behavior). * We are grateful for comments made by participants of the workshop 'Economic Models of Political Behavior' of the European Consortium of Political Research (Bochum, 2-7 April 1990), the European Public Choice Society Meeting (Meersburg, 18-21 April 1990), the World Congress of the Econometric Society (Barcelona, 22-28 August, 1990), and the Congress of the European Economic Association (Lisbon, 31 August 2 September 1990). In particular we acknowledge the helpful and stimulating comments by Eric Drissen, John Hudson, Karl Dieter Opp, Arthur Schram, and Franz Wirl.
THE PBIL ALGORITHM APPLIED TO A NUCLEAR REACTOR DESIGN OPTIMIZATION

The Population-Based Incremental Learning (PBIL) algorithm is a method that combines the mechanism of genetic algorithm with the simple competitive learning, creating an important tool to be used in the optimization of numeric functions and combinatory problems. PBIL works with a set of solutions to the problems, called population, whose objective is create a probability vector, containing real values in each position, that when used in a decoding procedure gives subjects that present the best solutions for the function to be optimized. In this work a new form of learning for algorithm PBIL is developed, having aimed at to reduce the necessary time for the optimization process. This new algorithm will be used in the nuclear reactor design optimization. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichement zone reactor, considering some restrictions. In this optimization is used the computational code HAMMER, and the results compared with other methods of optimization by artificial intelligence.
Does Money Illusion Delude Investors ? Evidence from Anomalies

This paper investigates the role of money illusion in the anomaly-based strategies. To the extent that anomalies reflect mispricing, I examine whether money illusion predicts anomaly returns. I find that, following high inflation, anomalies are stronger and the returns on the short-leg portfolios are lower. These findings indicate that money illusion leads to mispricing in the stock market. I explore the source of money illusion-driven mispricing. I find that money illusion negatively predicts forecast errors and dispersion. These results suggest that investors overestimate the upside potential of stock returns following high inflation and are subsequently surprised by the return reversal. JEL Classification: G02, G12, E31
The Price Is (Not) Right: Reflections on Pricing for Transient Cloud Servers

Amazon introduced spot instances in December 2009, enabling ``customers to bid on unused Amazon EC2 capacity and run those instances for as long as their bid exceeds the current Spot Price. '' Amazon's real-time computational spot market was novel in multiple respects. For example, it was the first (and to date only) large-scale public implementation of market-based resource allocation based on dynamic pricing after decades of research, and it provided users with useful information, control knobs, and options for optimizing the cost of running cloud applications. Spot instances also introduced the concept of transient cloud servers derived from variable idle capacity that cloud platforms could revoke at any time. Transient servers have since become central to efficient resource management of modern clusters and clouds. As a result, Amazon's spot market was the motivation for substantial research over the past decade. Yet, in November 2017, Amazon effectively ended its realtime spot market by announcing that users no longer needed to place bids and that spot prices will ``. adjust more gradually, based on longer-term trends in supply and demand. '' The changes made spot instances more similar to the fixed-price transient servers offered by other cloud platforms. Unfortunately, while these changes made spot instances less complex, they eliminated many benefits to sophisticated users in optimizing their applications. This paper provides a retrospective on Amazon's real-time spot market, including its advantages and disadvantages for allocating transient servers compared to current fixed-price approaches. We also discuss some fundamental problems with Amazon's spot market, which we identified in prior work (from 2016), that predicted its eventual end. We then discuss potential options for allocating transient servers that combine the advantages of Amazon's real-time spot market, while also addressing the problems that likely led to its elimination.
Response of Some Rice Varieties to Different Crop Management Practices towards Morphological and Yield Parameters

The present investigation was undertaken with seven rice varieties to assess their responses to three management practices, namely System of Rice Intensification (SRI), Recommended Practice (RP) and Traditional Practice (TP) towards morpho-physiological and yield parameters. The results indicated that all the cultivars under study performed better under SRI as compared to other two practices with respect to different morphological and yield traits including grain yield. All the cultivars exhibited delayed panicle initiation, increased leaf area, leaf area index and leaf area duration under SRI practice as compared to other two cultivation practices. Among the varieties, PAC 837 recorded the highest grain yield (7.83 t/ha), followed by Arize 6444 (6.55 t/ha) and IR-64 (5.07 t/ha) under SRI practice.
International consensus International consensus ( ICON ) on treatment of sudden sensorineural hearing loss

Sudden sensorineural hearing loss (SSNHL) is a common and alarming symptom that often prompts an urgent visit to an ENT specialist. Treatment of SSNHL remains one of the most problematic issues for contemporary otorhinolaryngology: although many meta-analyses and national guidelines have been issued, management is not standardized in terms of medical treatment, and duration and route of administration. We present several methodological suggestions for the study of treatments for SSNHL. These were developed from the existing level of evidence of the main treatments used in SSNHL by experts who convened at the IFOS 2017 ENT World Congress in Paris, France. All panelists agreed that one of the main limitations present in studies on SSNHL is related to the wide heterogeneity, which characterizes both the initial hearing deficit and the amount of hearing recovery. Although evidence of the efficacy of systemic steroids cannot be considered as strong enough to recommend their use, it is still the most widespread primary therapy and can be considered as the current standard of care. Therefore, systemic steroids stand as an adequate control for any innovative treatment. To reduce the number of subjects we suggest that the inclusion criteria should be restricted to moderate to profound levels of hearing loss. The efficacy of trans-tympanic steroids as a salvage therapy was suggested in several reports on small populations and needs to be confirmed with larger randomized controlled trials. © 2018 Elsevier Masson SAS. All rights reserved.
Low Precision Electroencephalogram for Seizure Detection with Convolutional Neural Network

Electroencephalogram (EEG) neural activity recording has been widely used for diagnosing and monitoring epileptic patients. Ambulatory epileptic monitoring devices that can detect or even predict seizures play an important role for patients with intractable epilepsy. Though many EEG-based seizure detection algorithms have been proposed in the literature with high accuracy, their hardware implementations are constrained because of power consumption. Many commercial non-research EEG monitoring systems samples multiple electrodes at a relatively high rate and transmit the data either via a wire or wirelessly to an external signal processing unit. In this work, we studied how a reduced sampling precision affects the performance of our machine learning signal processing in seizure detection. To answer this question, we reduce the number of bits (precision) in an analog-to-digital converter (ADC) used in an EEG recorder. The outcome shows that the reduction of ADC precision down to 6-bit does not significantly reduce our convolutional neural network performance in detecting seizure onsets. As an indication of the performance, we achieved an area under the curve (AUC) more than 92% and above 96% on Freiburg Hospital and the Boston Children’s Hospital-MIT seizure datasets, respectively. A possible reduction in ADC precision not only contribute to energy consumption reduction, particularly if the data has to be transmitted, but also offers an improved computational efficacy regarding memory requirement and circuit area.
Higher School of Economics Co-Authorship Network Study

Co-authorship networks represent a graph, in which vertices are authors, and edges represent research papers written in co-authorship. Every paper could generate several edges in such a graph, if a number of coauthors is greater than two. Co-authorship networks play important role in understanding the structure of research collaborations usually resulted in joint research papers. Moreover, when analyzing university ranking and research staff publishing activity, coauthorship network may help identifying both, efficient research communities and also people, who lack proper collaborators while having poor research results. Our paper is devoted to the visualization and interpretation of the former sets using as an example co-authorship network of National Research University Higher School of Economics (HSE), Moscow, Russia, while we also discuss the possible solutions for recommending collaborators for the latter set of researchers with low academic profile. Our paper is a case study for our university, which can be extended to larger co-authorship networks using research indexing services.
A Neighbor Discovery Algorithm for Flying Ad Hoc Network Using Directional Antennas

In this paper, we consider the neighbor discovery problem in Flying Ad Hoc Network using directional antennas. Based on realistic scenarios, the height of UAVs can be approximated to follow a Gaussian distribution. A novel 2-way neighbor discovery algorithm that adjusts the selection probability according to transmission space divided by pitch angle parameter is proposed. Then we derive analytical expressions of the probability of a successful discovery between two nodes and the expected value of time slots needed to complete the neighbor discovery. We also adopt a practical way to determine when to terminate the discovery procedure. Lastly, simulation results are presented to show the effectiveness of proposed algorithm.
Optimization of Hybrid Systems ’Operating Modes Based on Renewable Energy

The development of the isolated and decentralized territories’ energy infrastructure is one of the most important issues for the development of world energy. This task was set by the International Energy Agency within the framework of the adopted energy policy until 2050. Isolated energy supply zones in Russia cover up more than 60% of the entire territory. Authors in this paper define the operation modes of the hybrid systems with a high penetration level of diesel fuel. In the article, a multipurpose selection method of the main and auxiliary equipment of the energy complex based on renewable energy sources was showed. In the article, the process of creating unique laboratory bench for conducting simulation studies of wind power plants was described. The research work was completed during ENI CBC project Energy-efficient systems based on renewable energy for Arctic conditions "EFREA" financed by the European Union, the Russian Federation, and the Republic of Finland.
Real Time Adaptive Filter based EMG Signal Processing and Instrumentation Scheme for Robust Signal Acquisition Using Dry EMG Electrodes

Bio signals provide us with information which gives us valuable insight into the natural processes occurring inside the human body. This makes it absolutely imperative for the signal to be free of noise so that it provides a worthy estimate of the information provided by the bio signals. This paper focuses on the signal processing aspect of electromyography. We propose an adaptive filter based signal processing scheme in real time to remove noise from the EMG signal taking into account the unpredictable nature and changing dynamics of noise picked up from the surrounding environment while using dry EMG electrodes. In addition, we also propose an instrumentation scheme which not only cancels noise using analogue filters and Driven Right Leg circuit, but also provides a signal offset without the use of adder circuits to enable an ADC to acquire the signal. In order to validate the performance of the proposed signal processing and instrumentation scheme, we perform an analysis of the filtered EMG signal in time and frequency domains in terms of signal to noise ratio, discrete Fourier transform, and cross spectral density. In the end, we will come to a conclusion that the signal acquisition scheme proposed provides an EMG signal which sufficiently reduces noise and can be useful for various EMG applications.
Slasher: Stadium racer car for event camera end-to-end learning autonomous driving experiments

Slasher is the first open 1/10 scale autonomous driving platform for exploring the use of neuromorphic event cameras for fast driving in unstructured indoor and outdoor environments. Slasher features a DAVIS event-based camera and ROS computer for perception and control. The DAVIS camera provides high dynamic range, sparse output, and sub-millisecond latency output for the quick visual control needed for fast driving. A race controller and Bluetooth remote joystick are used to coordinate different processing pipelines, and a low-cost ultra-wide-band (UWB) positioning system records trajectories. The modular design of Slasher can easily integrate additional features and sensors. In this paper, we show its application in a reflexive Convolutional Neural Network (CNN) steering controller trained by end-to-end learning. We present preliminary experiments in closed-loop indoor and outdoor trail driving.
A Survey on Android Malware Detection Techniques Using Machine Learning Algorithms.

The smartphones users have been rapidly increasing over the years, mainly the Android users. The main concern on the Android platform growing is malware detection. Among the various existing approaches in detecting malware, machine learning-based algorithms have achieved a high accuracy in detecting malware. Therefore, Android devices must be provided with the ability to detect malware using machine learning algorithms. Different researchers proposed different Machine learning systems to detect malware based on Machine Learning Algorithms such as SVM, NB, and DNN. This paper surveys the state of the art on Android malware detection techniques by focusing on Machine Learning-based classifiers to detect malicious software on Android devices.
Beam-Oriented Digital Predistortion for Hybrid Beamforming Array Utilizing Over-the-Air Diversity Feedbacks

In this paper, a novel beam-oriented digital pre-distortion (DPD) is proposed for linearizing the hybrid beamforming large-scale array. Utilizing the over-the-air (OTA) diversity feedbacks, the proposed DPD can solve the hardware implementation issue of conventional DPD architecture in hybrid beamforming array by estimating and linearizing the main beam signal while avoiding the complex feedback configurations. Mathematical analysis proves that it is possible to derive the main beam signal from the OTA feedback information directly. The predistorter can then be identified based on the estimated main beam signal. Furthermore, the OTA diversity feedback configuration is suggested to address the problem of insufficient nonlinear information occasionally appearing on the single OTA feedback. The experimental tests were performed on a 4-antenna array using two OTA observations, and a more than 13 dB ACPR improvement has been realized with the proposed DPD.
Reassessing InGaAs for Logic: Mobility Extraction in sub-10nm Fin-Width FinFETs

We study the performance degradation of InGaAs FinFETs as they scale to sub-10 nm fin width. This is often attributed to degradation in intrinsic transport parameters. High frequency measurements, however, indicate increasingly severe oxide trapping as the fin width narrows. This is confirmed by pulsed-IV measurements. A new mobility extraction method using concurrent S-parameter and DC-IV measurements avoids the impact of oxide trapping and reveals promising mobility in thin-channel InGaAs planar MOSFETs and narrow-width FinFETs. Our study suggests that performance degradation of InGaAs FinFETs is largely an extrinsic phenomenon that can be engineered around and that the potential performance of deeply-scaled InGaAs FinFETs is significantly underestimated.
Rectangular Waveguide Quadruplet Filter for Satellite Applications

The objective of this paper is to describe the design of a new single-mode quadruplet filter structure that allows for the implementation of high-selectivity elliptic transfer functions in rectangular waveguide technology. The resulting filter structure is particularly compact, does not contain any tuning element, and is ideally suited for modern multi-beam satellite applications. In addition to theory, we compare the results of our simulations with the ones obtained with well established full wave EM simulators. Finally, measured results of a fabricated prototype of the filter are also presented thereby fully validating the new filter structure.
Analytical Approach to Optimize Battery Life using Fog-IoT Paradigm

The battery efficient concept is implemented in smartphones with the help of Internet of Things IoT application and Fog computing. The need of the most diverse technology is to increase battery capability to increase response time in smart devices by reducing heat emission in devices. Heat emission that gradually reduces the functionality of device on complex tasks and occupy major part of resources of processor in smartphone. Fog computing is further extended part of cloud computing that is also known as edge networking due to the presence of Fog nodes in the architecture. Whereas, Internet of Things IoT implementation is responsible to monitor and control data that is generated in huge amount by IoT devices such as sensors, grid or edges, cloud and mobile application. These technologies have one thing in common that they analysis real-time and time-sensitive data at the edge of network. In this paper we describe the implementation of FogIot paradigm that helps in optimizing the use of battery and reduces heat emission of smartphone to make it performance efficient. Experimental result shows combined implementation of fog computing and IoT applications in smartphones.
Framework for Implementation of Cognitive Radio Based Ultra-Dense Networks

As the standardization process for the coming 5G networks has entered its final stages, the research activities are ever growing with emphasis on practical deployment scenarios which incorporate many aspects of this family of technologies. Among them are the Ultra-dense network (UDN) and Cognitive Radio (CR) concepts which have both in their own right been objects of intense scientific interest which has yielded many significant accomplishments. Integrating CR functionalities in UDN is a viable solution to the spectrum shortage issue which is ever more critical, especially in such massive deployment scenarios. However, it has only been shallowly explored. This paper expands on the basic approaches for the realization of UDN and CR networks, to present a detailed conceptual scheme for the operation of CR-based nodes operating within the context of UDN along with their relevant functionalities.