Miracle-dz/UniFAR-FTUs huggingface dataset:
Saccade-Responsive Visual Cortical Neurons Do Not Exhibit Distinct Visual Response Properties

Abstract Rapid saccadic eye movements are used by animals to sample different parts of the visual scene. Previous work has investigated neural correlates of these saccades in visual cortical areas such as V1; however, how saccade-responsive neurons are distributed across visual areas, cell types, and cortical layers has remained unknown. Through analyzing 818 1 h experimental sessions from the Allen Brain Observatory, we present a large-scale analysis of saccadic behaviors in head-fixed mice and their neural correlates. We find that saccade-responsive neurons are present across visual cortex, but their distribution varies considerably by transgenically defined cell type, cortical area, and cortical layer. We also find that saccade-responsive neurons do not exhibit distinct visual response properties from the broader neural population, suggesting that the saccadic responses of these neurons are likely not predominantly visually driven. These results provide insight into the roles played by different cell types within a broader, distributed network of sensory and motor interactions.
Ladder operators and squeezed coherent states of a three-dimensional generalized isotonic nonlinear oscillator

We explore squeezed coherent states of a three-dimensional generalized isotonic oscillator whose radial part is the newly introduced generalized isotonic oscillator whose bound state solutions have been shown to admit the recently discovered X1-Laguerre polynomials. We construct a complete set of squeezed coherent states of this oscillator by exploring the squeezed coherent states of the radial part and combining the latter with the squeezed coherent states of the angular part. We also prove that the three-mode squeezed coherent states resolve the identity operator. We evaluate Mandel’s Q-parameter of the obtained states and demonstrate that these states exhibit sub-Possionian and super-Possionian photon statistics. Furthermore, we illustrate the squeezing properties of these states, both in the radial and angular parts, by choosing appropriate observables in the respective parts. We also evaluate the Wigner function of these three-mode squeezed coherent states and demonstrate the squeezing property explicitly.
Considerations on the Angular Error Due to the Shaft Eccentricity and the Compensation Effect by Short-Circuit Winding on a Resolver

When a built-in resolver is incorporated into a motor shaft, there is sometimes a problem of an eccentricity error caused by mounting misalignment and tilting. This eccentricity error affects the sensing accuracy of the resolver and causes an angular error. A short-circuit winding applied in quadrature to the exciting winding helps to reduce the angular error due to the shaft eccentricity if the internal impedance of the short-circuit winding is optimally designed. This paper (1) analyzes the mechanism whereby the angular error leads to an error equation, (2) analyzes how the short-circuit winding reduces the magnetic flux in the quadrature axis, which acts to induce the angular error, and (3) clarifies the conditions for realizing a resolver error of under 10' at an eccentric rate (δ/g×100%) of under 50%.
Uniform attractors for measure-driven quintic wave equation with periodic boundary conditions

We give a detailed study of attractors for measure driven quintic damped wave equations with periodic boundary conditions. This includes uniform energy-to-Strichartz estimates, the existence of uniform attractors in a weak or strong topology in the energy phase space, the possibility to present them as a union of all complete trajectories, further regularity, etc.
Slab window–related magmatism as a probe for pyroxenite heterogeneities in the upper mantle

New high-precision trace-element analyses of magmatic olivines point to a pyroxenite-dominated source for recent alkali basalts erupted above slab windows formed along the Antarctic Peninsula. Melting occurred at ambient mantle temperature, and basalts have geochemical compositions that are indistinguishable from ocean-island basalts (OIBs). We propose that the pyroxenite component originally resided in the upper mantle beneath the subducted slab; formation of a slab window allowed limited decompression and the generation of melts of garnet-pyroxenite, but little or no melting of mantle peridotite. The pyroxenite component in the mantle formed ca. 550 Ma, an age that does not require long-term recycling of subducting slabs to the core-mantle boundary. Enriched mid-ocean ridge basalt (E-MORB) from the adjacent extinct Phoenix Ridge owes its enriched trace-element compositions to mixing between small melt fractions of pyroxenite and peridotite during a period of decreased spreading rate prior to the death of the ridge ca. 3.3 Ma. It is likely that the variable trace-element enrichment seen in East Pacific Rise E-MORB distal from hotspots results from the same process of interactions between small-melt-fraction (<~5%) melts of pyroxenite and peridotite.
Advantages of SiC-Based Devices on the Design of Dual-Active Bridge DC/DC Converter for DC faults

DC short circuit fault ride-through is a critical feature for the reliability and performance of DC microgrids. This paper presents the advantage that SiC-based devices offer for designing a Dual-Active Bridge (DAB) DC-DC converter while considering DC short circuit events. It is known that SiC-MOSFET devices have a higher transient current carrying capability than Si-IGBT devices due to their superior thermal conductivity. This characteristic of SiC-MOSFET devices is utilized to improve the design of DAB for DC short circuit fault ride-through applications. An analytical model is developed to understand the performance of a DAB during DC short circuit faults. Switching and thermal simulations are used to validate the analytical model and compare DAB designs based on SiC-MOSFET and Si-IGBT. The advantages of SiC-MOSFET enabled DAB compared to Si-IGBT enabled DAB are also quantified for a particular application of DAB in a DC microgrid. It is shown that for fault ride-through applications, DAB enabled with SiC-MOSFET can be designed for lower phase shifts compared to Si-IGBT enabled DAB, which inherently reduces the inductor size and circulating current in a DAB.
3D INS/UWB based real time sensor fusion indoor position tracking architecture

Accurate indoor position tracking and analysis of the movement dynamics of autonomous driving systems are important challenges when it comes to automatize industrial processing, supply chains or warehouses. In this paper, the authors present an indoor position tracking architecture with a novel sensor fusion approach for autonomous robots in three-dimensional space. For robots to be able to drive autonomous, they need information of their position, speed and orientation in 3D-space. With the presented architecture, position information is provided by the Indoor Positioning System (IPS) and orientation information as well as velocity are determined by the Inertial Navigation System (INS). The proposed tracking architecture combines those informations with a sensor fusion approach, thus enabling the autonomous driving system.
Cyclotomic p-adic Multi-Zeta Values in Depth Two

In this paper we compute the values of the p-adic multiple polylogarithms of depth two at roots of unity. Our method is to solve the fundamental differential equation satisfied by the crystalline frobenius morphism using rigid analytic methods. The main result could be thought of as a computation in the p-adic theory of higher cyclotomy. We expect the result to be useful in proving non-vanishing results since it gives quite explicit formulas.
Edge-preserving Image Smoothing via Counting-weighted Total Variation

We present a new counting-weighted total variation measure, which captures the consistency of gradient directions in local regions to distinguish edges and details. A novel optimization framework with the proposed counting-weighted total variation in the l1 regularization term is then developed to realize the edge-preserving image smoothing. In order to solve the optimization problem, we adopt an iteratively re-weighted least square based algorithm. Experimental results demonstrate that the proposed method is capable of completing edge-preserving image smoothing, while avoiding blurring and over-sharpening the edge. The proposed method can also be used for the image detail enhancement without involving halos or gradient reversal artifacts, while achieve better quality scores in the comparison with other enhancement methods.
Power Management in PV-Wind Hybrid with Storage System Based on Multi-input DC-DC Converter

Both photovoltaic (PV) and wind energy is renewable energy that has intermittent power generation and implication for poor power stability. The multi-input DC-DC converter is one of the proven solutions that can be applied to multi-generation systems. In this research, simulations will be carried out to evaluate the performance of multi-input DC-DC converters with perturb and observe MPPT algorithm in off-grid hybrid PV-Wind with battery and supercapacitor energy storage based on solar irradiance intermittencies conditions. From the results, it was found that a multi-input DC-DC converter can rapidly lower ripple voltage on the load side from 12% to 1 - 2% in consequence of intermittent irradiancy even without the implementation of the algorithm on wind turbine output. Also, overshoot and undershoot of the system can be absorbed by the supercapacitor and stored effectively by the battery so that power losses can be reduced. Therefore, the multi-input converter with the proposed Perturb and Observe algorithm can be used as one of the solution concepts for hybrid renewable energy electrical systems.
Improvement of F-1 Score in Classifying Shark Data into Shark Behaviors

The objective of this paper is to improve the F-1 score computed in classifying shark raw-data into behaviors, namely; Resting, Swimming, Feeding, and Non-Directed Motion (NDM). Combining two different sets of pre-processed data into one image is examined for F-1 score improvement. The two sets of pre-processed data are Fast Fourier Transformation (FFT) and Walsh-Hadamard Transformation (WHT). Combining these two sets in a Convolutional Neural Network (CNN) model resulted in considerably improved F-1 score, while combining them in a K-Nearest Neighbors (K-NN) model averaged their individual F-1 scores.
Restrictions on the geometry of the periodic vorticity equation

We prove that several evolution equations arising as mathematical models for fluid motion cannot be realized as metric Euler equations on the Lie group of all smooth and orientation-preserving diffeomorphisms on the circle. These include the quasi-geostrophic model equation, the axisymmetric Euler flow in higher space dimensions, and De Gregorio's vorticity model equation.
Hyperspectral Sparse Unmixing Via Firm Thresholding Mapping

Hyperspectral sparse unmixing aims at finding the best subset of spectral signatures in the spectral library (known in advance) and estimating their proportions in each pixel. Recently, simultaneously sparse and low-rank representation has been widely used in hyperspectral sparse unmixing. This paper proposed a new manner to approximate the sparsity and low rankness penalties in the unmixing model. The core of the proposed framework is to design the new nonconvex penalties for efficient minimization by means of the firm thresholding mapping. Experiments conducted on the synthetic data and real data demonstrate the superiority of the proposed framework in improving the unmixing performance with respect to state-of-the-art methods.
Multi-modal Sentiment Analysis of Audio and Visual Context of the Data using Machine Learning

Sentiment analysis on video streams in real time comprises using visual and/or aural data from the data stream to identify a subject’s emotional expressions over time. Sentiment can be assessed using a variety of modalities, including speech, lip movements, and facial expression. This paper presents a multi-modal deep learning strategy for sentiment classification that fuses derived features from an audiovisual input stream in real time. The proposed system consists of four small deep neural network models that analyse visual and auditory data at the same time. To create a final forecast, the visual and audio emotion features are merged into a single stream and an exponentially weighted moving average is used to gather data over time. This paper introduces a method for multimodal sentiment analysis based on feature extraction as well as emotion recognition from text and visual modalities using convolutional neural networks. By merging visual, text, and audio capabilities, a 12% performance gain has been achieved. While using RNN-COVAREP, a few critical factors that are frequently missed in multimodal analysis in research has been analysed.
Advances and current trends on the use of nanosilica in Portland cement-based composites: An overview

Nanotechnology encompasses a broad market of multiple raw materials at scale of one billionth of meter. This market is in ongoing growing and serves many fields of science and industry, where the construction sector is no exception. Particularly, nanosilica is a material that has been recently used in the construction sector on a mass scale, but so far there is no full knowledge regarding its advantages and limitations on cement-based composites notwithstanding its current research and application. This study reports on the current advances and trends of the nanosilica synthesis and application in cement based-composites, as well as its influence on the fresh empirical, rheological, mechanical, and durability properties of these composites. The findings reported here could be useful to support better understanding regarding the effect of silica nanoparticles on the fresh, mechanical and durability performances of cement-based pastes mortars and concretes. Based on these identified outputs, benefits and limitations of the silica nanoparticles are addressed, and recommendations about its implementation are provided to carry out further performance and case studies.
Development of a CNN-based Time-Varying Reactive Power Forecast Model for Power systems

Reactive power plays a vital role in the stability of power system, lack of which can lead to degradation in overall system votage profile. Conventionally the reactive power demand is fulfilled by using reactive power generators such as capacitor banks installed in the transmission grid. However, this method of reactive power generation is not effective in achieving power flow control and minimizing power losses posing for a need to predict the reactive power. In this paper, a convolutional neural network based reactive power forecast model is presented. Further, to validate the model and to show its effectiveness, it is compared with artificial neural network (ANN) based and bagged regression tree (BRT) based forecasting models and it is shown that the proposed model is more accurate and effective up to 21%. Additionally, experiments were performed for various CNN architectures composed of different convolutional layers and the results of these tests were compared to ANN, BRT. It is shown that the proposed CNN provide better performance in both active and reacvtive power forecasting.
Scaling of EPC’s 100 V Enhancement-Mode Power Transistors

The same core device model is shown to accurately reproduce the current-voltage and capacitance-voltage characteristics of enhancement-mode GaN 100 V power transistors of various sizes. Using linear scaling, excellent agreement with measurements is achieved over an order of magnitude variation in total gate width. Fractional variation in on-resistance is shown to decrease with increasing transistor size, with implications for integrated circuit-sized transistors where gate width decreases by up to 5 orders of magnitude.
A Holistic and State of the Art of Understanding the Linkages of Smart-City Healthcare Technologies

One can expect a day to come where internet would connect people from across the globe whenever required and become a mainstay for business function. But the realization of this would need a lot of time. Internet of Things is still in a primary stage and development has a long way to go. Wide range applications for IoT require infrastructure, application architecture, security measures and a standard system is yet to be established. By introducing IoT, several challenges, constraints, security concerns have to be addressed differently unlike the currently existing data systems. The current systems use make use of easy to implement standards and these are compatible for most of the devices used in communications and storage systems. But no such standard exists for devices using IoT owing to the various constraints arising between different devices that results in different classifications within IoT itself. It also discusses the requirement of security architectures for IoT and feasible mechanisms that can be implemented for various architecture layers of IoT for e-Health applications. The challenges faced in order to integrate IoT into health care applications are also discussed alongside the standards and security mechanisms. IoT can be considered as a key to providing improved medical facilities for patients whilst facilitating doctors, staff and hospitals. Most importantly, this work presents the ways to handle security for implementing IoT in healthcare sector not only in the currently existing systems but also for future applications. The current systems that use various domains and different technologies have been studied and analyzed to present a clear and precise solutions to challenges faced by IoT implementations as of today.This electronic document is a “live” template and already defines the components of you
Design and Implementation of Automation Control System for Photovoltaic Modules Coating

The aging and damage of the coating are the major factors that decrease the photoelectric conversion efficiency of PV modules in outdoor environment. A feasible solution is to coat a new anti-reflection film on PV modules. In this paper, an automation control system for coating device of PV modules is designed, which can operate intelligently on the PV modules without manual intervention. The spraying module, walking mechanism and linear drive unit of the coating device are dynamically allocated through the designed control system based on the coating procedure. The experimental results show that the system can automatically control the device to accomplish the coating process of the whole PV modules and solve the problems including uneven coating thickness, low utilization rate of coating solution, and high work intensity in manual coating methods.
A note on the voting problem

Let $v(n)$ be the minimum number of voters with transitive preferences which are needed to generate any strong preference pattern (ties not allowed) on $n$ candidates. Let $k=\lfloor \log_2 n\rfloor$. We show that $v(n)\le n-k$ if $n$ and $k$ have different parity, and $v(n)\le n-k+1$ otherwise.
A Fully Integrated 1 MHz – 2.5 GHz Radiation-Hardened All-Digital Frequency Synthesizer

A fully integrated radiation-hardened all-digital frequency synthesizer, fabricated in a commercial CMOS technology, is presented. Single event monitoring of the Phase-Locked Loop (PLL) is proposed by comparing the Time-to-Digital Converter (TDC) output with a threshold limit. With 2.5 GHz at the LVDS output, the measured phase noise performance is -101 dBc/Hz at 10 kHz and -99 dBc/Hz at 100 kHz offset frequencies. An integrated phase jitter of 460 fs is measured using the on-chip oscillator. When using a 48 MHz reference crystal, the overall dynamic power consumption is 90 mW at 1.2 V core and 2.65 V IO supplies, respectively. The radiation tolerance is validated up to a total-ionizing-dose (TID) level of 1 kGy and a single-event latch-up (SEL) / single-event upset (SEU) level of 62.5 MeV·cm2/mg.
On The Convergence of Linearized ADMM for Separable Reweighted Sparse Hyperspectral Unmixing

Reweighted sparse regularization is a popular technique in sparse hyperspectral unmixing (SHU). Alternating direction method of multipliers (ADMM) is used with local linearization to solve reweighted SHU models. Theoretical guarantee of its convergence is a fundamental issue, but is not clear owing to the nonconvex nature of reweighted sparsity. In this paper, the convergence of linearized ADMM for separable, reweighted SHU is analyzed. It is proved that the algorithm has a converging sequence toward a stationary point of SHU problems with separable concave penalties. Practical convergence behavior is also numerically examined and analytically characterized against two basic penalties: the log-sum penalty and the arctangent.
Determination of the Number of Valid Scan Pairs in a Multielement Waveguide Simulator

The multielement waveguide simulator is an experimental technique that enables computation of the active reflection coefficient for elements in a large array phased to a certain scan angle pair $(\theta ,\phi )$. This paper shows that the number of scan pairs is equal to the number of modes that are excitable in the waveguide simulator. The problem of solving for the number of valid scan pairs is seen to be a variant of the Gauss Circle Problem with those modes which cannot be excited removed. A 4×3 waveguide simulator is used as a case study to illustrate the design of a simulator and physics behind the inability to excite certain modes.
Homology cycles in manifolds with locally standard torus actions

Let $X$ be a $2n$-manifold with a locally standard action of a compact torus $T^n$. If the free part of action is trivial and proper faces of the orbit space $Q$ are acyclic, then there are three types of homology classes in $X$: (1) classes of face submanifolds; (2) $k$-dimensional classes of $Q$ swept by actions of subtori of dimensions $<k$; (3) relative $k$-classes of $Q$ modulo $\partial Q$ swept by actions of subtori of dimensions $\geqslant k$. The submodule of $H_*(X)$ spanned by face classes is an ideal in $H_*(X)$ with respect to the intersection product. It is isomorphic to $(\mathbb{Z}[S_Q]/\Theta)/W$, where $\mathbb{Z}[S_Q]$ is the face ring of the Buchsbaum simplicial poset $S_Q$ dual to $Q$; $\Theta$ is the linear system of parameters determined by the characteristic function; and $W$ is a certain submodule, lying in the socle of $\mathbb{Z}[S_Q]/\Theta$. Intersections of homology classes different from face submanifolds are described in terms of intersections on $Q$ and $T^n$.
A novel integrated cooling packaging for high power density semiconductors

This work investigates a packaging solution for high power density semiconductors (> 200W /cm2), allowing for a dramatic reduction in size and complexity of power electronics modules. The multiple layers in standard packaging structures degrade the cooling efficiency due as they lengthen the path between dies and heatsinks. Here, we reduce the layer count by merging the ceramic substrate and the heat exchanger in a single part. CFD simulations and experimental validation are performed on a single-chip cooling packaging, and demonstrate a 10–20 % reduction in thermal resistance over more traditional cooling solutions.