Miracle-dz/UniFAR-FTUs huggingface dataset:
High frequency guided mode resonances in mass-loaded, thin film gallium nitride surface acoustic wave devices

We demonstrate high-frequency (> 3 GHz), high quality factor radio frequency (RF) resonators in unreleased thin film gallium nitride (GaN) on sapphire and silicon carbide substrates by exploiting acoustic guided mode (Lamb wave) resonances. The associated energy trapping, due to mass loading from the gold electrodes, allows us to efficiently excite these resonances from a 50 $\Omega$ input. The higher phase velocity, combined with lower electrode damping, enables high quality factors with moderate electrode pitch, and provides a viable route towards high-frequency piezoelectric devices. The GaN platform, with its ability to guide and localize high-frequency sound on the surface of a chip with access to high-performance active devices, will serve as a key building block for monolithically integrated RF front-ends.
PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing

In this paper, we introduce a Point Recurrent Neural Network (PointRNN) for moving point cloud processing. At each time step, PointRNN takes point coordinates $\boldsymbol{P} \in \mathbb{R}^{n \times 3}$ and point features $\boldsymbol{X} \in \mathbb{R}^{n \times d}$ as input ($n$ and $d$ denote the number of points and the number of feature channels, respectively). The state of PointRNN is composed of point coordinates $\boldsymbol{P}$ and point states $\boldsymbol{S} \in \mathbb{R}^{n \times d'}$ ($d'$ denotes the number of state channels). Similarly, the output of PointRNN is composed of $\boldsymbol{P}$ and new point features $\boldsymbol{Y} \in \mathbb{R}^{n \times d''}$ ($d''$ denotes the number of new feature channels). Since point clouds are orderless, point features and states from two time steps can not be directly operated. Therefore, a point-based spatiotemporally-local correlation is adopted to aggregate point features and states according to point coordinates. We further propose two variants of PointRNN, i.e., Point Gated Recurrent Unit (PointGRU) and Point Long Short-Term Memory (PointLSTM). We apply PointRNN, PointGRU and PointLSTM to moving point cloud prediction, which aims to predict the future trajectories of points in a set given their history movements. Experimental results show that PointRNN, PointGRU and PointLSTM are able to produce correct predictions on both synthetic and real-world datasets, demonstrating their ability to model point cloud sequences. The code has been released at \url{this https URL}.
Testing Hamiltonian Reduced QED

Certain gauge transformations may act non-trivially on physical states in quantum electrodynamics (QED). This observation has sparked the yet unresolved question of how to characterize allowed boundary conditions for gauge theories. Faddeev and Jackiw proposed to impose Gauss' law on the action to find the Hamiltonian reduced theory of QED. The reduction eliminates the scalar gauge mode, renders the theory manifestly gauge invariant and the symplectic form non-singular. In this work we show that while the predictions of the reduced theory coincide with those of conventional QED for scattering events, it is experimentally distinguishable. Quantum interference of charges traveling along time-like Wilson loops that encircle (but remain clear of) electric fields is sensitive to a relative phase shift due to an interaction with the scalar potential. This is the archetypal electric Aharonov-Bohm effect and does not exist in the reduced theory. Despite its prediction over six decades ago, and in contrast to its well known magnetic counterpart, this electric Aharonov-Bohm phenomenon has never been observed. We present a conclusive experimental test using superconducting quantum interferometry. The Hamiltonian reduction renders a theta term non-topological. We comment on consequences for semi-classical gravity, where it may alleviate a problem with the measure.
Psychic Disturbances in Adult Coeliac Disease

Untreated adult coeliac patients have previously been shown to have a high frequency of depressive symptoms as reported in a personality inventory (the MMPI). In the present study we determined the concentrations of three major monoamine metabolites in samples of lumbar cerebrospinal fluid of ten consecutive adults with newly detected coeliac disease. They showed significant reduction in levels of 5-HIAA (70.3 ± 25.4 pmol/ml), HVA (128.2 ± 58.3 pmol/ml), and MOPEG (27.7 ± 7.4 pmol/ml), indicating reduced central metabolism in all three monoamine pathways. The concentrations, in particular that of MOPEG, were inversely correlated with depressive symptoms reported on the MMPI scale 2 (‘depression’), which conforms with current concepts on the pathogenesis of depression.
Prevalência e fatores de risco para transtornos mentais comuns entre estudantes de medicina

OBJETIVO: Estimar a prevalencia de transtornos mentais comuns entre estudantes de medicina e respectivos fatores de risco. METODOS: Estudo transversal realizado com 551 universitarios de um curso de medicina de Botucatu, SP. Utilizou-se questionario auto-aplicavel investigando aspectos socio demograficos, relacionados ao curso e o Self Reporting Questionnaire. Para analise dos dados empregaram-se os testes de qui-quadrado e regressao logistica. RESULTADOS: Participaram 82,6% dos alunos matriculados no curso, predominando mulheres (61%), jovens (60% 20-23 anos), procedentes de outros municipios (99%). A prevalencia de transtornos mentais comuns foi de 44,7% associando-se independentemente a: dificuldade para fazer amigos (RC=2,0), avaliacao ruim sobre desempenho escolar (RC=1,7), pensar em abandonar o curso (RC=5,0), nao receber o apoio emocional de que necessita (RC=4,6). Embora na primeira analise a prevalencia tenha se mostrado associada ao ano do curso, esta associacao nao se manteve na analise multivariada. CONCLUSOES: A prevalencia de transtornos mentais comuns mostrou-se elevada entre os estudantes de medicina, associando-se a variaveis relacionadas a rede de apoio. As experiencias emocionalmente tensas como o contato com pacientes graves, formacao de grupos, entre outras, vividas nos ultimos anos do curso, sao provavelmente potentes estressores, especialmente para sujeitos com uma rede de apoio considerada deficiente. Sugere-se que instituicoes formadoras estejam atentas a esse fato, estabelecendo intervencoes voltadas ao acolhimento e ao cuidado com o sofrimento dos estudantes.
Improving the convergence of SGD through adaptive batch sizes

Mini-batch stochastic gradient descent (SGD) approximates the gradient of an objective function with the average gradient of some batch of constant size. While small batch sizes can yield high-variance gradient estimates that prevent the model from learning a good model, large batches may require more data and computational effort. This work presents a method to change the batch size adaptively with model quality. We show that our method requires the same number of model updates as full-batch gradient descent while requiring the same total number of gradient computations as SGD. While this method requires evaluating the objective function, we present a passive approximation that eliminates this constraint and improves computational efficiency. We provide extensive experiments illustrating that our methods require far fewer model updates without increasing the total amount of computation.
RPBA - Robust Parallel Bundle Adjustment Based on Covariance Information

A core component of all Structure from Motion (SfM) approaches is bundle adjustment. As the latter is a computational bottleneck for larger blocks, parallel bundle adjustment has become an active area of research. Particularly, consensus-based optimization methods have been shown to be suitable for this task. We have extended them using covariance information derived by the adjustment of individual three-dimensional (3D) points, i.e., "triangulation" or "intersection". This does not only lead to a much better convergence behavior, but also avoids fiddling with the penalty parameter of standard consensus-based approaches. The corresponding novel approach can also be seen as a variant of resection / intersection schemes, where we adjust during intersection a number of sub-blocks directly related to the number of threads available on a computer each containing a fraction of the cameras of the block. We show that our novel approach is suitable for robust parallel bundle adjustment and demonstrate its capabilities in comparison to the basic consensus-based approach as well as a state-of-the-art parallel implementation of bundle adjustment. Code for our novel approach is available on GitHub: this https URL
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with an MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things in a finite period. In this article, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV’s flight energy by optimizing the bits allocation, time slot scheduling, and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we propose to solve the problem by two parts, and obtain the near optimal solution by the Lagrangian duality method and successive convex approximation technique, respectively. By analysis, the proposed algorithm can be guaranteed to converge within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.
A Topological "Reading" Lesson: Classification of MNIST using TDA

We present a way to use Topological Data Analysis (TDA) for machine learning tasks on grayscale images. We apply persistent homology to generate a wide range of topological features using a point cloud obtained from an image, its natural grayscale filtration, and different filtrations defined on the binarized image. We show that this topological machine learning pipeline can be used as a highly relevant dimensionality reduction by applying it to the MNIST digits dataset. We conduct a feature selection and study their correlations while providing an intuitive interpretation of their importance, which is relevant in both machine learning and TDA. Finally, we show that we can classify digit images while reducing the size of the feature set by a factor 5 compared to the grayscale pixel value features and maintain similar accuracy.
Convergence of a first-order consensus-based global optimization algorithm

Global optimization of a non-convex objective function often appears in large-scale machine learning and artificial intelligence applications. Recently, consensus-based optimization (CBO) methods have been introduced as one of the gradient-free optimization methods. In this paper, we provide a convergence analysis for the first-order CBO method in [J. A. Carrillo, S. Jin, L. Li and Y. Zhu, A consensus-based global optimization method for high dimensional machine learning problems, https://arxiv.org/abs/1909.09249v1 ]. Prior to this work, the convergence study was carried out for CBO methods on corresponding mean-field limit, a Fokker–Planck equation, which does not imply the convergence of the CBO method per se. Based on the consensus estimate directly on the first-order CBO model, we provide a convergence analysis of the first-order CBO method [J. A. Carrillo, S. Jin, L. Li and Y. Zhu, A consensus-based global optimization method for high dimensional machine learning problems, https://arxiv.org/abs/1909.09249v1 ] without resorting to the corresponding mean-field model. Our convergence analysis consists of two steps. In the first step, we show that the CBO model exhibits a global consensus time asymptotically for any initial data, and in the second step, we provide a sufficient condition on system parameters — which is dimension independent — and initial data which guarantee that the converged consensus state lies in a small neighborhood of the global minimum almost surely.
Inflection point in the power spectrum of stellar brightness variations

Context. Stellar rotation periods can be determined by observing brightness variations caused by active magnetic regions transiting visible stellar disk as the star rotates. Successful stellar photometric surveys stemming from the Kepler and TESS observations have led to the determination of rotation periods in tens of thousands of young and active stars. However, there is still a lack of information on the rotation periods of older and less active stars like the Sun. The irregular temporal profiles of light curves caused by the decay times of active regions, which are comparable to, or even shorter than, stellar rotation periods, in combination with the random emergence of active regions make period determination for such stars very difficult. Aims. We tested the performance of a new method for the determination of stellar rotation periods against stars with previously determined rotation periods. The method is based on calculating the gradient of the power spectrum (GPS) and identifying the position of the inflection point (i.e. point with the highest gradient). The GPS method is specifically aimed at determining rotation periods of low-activity stars like the Sun. Methods. We applied the GPS method to 1047 Sun-like stars observed by the Kepler telescope. We considered two stellar samples individually: one with near-solar rotation periods (24–27.4 d) and a broad range of effective temperatures (5000–6000 K) and the other with near-solar effective temperatures (5700–5900 K) and a broad range of rotation periods (15–40 d). Results. We show that the GPS method returns precise values for stellar rotation periods. Furthermore, it allows us to constrain the ratio between facular and spot areas of active regions at the moment of their emergence. We also show that the relative facular area decreases with the stellar rotation rate. Conclusions. Our results suggest that the GPS method can be successfully applied to retrieve the periods of stars with both regular and non-regular light curves.
KerCNNs: biologically inspired lateral connections for classification of corrupted images

The state of the art in many computer vision tasks is represented by Convolutional Neural Networks (CNNs). Although their hierarchical organization and local feature extraction are inspired by the structure of primate visual systems, the lack of lateral connections in such architectures critically distinguishes their analysis from biological object processing. The idea of enriching CNNs with recurrent lateral connections of convolutional type has been put into practice in recent years, in the form of learned recurrent kernels with no geometrical constraints. In the present work, we introduce biologically plausible lateral kernels encoding a notion of correlation between the feedforward filters of a CNN: at each layer, the associated kernel acts as a transition kernel on the space of activations. The lateral kernels are defined in terms of the filters, thus providing a parameter-free approach to assess the geometry of horizontal connections based on the feedforward structure. We then test this new architecture, which we call KerCNN, on a generalization task related to global shape analysis and pattern completion: once trained for performing basic image classification, the network is evaluated on corrupted testing images. The image perturbations examined are designed to undermine the recognition of the images via local features, thus requiring an integration of context information - which in biological vision is critically linked to lateral connectivity. Our KerCNNs turn out to be far more stable than CNNs and recurrent CNNs to such degradations, thus validating this biologically inspired approach to reinforce object recognition under challenging conditions.
Directional selectivity of auditory spatial attention in multi-talker environment

When people direct their auditory selective attention in a specific direction, the information from that direction can be perceived easily, even in a noisy environment. Understanding how this effect is distributed spatially is important. The extent of spatial distribution may depend on the direction of the attention focus, and thus may differ between the frontal and oblique directions. In the present study, we investigated the dependence of the spatial extent of auditory selective attention on the focus azimuthal direction. To examine the spatial extent, speech intelligibility was measured in a multi-talker environment. In the experiment, a target sound and multiple distracting speech sounds were presented simultaneously from loudspeakers surrounding observers. By manipulating the probability of the target presentation directions, the listener’s auditory spatial attention was directed to a specific loudspeaker. The attracted direction was one of three directions: –30, 0, or +30 degrees. The results showed that the greatest improvement in intelligibility was observed at the attracted direction, for all attracted directions. The spatial extent of the auditory selective attention seems identical for all attracted directions. This means that the attention spotlight is not modulated by the direction of the attention.
EFFECT OF FOREIGN DIRECT INVESTMENT ON ECONOMIC GROWTH IN NIGERIA: 1981 – 2016. BY OLUBUNMI SOLOMON DEPARTMENT OF ECONOMICS FACULTY OF SOCIAL SCIENCES

This study investigated the effect of foreign direct investment on economic growth in Nigeria. The study covered the period of 37 years from 1981 to 2017. The annual time series data for the study were analysed using the Ordinary least square (OLS) technique. The results of the estimated model show that foreign direct investment had strong positive impact on economic growth. Foreign exchange rate and gross fixed capital formation both have positive relationships with economic growth while trade openness and expenditure on education have strong negative effect on economic growth. The study therefore concludes that foreign direct investment has long run impact on economic growth in Nigeria.
Longitudinal analysis of information security incident spillover effects

When a company is hacked, market participants take notice. This has been observed consistently for at least a decade, mostly through calculating abnormal returns of individual corporate stocks after a company’s information security incident announcement. Some researchers have found that information security incidents have had a decreasing effect on stock price over time. Their reports suggest that breach related stock price impacts have become increasingly shallow and short-lived. This has led some information security economists to suggest that market forces are not enough to incentivize sufficient corporate investment to information security. They argue that further regulation is necessary to remedy what seems like a rise in investor apathy toward corporate breaches. Other researchers, though, have cautioned that further examination is required and that other market metrics—beyond individual stock price movements—are available to better understand the effects of an information security incident. Sector-wide systematic risk is a measure of the sector’s exposure to exogenous shock. Here, this risk measurement is applied to measure the spillover effects of a corporate information security incident. I conduct 203 event studies between the years 2006 and 2016, calculating sector-wide systematic risk within American stock markets, to measure the spillover effects of data breaches within finance, healthcare, technology and services sectors. The novel application of a longitudinal analysis of variance between repeated event studies reveals that the sector-wide spillover of an incident is both significant and growing. This suggests that an increasingly compelling market incentive exists for sectors to police themselves. Also, further inquiry into common factors among outliers to these sector-wide trends may reveal best-practice strategies for information security risk management.
Pharmacological inhibition of PDE 1 by vinpocetine attenuates 3-nitropropionic acid-induced behavioral and biochemical abnormalities in rats

Enhancing cyclic nucleotide signaling by phosphodieterase inhibition (PDEs) has been reported to be beneficial in neurodegenerative disorders. The present study was designed to investigate the effect of vinpocetine (PDE1 inhibitor) in 3-nitropropionic acid (3-NP) induced experimental Huntington’s disease. 3-nitropropionic acid was administered for 14 days (10 mg/kg i.p) in rats and these animals were treated with vehicle or different doses of vinpocetine (5, 10 and 20 mg/kg i.p). Changes in body weight, cognitive and motor behavior were assessed at different time points. Biochemically markers of oxidative stress, such as striatal glutathione and malondialdehyde levels were assessed terminally. Chronic administration of 3-NP produced significant decrease in body weight, showed marked abnormalities in cognitive and motor function and increased striatal oxidative stress. Vinpocetine dose dependently attenuated 3-NP induced behavioral and biochemical toxicity. Among the doses selected, vinpocetine at a dose of 10 mg/kg i.p was observed to be most effective in improving learning and memory in morris water maze and other motor functions such as grip strength, limb withdrawal and locomotor activity in rats. Further vinpocetine significantly attenuated oxidative stress in 3-NP treated rats. The above results suggesting that inhibition of PDE1 would be therapeutically beneficial in motor disorders including Huntington’s disease.
Foreign Direct Investment and Employment in Indian Automobile Industry : An Empirical Analysis

To take the benefit of positive externalities and to augment domestic capital, FDI is sought by all countries. Amongst the potential contributions of Foreign Direct Investment (FDI) flows to the developing countries, generation of additional employment is of specific interest to the policy makers. Thus, the purpose of this research paper is to look for evidence regarding the impact of Foreign Direct Investment on employment in Indian Automobile Industry using recent firm level panel data for aperiod of fifteen years (2001-2015). Our analysis broadly concludes that increased FDI flows have led to higher levels of employment in the industry. It is also observed that technology import intensity and size of a firm positively affect the employment while capital intensity and wage share have a negative impact. A significant policy direction that emerges from the study is that in order to increase the employment, FDI should be encouraged in industries in which skilled labour is required.