Miracle-dz/UniFAR-FTUs huggingface dataset:
Network analysis of Japanese global business using quasi-exhaustive micro-data for Japanese overseas subsidiaries

Network analysis techniques remain rarely used for understanding international management strategies. Our paper highlights their value as research tool in this field of social science using a large set of micro-data (20,000) to investigate the presence of networks of subsidiaries overseas. The research question is the following: to what extent did/do global Japanese business networks mirror organizational models existing in Japan? In particular, we would like to assess how much the links building such business networks are shaped by the structure of big-size industrial conglomerates of firms headquartered in Japan, also described as HK. The major part of the academic community in the fields of management and industrial organization considers that formal links can be identified among firms belonging to HK. Miwa and Ramseyer (Miwa and Ramseyer 2002; Ramseyer 2006) challenge this claim and argue that the evidence supporting the existence of HK is weak. So far, quantitative empirical investigation has been conducted exclusively using data for firms incorporated in Japan. Our study tests the Miwa-Ramseyer hypothesis (MRH) at the global level using information on the network of Japanese subsidiaries overseas. The results obtained lead us to reject the MRH for the global dataset, as well as for subsets restricted to the two main regions/countries of destination of Japanese foreign investment. The results are robust to the weighting of the links, with different specifications, and are observed in most industrial sectors. The global Japanese network became increasingly complex during the late 20th century as a consequence of increase in the number of Japanese subsidiaries overseas but the key features of the structure remained rather stable. We draw implications of these findings for academic research in international business and for professionals involved in corporate strategy.
ST ] 6 O ct 2 01 6 When do nonparametric regressions based on discretely sampled curves work ?

In the context of nonparametric regression, we study conditions under which the consistency (and rates of convergence) of estimators built from discretely sampled curves can be derived from the consistency of estimators based on the unobserved whole trajectories. As a consequence, we derive asymptotic results for most of the regularization techniques used in functional data analysis, including smoothing and basis representation.
DEPA: Self-Supervised Audio Embedding for Depression Detection

Depression detection research has increased over the last few decades, one major bottleneck of which is the limited data availability and representation learning. Recently, self-supervised learning has seen success in pretraining text embeddings and has been applied broadly on related tasks with sparse data, while pretrained audio embeddings based on self-supervised learning are rarely investigated. This paper proposes DEPA, a self-supervised, pretrained dep ression a udio embedding method for depression detection. An encoder-decoder network is used to extract DEPA on in-domain depressed datasets (DAIC and MDD) and out-domain (Switchboard, Alzheimer's) datasets. With DEPA as the audio embedding extracted at response-level, a significant performance gain is achieved on downstream tasks, evaluated on both sparse datasets like DAIC and large major depression disorder dataset (MDD). This paper not only exhibits itself as a novel embedding extracting method capturing response-level representation for depression detection but more significantly, is an exploration of self-supervised learning in a specific task within audio processing.
Theory MIMO Scheme Performance and Robust Detection in Mixture of Background and Impulse Noise

Page 1 of 28 to be published in IEEE Trains. Inform. Theory MIMO Scheme Performance and Robust Detection in Mixture of Background and Impulse Noise Sander Stepanov Technion – Israel Institute of Technology sanders@ee.technion. ac.il ABSTRACT A new approach to analyzing and decoding MIMO signaling has been developed for the regular model of non-Gaussion noise, consisting of background and impulse noise named ε noise. It has been shown that non-Gaussion noise performance is significantly worse than Gaussion noise performance. Simulation results support the suggested theory. Robust in a statistical sense detection rule is suggested for such kind of noise, featuring a much better robust detector performance than the detector designed for Gaussian noise in an impulsive environment, but marginally less effective in background noise. The performance of the proposed approach is comparable with the developed potential bound. The proposed tool is a crucial issue for the MIMO communication system design, since a real noise environment has an impulsive character that contradicts the widely used Gaussian model. Therefore, the real MIMO performance for Gaussian and nonGaussian noise models is entirely different.
LISA and the Existence of a Fast-merging Double Neutron Star Formation Channel

Using a Milky Way (MW) double neutron star (DNS) merger rate of 210 Myr−1, as derived by the Laser Interferometer Gravitational-Wave Observatory (LIGO), we demonstrate that the Laser Interferometer Space Antenna (LISA) will detect on average 240 (330) DNSs within the MW for a 4 yr (8 yr) mission with a signal-to-noise ratio greater than 7. Even adopting a more pessimistic rate of 42 Myr−1, as derived by the population of Galactic DNSs, we find a significant detection of 46 (65) MW DNSs. These DNSs can be leveraged to constrain formation scenarios. In particular, without prior information on a particular system’s position and orbital period, traditional NS-discovery methods using radio telescopes alone are insensitive to DNSs with Porb ≲ 1 hr (merger times ≲10 Myr). If a fast-merging channel exists that forms DNSs at these short orbital periods, LISA affords, perhaps, the best opportunity to observationally identify and characterize these systems; we show that toy models for possible formation scenarios leave imprints on DNS orbital eccentricities, which may be measured by LISA for values as small as ∼10−2.
FLOWS SCHEDULING IN CLOUDDATA CENTRES

Hybrid intra-data centre networks, with optical and electrical capabilities, are attracting research interest in recent years. This is attributed to the emergence of new bandwidth greedy applications and novel computing paradigms. A key decision to make in networks of this type is the selection and placement of suitable flows for switching in circuit network. Here, we propose an efficient strategy for flow selection and placement suitable for hybrid Intra-cloud data centre networks. We further present techniques for investigating bottlenecks in a packet networks and for the selection of flows to switch in circuit network. The bottleneck technique is verified on a Software Defined Network (SDN) testbed. We also implemented the techniques presented here in a scalable simulation experiment to investigate the impact of flow selection on network performance. Results obtained from scalable simulation experiment indicate a considerable improvement on average throughput, lower configuration delay, and stability of offloaded flows.
$(1-2u^3)$-constacyclic codes and quadratic residue codes over $\mathbb{F}_{p}[u]/\langle u^4-u\rangle$

Let $\mathcal{R}=\mathbb{F}_{p}+u\mathbb{F}_{p}+u^2\mathbb{F}_{p}+u^3\mathbb{F}_{p}$ with $u^4=u$ be a finite non-chain ring, where $p$ is a prime congruent to $1$ modulo $3$. In this paper we study $(1-2u^3)$-constacyclic codes over the ring $\mathcal{R}$, their equivalence to cyclic codes and find their Gray images. To illustrate this, examples of $(1-2u^3)$-constacyclic codes of lengths $2^m$ for $p=7$ and of lengths $3^m$ for $p=19$ are given. We also discuss quadratic residue codes over the ring $\mathcal{R}$ and their extensions. A Gray map from $\mathcal{R}$ to $\mathbb{F}_{p}^4$ is defined which preserves self duality and gives self-dual and formally self-dual codes over $\mathbb{F}_{p}$ from extended quadratic residue codes.
PowerNet: Neural Power Demand Forecasting in Smart Grid

Power demand forecasting is a critical task for achieving efficiency and reliability in power grid operation. Accurate forecasting allows grid operators to better maintain the balance of supply and demand as well as to optimize operational cost for generation and transmission. This article proposes a novel neural network architecture PowerNet, which can incorporate multiple heterogeneous features, such as historical energy consumption data, weather data, and calendar information, for the power demand forecasting task. Compared to two recent works based on Gradient Boosting Tree (GBT) and Support Vector Regression (SVR), PowerNet demonstrates a decrease of 33.3% and 14.3% in forecasting error, respectively. We further provide empirical results the two operational considerations that are crucial when using PowerNet in practice, i.e., how far in the future the model can forecast with a decent accuracy and how often we should re-train the forecasting model to retain its modeling capability. Finally, we briefly discuss a multilayer anomaly detection approach based on PowerNet.
Throughput Maximization for Full Duplex Wireless Powered Communication Networks

In this paper, we consider a full duplex wireless powered communication network where multiple users with RF energy harvesting capabilities communicate to a hybrid energy and information access point. An optimization framework is proposed with the objective of maximizing the sum throughput of the users subject to energy causality and maximum transmit power constraints considering a realistic energy harvesting model incorporating initial battery levels of the users. The joint optimization of power control, time allocation and scheduling is mathematically formulated as a mixed integer non linear programming problem which is hard to solve for a global optimum. The optimal power and time allocation and scheduling decisions are investigated separately based on the optimality analysis on the optimization variables. Optimal power and time allocation problem is proven to be convex for a given transmission order. Based on the derived optimality conditions, we propose a fast polynomial-time complexity heuristic algorithm. We illustrate that the proposed algorithm performs very close-to-optimal while significantly outperforming an equal time allocation based scheduling scheme.
Literature Review: Human Segmentation with Static Camera

Our research topic is Human segmentation with static camera. This topic can be divided into three sub-tasks, which are object detection, instance identification and segmentation. These sub-tasks are three closely related subjects. The development of each subject has great impact on the other two fields. In this literature review, we will first introduce the background of human segmentation and then talk about issues related to the above three fields as well as how they interact with each other.
THANGTHAI, BEAR, HARVEY: PHONEMES AND VISEMES WITH DNNS

There is debate if phoneme or viseme units are the most effective for a lipreading system. Some studies use phoneme units even though phonemes describe unique short sounds; other studies tried to improve lipreading accuracy by focusing on visemes with varying results. We compare the performance of a lipreading system by modeling visual speech using either 13 viseme or 38 phoneme units. We report the accuracy of our system at both word and unit levels. The evaluation task is large vocabulary continuous speech using the TCD-TIMIT corpus. We complete our visual speech modeling via hybrid DNN-HMMs and our visual speech decoder is a Weighted Finite-State Transducer (WFST). We use DCT and Eigenlips as a representation of mouth ROI image. The phoneme lipreading system word accuracy outperforms the viseme based system word accuracy. However, the phoneme system achieved lower accuracy at the unit level which shows the importance of the dictionary for decoding classification outputs into words.
Big Bidirectional Insertion Representations for Documents

The Insertion Transformer is well suited for long form text generation due to its parallel generation capabilities, requiring O(\log_2 n) generation steps to generate n tokens. However, modeling long sequences is difficult, as there is more ambiguity captured in the attention mechanism. This work proposes the Big Bidirectional Insertion Representations for Documents (Big BIRD), an insertion-based model for document-level translation tasks. We scale up the insertion-based models to long form documents. Our key contribution is introducing sentence alignment via sentence-positional embeddings between the source and target document. We show an improvement of +4.3 BLEU on the WMT’19 English->German document-level translation task compared with the Insertion Transformer baseline.
A data set of aerial imagery from robotics simulator for map-based localization systems benchmark

Purpose The purpose of this paper is to present a new data set of aerial imagery from robotics simulator (AIR). AIR data set aims to provide a starting point for localization system development and to become a typical benchmark for accuracy comparison of map-based localization algorithms, visual odometry and SLAM for high-altitude flights. Design/methodology/approach The presented data set contains over 100,000 aerial images captured from Gazebo robotics simulator using orthophoto maps as a ground plane. Flights with three different trajectories are performed on maps from urban and forest environment at different altitudes, totaling over 33 kilometers of flight distance. Findings The review of previous research studies show that the presented data set is the largest currently available public data set with downward facing camera imagery. Originality/value This paper presents the problem of missing publicly available data sets for high-altitude (100‒3,000 meters) UAV flights; the current state-of-the-art research studies performed to develop map-based localization system for UAVs depend on real-life test flights and custom-simulated data sets for accuracy evaluation of the algorithms. The presented new data set solves this problem and aims to help the researchers to improve and benchmark new algorithms for high-altitude flights.
Fathers' Involvement in Sure Start: What Do Fathers and Mothers Perceive as the Benefits?

This paper is based on a case study of a Sure Start programme in the North East of England which was effective in engaging fathers. It details the positive views of 17 fathers and eight mothers on the benefits of father involvement. Perceived benefits included fathers' increased engagement with their young children and concern for their psychosocial development, improved relationships with children, increased social interaction with other fathers and consequently access to greater peer support, learning from the experiences of other fathers, increased knowledge and skills through education, changing understanding of the fathering role and access to a better quality of life for the whole family. Findings are discussed in relation to implications for working with fathers from poor socio-economic backgrounds.
Sentence Embeddings for Russian NLU

We investigate the performance of sentence embeddings models on several tasks for the Russian language. In our comparison, we include such tasks as multiple choice question answering, next sentence prediction, and paraphrase identification. We employ FastText embeddings as a baseline and compare it to ELMo and BERT embeddings. We conduct two series of experiments, using both unsupervised (i.e., based on similarity measure only) and supervised approaches for the tasks. Finally, we present datasets for multiple choice question answering and next sentence prediction in Russian.
Unsupervised Thresholding for Automatic Extraction of Dolphin Dorsal Fin Outlines from Digital

At least two software packages—DARWIN, Eckerd College [3], and FinScan, Texas A&M [4]—exist to facilitate the identification of cetaceans—whales, dolphins, porpoises— based upon the naturally occurring features along the edges of their dorsal fins. Such identification is useful for biological studies of population, social interaction, migration, etc. The process whereby fin outlines are extracted in current fin-recognition software packages is manually intensive and represents a major user input bottleneck: it is both time consuming and visually fatiguing. This research aims to develop automated methods (employing unsupervised thresholding and morphological processing techniques) to extract cetacean dorsal fin outlines from digital photographs thereby reducing manual user input. Ideally, automatic outline generation will improve the overall user experience and improve the ability of the software to correctly identify cetaceans. Various transformations from color to gray space were examined to determine which produced a grayscale image in which a suitable threshold could be easily identified. To assist with unsupervised thresholding, a new metric was developed to evaluate the jaggedness of figures (“pixelarity”) in an image after thresholding. The metric indicates how cleanly a threshold segments background and foreground elements and hence provides a good measure of the quality of a given threshold. This research results in successful extractions in roughly 93% of images, and significantly reduces user-input time.
Obscuration properties of mid-IR-selected AGN

The goal of this work is to study the obscuration properties of mid-infrared (mid-IR)-selected active galactic nuclei (AGN). For that purpose, we use Wide-field Infrared Survey Explorer (WISE) sources in the Stripe 82–XMM area to identify mid-IR AGN candidates, applying the Assef et al. criteria. Stripe 82 has optical photometry ≈2 times deeper than any single-epoch Sloan Digital Sky Survey (SDSS) region. XMM–Newton observations cover ∼26 deg2. Applying the aforementioned criteria, 1946 IR AGN are selected. ${\sim} 78{{\, \rm per\, cent}}$ have SDSS detection, while 1/3 of them are detected in X-rays, at a flux limit of $\rm {\sim} 5 \times 10^{-15}\, erg\, s^{-1}\, cm^{-2}$. Our final sample consists of 507 IR AGN with X-ray detection and optical spectra. Applying a $r-W2 > 6$ colour criterion, we find that the fraction of optically red AGN drop from 43 per cent for those sources with SDSS detection to $23{{\, \rm per\, cent}}$ for sources that also have X-ray detection. X-ray spectral fitting reveals 40 (${\sim} 8{{\, \rm per\, cent}}$) X-ray absorbed AGN ($N_\mathrm{ H} > 10^{22}\,{\rm cm}^{-2}$). Among the X-ray unabsorbed AGN, there are 70 red systems. To further investigate the absorption of these sources, we construct spectral energy distributions (SEDs) for the total IR AGN sample. SED fitting reveals that ${\sim} 20{{\, \rm per\, cent}}$ of the optically red sources have such colours because the galaxy emission is a primary component in the optical part of the SED, even though the AGN emission is not absorbed at these wavelengths. SED fitting also confirms that $12{{\, \rm per\, cent}}$ of the X-ray unabsorbed IR AGN are optically obscured.
Angular distribution studies on the two-photon ionization of hydrogen-like ions: Relativistic description

The angular distribution of the emitted electrons, following the two-photon ionization of the hydrogen-like ions, is studied within the framework of second order perturbation theory and the Dirac equation. Using a density matrix approach, we have investigated the effects which arise from the polarization of the incoming light as well as from the higher multipoles in the expansion of the electron--photon interaction. For medium- and high-Z ions, in particular, the non-dipole contributions give rise to a significant change in the angular distribution of the emitted electrons, if compared with the electric-dipole approximation. This includes a strong forward emission while, in dipole approxmation, the electron emission always occurs symmetric with respect to the plane which is perpendicular to the photon beam. Detailed computations for the dependence of the photoelectron angular distributions on the polarization of the incident light are carried out for the ionization of H, Xe$^{53+}$, and U$^{91+}$ (hydrogen-like) ions.
Transformer-based Cascaded Multimodal Speech Translation

This paper describes the cascaded multimodal speech translation systems developed by Imperial College London for the IWSLT 2019 evaluation campaign. The architecture consists of an automatic speech recognition (ASR) system followed by a Transformer-based multimodal machine translation (MMT) system. While the ASR component is identical across the experiments, the MMT model varies in terms of the way of integrating the visual context (simple conditioning vs. attention), the type of visual features exploited (pooled, convolutional, action categories) and the underlying architecture. For the latter, we explore both the canonical transformer and its deliberation version with additive and cascade variants which differ in how they integrate the textual attention. Upon conducting extensive experiments, we found that (i) the explored visual integration schemes often harm the translation performance for the transformer and additive deliberation, but considerably improve the cascade deliberation; (ii) the transformer and cascade deliberation integrate the visual modality better than the additive deliberation, as shown by the incongruence analysis.
Weekly Idiosyncratic Risk Metrics and Idiosyncratic Momentum: Evidence from the Chinese Stock Market

This paper focuses on the weekly idiosyncratic momentum (IMOM) as well as its risk-adjusted versions with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum and idiosyncratic momentum based on raw returns and idiosyncratic returns, respectively. After that the univariate portfolio analysis is conducted to investigate the return predictability with respect to various idiosyncratic risk metrics. Further, we perform a comparative study on the performance of the IMOMportfolios with respect to various risk metrics. At last, we explore the possible explanations to the IMOM as well as risk-based IMOM portfolios. We find that 1) there is a prevailing contrarian effect and a IMOM effect for the whole sample; 2) a negative relation exists between most of the idiosyncratic risk metrics and the cross-sectional returns, and better performance is found that is linked to idiosyncratic volatility (IVol) and maximum drawdowns (IMDs); 3) additionally, the IVol-based and IMD-based IMOM portfolios exhibit a better explanatory power to the IMOM portfolios with respect to other risk metrics; 4) finally, higher profitability of the IMOM as well as IVol-based and IMD-based IMOM portfolios is found to be related to upside market states, high levels of liquidity and high levels of investor sentiment.