Miracle-dz/UniFAR-FTUs huggingface dataset:
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to the limited computation resources. What’s worse, traditional cloud-assisted DNN inference is heavily hindered by the significant wide-area network latency, leading to poor real-time performance as well as low quality of user experience. To address these challenges, in this paper, we propose Edgent, a framework that leverages edge computing for DNN collaborative inference through device-edge synergy. Edgent exploits two design knobs: (1) DNN partitioning that adaptively partitions computation between device and edge for purpose of coordinating the powerful cloud resource and the proximal edge resource for real-time DNN inference; (2) DNN right-sizing that further reduces computing latency via early exiting inference at an appropriate intermediate DNN layer. In addition, considering the potential network fluctuation in real-world deployment, Edgent is properly design to specialize for both static and dynamic network environment. Specifically, in a static environment where the bandwidth changes slowly, Edgent derives the best configurations with the assist of regression-based prediction models, while in a dynamic environment where the bandwidth varies dramatically, Edgent generates the best execution plan through the online change point detection algorithm that maps the current bandwidth state to the optimal configuration. We implement Edgent prototype based on the Raspberry Pi and the desktop PC and the extensive experimental evaluations demonstrate Edgent’s effectiveness in enabling on-demand low-latency edge intelligence.
The impact of light polarization effects on weak lensing systematics

A fraction of the light observed from edge-on disc galaxies is polarized due to two physical effects: selective extinction by dust grains aligned with the magnetic field and scattering of the anisotropic starlight field. Since the reflection and transmission coefficients of the reflecting and refracting surfaces in an optical system depend on the polarization of incoming rays, this optical polarization produces both (a) a selection bias in favour of galaxies with specific orientations and (b) a polarization-dependent point spread function (PSF). In this work, we build toy models to obtain for the first time an estimate for the impact of polarization on PSF shapes and the impact of the selection bias due to the polarization effect on the measurement of the ellipticity used in shear measurements. In particular, we are interested in determining if this effect will be significant for Wide-Field Infrared Survey Telescope (WFIRST). We show that the systematic uncertainties in the ellipticity components are 8 × 10−5 and 1.1 × 10−4 due to the selection bias and PSF errors respectively. Compared to the overall requirements on knowledge of the WFIRST PSF ellipticity (4.7 × 10−4 per component), both of these systematic uncertainties are sufficiently close to the WFIRST tolerance level that more detailed studies of the polarization effects or more stringent requirements on polarization-sensitive instrumentation for WFIRST are required.
PipeMare: Asynchronous Pipeline Parallel DNN Training

Recently there has been a flurry of interest around using pipeline parallelism while training neural networks. Pipeline parallelism enables larger models to be partitioned spatially across chips and within a chip, leading to both lower network communication and overall higher hardware utilization. Unfortunately, to preserve statistical efficiency, existing pipeline-parallelism techniques sacrifice hardware efficiency by introducing bubbles into the pipeline and/or incurring extra memory costs. In this paper, we investigate to what extent these sacrifices are necessary. Theoretically, we derive a simple but robust training method, called PipeMare, that tolerates asynchronous updates during pipeline-parallel execution. Using this, we show empirically, on a ResNet network and a Transformer network, that PipeMare can achieve final model qualities that match those of synchronous training techniques (at most 0.9% worse test accuracy and 0.3 better test BLEU score) while either using up to 2.0X less weight and optimizer memory or being up to 3.3X faster than other pipeline parallel training techniques. To the best of our knowledge we are the first to explore these techniques and fine-grained pipeline parallelism (e.g. the number of pipeline stages equals to the number of layers) during neural network training.
Corporate Distance Education: An Applied Understanding of its Resistance Factors

The objective of this study was to identify and analyze the main factors that explain the resistance to distance education (DE) in corporate education (CE) in a military institution. The present study was structured with quantitative and explanatory approach, based on the theoretical framework of the READEC model, which was developed and validated by Albertin and Brauer (2012). From a technical point of view, a bibliographic survey and a field survey were carried out by means of an electronic questionnaire. Data collection was carried out with a sample of 345 Brazilian Army personnel who were part of the Training Course for Sergeants (TCS). The data of this research were treated with the Structural Equation Modeling (SEM), technique based on Partial Least Squares (PLS). The results of the research showed that the self-efficacy and performance expectation dimensions directly and positively influence the resistance to the DE in the CE, and the effort expectation, facilitating conditions and interactivity dimensions are constructs antecedent to the performance expectation. Contrary to the previous theory, the results also indicated that the perception about the organizational infrastructure was not significant to explain the resistance to DE, which allows us to bring new insights about this phenomenon.
SDSS-IV MaNGA: New benchmark for the connection between stellar angular momentum and environment: a study of about 900 groups/clusters

It has been observed that low redshift early-type galaxies can be separated into slow and fast rotators according to a proxy of specific stellar angular momentum, $\lambda_{R_e}$. Detailed studies of a handful of nearby clusters have shown that slow rotators are generally found at the centres of clusters where the number density is highest, whereas the fast rotators trace the trend followed by early-type galaxies of increasing in number with local density. In this paper, we study the environmental distribution of slow and fast rotators using the stellar kinematics of about 3900 galaxies from the Sloan Digital Sky Survey's Mapping Nearby Galaxies at Apache Point Observatory survey. For galaxies in groups closer than $z=0.08$ that are not observed with MaNGA but satisfy the necessary conditions for slow rotators, we visually assign slow/fast rotator classifications to obtain a complete sample. Our final catalogue contains about 900 groups of five or more members. We observe the kinematic morphology-density (kT-$\Sigma$) relation for each group and find an increasing fraction of massive slow rotators with increasing number density. We provide evidence suggesting that the observed lack of trends in angular momentum with environment at fixed stellar mass is partly because the maximum density varies between clusters, and that the locations of massive slow rotators are strongly correlated with peak densities in galaxy groups and clusters. We conclude that the (projected) number density relative to the cluster peak is more fundamental than the absolute number density in influencing the abundance of slow rotators. We find that the kT-$\Sigma$ relation does exist at fixed stellar mass, and we rule out the hypothesis that the kT-$\Sigma$ relation is a result of dynamical friction alone, instead arguing that massive slow rotators grow hierarchically in tandem with their host clusters.
Neural networks on microcontrollers: saving memory at inference via operator reordering

Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence. Microcontrollers (MCUs) are an attractive platform for building smart devices due to their low cost, wide availability, and modest power usage. However, they lack the computational resources to run neural networks as straightforwardly as mobile or server platforms, which necessitates changes to the network architecture and the inference software. In this work, we discuss the deployment and memory concerns of neural networks on MCUs and present a way of saving memory by changing the execution order of the network's operators, which is orthogonal to other compression methods. We publish a tool for reordering operators of TensorFlow Lite models and demonstrate its utility by sufficiently reducing the memory footprint of a CNN to deploy it on an MCU with 512KB SRAM.
Light vector mediator search in the low-energy data of the CONNIE reactor neutrino experiment

The CONNIE experiment is located at a distance of 30 m from the core of a commercial nuclear reactor, and has collected a 3.7 kg-day exposure using a CCD detector array sensitive to an $\sim$1 keV threshold for the study of coherent neutrino-nucleus elastic scattering. Here, we use the results previously published by the CONNIE collaboration to constrain the allowed region in the parameter space of a Standard Model extension with a light vector mediator. We obtain new world-leading constraints for a light vector mediator with mass $M_{Z^{\prime}}<$ 10 MeV. These results constitute the first use of the CONNIE data as a probe for physics beyond the Standard Model.
2-OGC: Open Gravitational-wave Catalog of Binary Mergers from Analysis of Public Advanced LIGO and Virgo Data

We present the second Open Gravitational-wave Catalog (2-OGC) of compact-binary coalescences, obtained from the complete set of public data from Advanced LIGO’s first and second observing runs. For the first time we also search public data from the Virgo observatory. The sensitivity of our search benefits from updated methods of ranking candidate events including the effects of nonstationary detector noise and varying network sensitivity; in a separate targeted binary black hole merger search we also impose a prior distribution of binary component masses. We identify a population of 14 binary black hole merger events with probability of astrophysical origin >0.5 as well as the binary neutron star merger GW170817. We confirm the previously reported events GW170121, GW170304, and GW170727 and also report GW151205, a new marginal binary black hole merger with a primary mass of that may have formed through hierarchical merger. We find no additional significant binary neutron star merger or neutron star–black hole merger events. To enable deeper follow-up as our understanding of the underlying populations evolves, we make available our comprehensive catalog of events, including the subthreshold population of candidates and posterior samples from parameter inference of the 30 most significant binary black hole candidates.
Determinants of internal and external R&D offshoring: evidence from Spanish firms

Abstract This paper analyses the determinants of R&D offshoring of Spanish firms using information from the Panel of Technological Innovation. We find that being an exporter, international technological cooperation, continuous R&D engagement, applying for patents, being a foreign subsidiary, and firm size are factors that positively affect the decision to offshore R&D. In addition, we find that a lack of financing is an obstacle relatively more important for independent firms than for firms that belong to business groups. For these latter, we also obtain that the factors that influence the decision to offshore R&D differ depending on whether the firm purchases the R&D services within the group or through the market: a higher degree of importance assigned to internal sources of information for innovation as compared to market sources increases (decreases) the probability of R&D offshoring only through the group (market).
Communicative Associative Logic of Cognitive Professional Robot with Imitative Thinking

There are two main approaches to hardware-software realization of imitative thinking of cognitive robots. On the basis of machine learning. Such cognitive robots are used in services industry, for the commercial and entertaining purposes. In article approach to creation of cognitive robots on the basis of modeling of communicative and associative logic of imitative thinking of the person is considered. Cognitive robots on the basis of modeling of communicative and associative logic of imitative thinking of the person are used as lecturers and consultants. also in professional activity when concepts and competences are strictly defined. The communicative and associative logic of thinking allows to create the symbolical conceiving robot capable to study, realize information requirements (tasks), to train subject areas, to communicate with the help of the speech, to read and write in various languages. The robot on the basis of symbolical language communicative logic solves a problem of the automated imitation of imitative thinking with associative and communicative symbolical language elements of knowledge. The main practical objectives of imitation of imitative thinking are drawing up the intrinsic focused dictionaries of the developed subject domains of knowledge and standard information requirements; drawing up standard procedures of realization of information requirements; formation of networks from communicative and associative symbolical language elements of knowledge of subject domains; expansion of a natural language to functional; creation of systems of speech and text communication in a natural language and recognitions of the speech of interlocutors.
Central Bank Credibility: An Historical and Quantitative Exploration

In this paper we provide empirical measures of central bank credibility and augment these with historical narratives from eleven countries. To the extent we are able to apply reliable institutional information we can also indirectly assess their role in influencing the credibility of the monetary authority. We focus on measures of inflation expectations, the mean reversion properties of inflation, and indicators of exchange rate risk. In addition we place some emphasis on whether credibility is particularly vulnerable during financial crises, whether its evolution is a function of the type of crisis or its kind (i.e., currency, banking, sovereign debt crises). We find credibility changes over time are frequent and can be significant. Nevertheless, no robust empirical connection between the size of an economic shock (e.g., the Great Depression) and loss of credibility is found. Second, the frequency with which the world economy experiences economic and financial crises, institutional factors (i.e., the quality of governance) plays an important role in preventing a loss of credibility. Third, credibility shocks are dependent on the type of monetary policy regime in place. Finally, credibility is most affected by whether the shock can be associated with policy errors.
Gait recognition based on optimized neural network

By optimizing the parameters of neural network and applying it to gait recognition, we propose a gait recognition method based on optimized neural network. And we use gait Gaussian image to replace the most popular gait energy image in gait recognition. In this method, an eight-layer convolution neural network is built and initialized with the parameters of the well trained model Alexnet, which can speed up the model convergence and prevent over-fitting effectively. Compared with the traditional methods,the model training time is shortened and the model's expression ability is enhanced at the same time.Further, the gait Gaussian images of human motion are used to train the optimized neural network and update the parameters of the model, training with gait Gaussian image makes the expression of the model be better than the traditional training with gait energy image. To our knowledge, it is the first time to apply gait Gaussian image based neural network to gait recognition in existing researches, this is a breakthrough in the performance of the algorithm. Thus, we get an optimized neural network that can achieve gait recognition successfully. A satisfactory recognition result of the model was found by lots of experiments, especially when the targets carrying status or wearing coat. The experimental results show that the optimized neural network based gait recognition can speed up the model training. In addition，the optimization strategy well avoids over-fitting of the model, and the use of gait Gaussian image also makes the model better than the previous.
Malware Detection Using Semantic Features and Improved Chi-square

As advances in information technology (IT) affect all areas in the world, cyber-attacks also continue to increase. Malware has been used for cyber attacks, and the number of new malware and variants tends to explode in these years, depending on its trendy types. In this study, we introduce semantic feature generation and new feature selection methods for improving the accuracy of malware detection based on API sequences to detect these new malware and variants. Therefore, one of the existing feature selection methods is chosen because it shows the best performance, and then it is improved to be suitable for malware detection. In addition, the improved feature selection method is verified by using the Reuter dataset. Finally, the actual API sequences are extracted from the given malware and benign, and the proposed feature generation and selection methods are used to generate a feature vector. The performance is verified through classification.
TÜRKİYE’NİN EGE BÖLGESİNDEKİ KONTEYNER TERMİNALLERİNİN TEDARİK ZİNCİRLERİNE ENTEGRASYONU

Denizcilik isletmelerinin yeni rekabetci cevresi limanlarin tedarik zinciri entegrasyonlarini degerlendirme ihtiyaclarini dogurmustur. Bu calismanin amaci Turkiye’nin Ege Bolgesinde bulunan uc konteyner terminalinin tedarik zincirlerine entegrasyonlarini degerlendirmektir. Bunun icin dort yapidan olusan bir model gelistirdik: “kullanicilar ile iliskiler”; “bilgi ve iletisim sistemleri”; “katma degerli hizmetler” ve “coklu ulasim baglantilari ve sistemleri”. Buna ek olarak konteyner terminallerini kullanan denizcilik isletmelerine anket uyguladik ve bu uygulamanin gecerliligini Dogrulayici Faktor Analizi ile test ettik. Calismanin sonuclari gostermektedir ki iki ozel konteyner terminali tedarik zincirlerine devlet tarafindan isletilen konteyner terminalinden daha fazla entegredir. Ote yandan, devlet tarafindan isletilen konteyner terminalinin “coklu ulasim baglantilari ve sistemleri“ iki ozel konteyner terminalinden, yeterli demiryolu ve kara yolu baglantilarina sahip olmadiklari icin, daha fazla tedarik zincirine entegredir.
Model compression of SDM‐based face alignment for mobile applications

Face alignment could be widely used in face recognition, expression recognition, face-based AR applications etc. Cascaded-regression-based face alignment algorithms have been popular in recent years for their low computational costs and impressive results in uncontrolled scenarios. Unfortunately, the size of the trained model is quite large for cascaded-regression-based methods which makes it unsuitable for commercial applications on mobile phones. In this study, the authors proposed a data compression method for the trained model of the supervised descent method (SDM). Firstly, the distribution of the model data was estimated using a non-parametric method. Then an adaptive quantisation algorithm was proposed to quantise the model data. Finally, their adaptive quantisation algorithm was tightly coupled with the SDM training process to fine tune the results. The quantitative experimental results proved that their proposed method could compress the data model to <20% of its original size without hurting the performances. The proposed method has been integrated into a mobile AR application, subjective evaluations proved that the proposed compression method could provide similar visual effects compared with the uncompressed counterpart.
Annual Revision of the National Income and Product Accounts Annual Estimates, 1998-2000 Quarterly Estimates, 1998:I-2001:I

I N this issue of the SURVEY OF CURRENT BUSINESS, the Bureau of Economic Analysis (BEA) presents revised estimates of the national income and product accounts (NIPA's) for 1999-2001 and the first quarter of 2002. 1 As is usual in annual NIPA revisions, these estimates incorporate newly available source data that are more complete, more detailed, and otherwise more appropriate than those that were previously incorporated. Both the revised and the previously published estimates show similar quarterly patterns of growth in real gross domestic product (GDP) in 1999 and 2000. However, the revised estimates show that GDP de-clined in each of the first three quarters of 2001, whereas the previously published estimates showed positive, albeit decelerating, growth in the first two quarters of 2001 and a decline in the third quarter. Both sets of estimates show GDP growth resuming in the fourth quarter of 2001.
An Introduction to Using Corpora with EFL Learners

The use of corpora in the classroom represents an innovative way to enable English language learners to undertake independent study of lexical and grammatical patterns; however, only a limited amount of investigation into the use of corpora with students exists. This paper will first briefly introduce pertinent literature, which will give a basic overview of corpus linguistics. The paper will report on the use of the British National Corpus (BNC) and other corpus tools with students in a semester long course in a Japanese university with advanced EFL learners. These students undertook a series of tasks and projects, which allowed them to achieve the overall course goal of being able to conduct independent research into lexical and grammatical patterns. In order to assess student progress and to gather student opinions about the course and the use of corpora, data was collected in two ways: pre and post CEFR style student self-assessments, and a course reflection and evaluation survey. The results indicated that the students had progressed throughout the semester and that they had a largely positive opinion of the course. In concluding the paper, suggestions for teachers wishing to use corpora and corpus tools with their students will be offered.
Hybrid Deep Neural Network based Performance Estimation Method for Efficient Offloading on IoT-Cloud Environments

The IoT-Cloud virtual machine system is a cloud-based execution solution for IoT devices with offloading techniques that delegate tasks requiring high computing power from low-performance IoT devices to a high-performance cloud environment as a service. The IoT devices with the IoT-Cloud virtual machine system can perform complex tasks using the computing power of high-performance cloud. The offloading technique can reduce the execution performance depending on the workload of the IoT devices and the clouds. Therefore, it is necessary to decide offloading execution considering the workload of the IoT devices and the clouds. In this paper, CPU utilization trend, which is one of the workload indices, is predicted through deep learning in order to decide offloading execution considering the workload of the IoT devices and clouds. In this paper, we present four CPU usage models and introduce a technique for predicting server load based on hybrid deep neural network. The predicted CPU utilization trend is indicative of future CPU utilization information and is therefore an indicator for offloading execution decisions. Through experiments, we confirmed that the proposed method estimates the load of the model very similar, and it can apply the offloading adaptively according to the load of the server.